system

Abstract

We provide the system. [Solution] An authentication method that receives authentication information from the user and authenticates the user based on that authentication information, A display means that provides authenticated users with a selection of disaster prevention content and displays learning content according to the user's selection, A simulation method that presents a virtual disaster situation and allows the user to perform actions according to that situation to run the simulation, An analytical method that records user actions in a simulation and analyzes user behavior based on those records, A means of providing disaster response exercises in a virtual environment through information devices accessible to citizens, A system that includes measures for collaboratively creating and implementing disaster prevention plans in cooperation with local communities.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Disaster prevention education is important for people to acquire correct knowledge and actions regarding disasters. However, with conventional education methods, it is difficult to attract the interest of learners because the content is monotonous, and it is not sufficient for taking appropriate actions during actual disasters. Also, it is an issue that disaster prevention awareness improvement and planning at the unit of family or local community are not effectively carried out.

Means for Solving the Problems

[0005] This invention solves the above problems by providing an interactive disaster prevention learning system. Upon user login, personalized disaster prevention content is provided through a display means via an authentication means. Furthermore, it includes a simulation means that allows users to experience virtual disaster scenarios, and improves the user's knowledge and actions by recording and analyzing the user's actions. The analysis results are presented to the user as feedback, and the suggestion means proposes the most suitable learning content for the next time. In addition, using a collaboration means, users can create disaster prevention plans together with other members and raise disaster prevention awareness within the community.

[0006] An "authentication method" is a mechanism for verifying a user's identity based on authentication information received from the user.

[0007] A "display mechanism" is a system that presents users with selectable disaster prevention content and shows them appropriate learning information based on their selection.

[0008] A "simulation method" is a mechanism that allows users to experience a virtual disaster situation and take actions based on that experience.

[0009] An "analysis tool" is a system that records the user's actions during a simulation and analyzes the user's behavior based on that data.

[0010] The "suggestion method" is a system that, based on the analysis results, presents users with disaster prevention content they should study next time.

[0011] A "quiz implementation method" is a system that administers quizzes to evaluate the user's level of knowledge retention and retrieves the results.

[0012] "Collaboration tools" refer to a system that enables users to work together with other members to create disaster prevention plans and raise disaster preparedness awareness throughout the community. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention provides an interactive disaster prevention learning system designed to enable users to effectively acquire disaster prevention knowledge and actions. Specific embodiments are described below.

[0035] The user launches the application from their device and enters their personal authentication information on the login screen. The server receives this information and authenticates the user using the authentication method. If this process is successful, the server selects the most appropriate disaster prevention content based on the user's profile information and sends it to the device.

[0036] The terminal displays a list of disaster prevention categories (e.g., earthquakes, typhoons, floods, etc.) to the user based on information received from the server. The user selects a category of interest and starts interactive learning content related to that category.

[0037] During the learning process, the device displays selected content, and the user experiences a virtual disaster scenario based on that content. The simulation allows the user to choose actions for each situation and receive real-time feedback based on those choices.

[0038] Subsequently, the user takes a quiz based on what they have learned through a quiz administration system. The server evaluates the user's answers and uses the data obtained through analysis to check the level of knowledge retention. The evaluation results are sent to the terminal and presented to the user as feedback.

[0039] The proposed method allows the server to comprehensively analyze the user's learning history and evaluation results to suggest disaster prevention content deemed optimal for the next learning session. This suggestion is then notified to the user via their device.

[0040] In addition, using collaborative methods, users can invite family members and community members to the app to jointly create disaster preparedness plans. This feature is designed to promote the sharing of disaster preparedness awareness and the effective implementation of the plans.

[0041] For example, if a user selects the earthquake preparedness category, the simulation will show a scenario where an earthquake occurs in a home. The user selects which piece of furniture to hide under, and receives real-time feedback on whether their selection is correct. In the subsequent quiz, questions such as "Where is the safest place to be during an earthquake?" are asked, and the user's level of understanding is evaluated.

[0042] In this way, the present invention is a system that enables users to learn practical and in-depth disaster prevention knowledge.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user launches the application from their device and enters their personal authentication information on the login screen.

[0046] Step 2:

[0047] The terminal sends user authentication information to the server, and the server uses that information to verify credentials against a database and authenticate the user.

[0048] Step 3:

[0049] The server retrieves the profile information of the authenticated user and selects the most suitable disaster prevention content based on that information. The server then sends that content information to the terminal.

[0050] Step 4:

[0051] The terminal displays content information received from the server to the user. The user selects a disaster prevention category (earthquake, typhoon, flood, etc.) based on their interests.

[0052] Step 5:

[0053] Interactive learning content related to the selected category is displayed on the device. The user then begins learning based on that content.

[0054] Step 6:

[0055] As the learning process progresses, the device uses simulation tools to present virtual disaster scenarios. Users select actions according to the situation and advance the simulation.

[0056] Step 7:

[0057] The server records the user's choices and their results in real time, and uses analytical tools to evaluate the user's behavior. This evaluation result is immediately fed back to the user through the terminal.

[0058] Step 8:

[0059] After the simulation ends, the device will present the user with a quiz using a quiz administration method. The quiz will be based on what was learned during the simulation.

[0060] Step 9:

[0061] Once the quiz is completed, the server analyzes the user's answers and evaluates their knowledge retention. The evaluation results and feedback are sent to the device.

[0062] Step 10:

[0063] Using the proposed method, the server selects the next disaster prevention content to be learned based on the learning history and evaluation results. This information is then suggested to the user via the terminal.

[0064] Step 11:

[0065] The collaboration mechanism allows users to invite other members to the app and provides an option for collaboratively creating disaster preparedness plans. The generated plans are shared among members via their devices.

[0066] (Example 1)

[0067] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0068] Conventional disaster prevention learning systems have faced challenges such as difficulty in providing individually optimized disaster prevention content to users and a lack of effective means for providing feedback on learning results. Furthermore, they lacked sufficient functionality for collaborative disaster prevention planning, making it difficult for users to easily work together.

[0069] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0070] In this invention, the server includes an information authentication means that receives authentication information from a user and authenticates the user based on that authentication information; a content display means that provides the authenticated user with options to select disaster prevention content according to the user's attributes and displays content according to the user's selection; and a virtual situation presentation means that presents a virtual disaster situation and allows the user to perform operations according to that situation to execute a simulation. This enables the provision of disaster prevention content optimized for the user, highly effective feedback for learning, and cooperation in jointly formulating a disaster prevention plan.

[0071] An "information authentication method" is a function that verifies the legitimacy of a user based on the authentication information received from that user.

[0072] The "content display means" is a function that provides authenticated users with the most suitable disaster prevention learning content as a selection based on their attribute information, and displays the learning content based on that selection.

[0073] A "virtual situation presentation means" is a function that presents a virtual disaster scenario to the user and accepts user actions or executes simulations according to that situation.

[0074] "Behavioral analysis means" refers to a function that analyzes user behavior based on user actions recorded in simulations.

[0075] The "content suggestion method" is a function that suggests the most suitable disaster prevention content for the next learning session based on the user's learning history and operation results.

[0076] A "problem-solving tool" is a function that evaluates the degree of knowledge retention based on user actions and provides preliminary learning such as quizzes and tests based on that evaluation.

[0077] "Collaboration provision means" refers to a function that provides the necessary collaborative features for users to invite other participants and create disaster prevention plans together.

[0078] This invention provides an interactive learning system that enables users to effectively acquire disaster prevention knowledge. This system is primarily operated by a server, terminals, and the user.

[0079] The server first receives authentication information entered by the user and uses this information to verify the user's legitimacy using an information authentication method. If this authentication is successful, the server uses a generative AI model to consider the user's attribute information and past learning history to select the most suitable disaster prevention content. The server then sends this selected content to the terminal.

[0080] The terminal uses a content display mechanism to show the user a list of disaster prevention categories (e.g., earthquakes, floods, etc.) based on the information received from the server. When the user selects a category of interest, a simulation related to that category is started using a virtual situation presentation mechanism. This allows the user to experience operations in a virtual disaster situation and receive real-time feedback.

[0081] Once the simulation is complete, the user automatically participates in a quiz through a problem-solving tool. The server uses behavioral analysis tools to evaluate the user's knowledge retention based on their quiz answers. The evaluation results are then sent back to the terminal and presented to the user as feedback.

[0082] Furthermore, the server uses a content suggestion mechanism to consider the user's learning history and knowledge retention level, and suggests disaster prevention content for the next learning session. Users can also invite other participants to the application through a collaborative provision mechanism and jointly develop disaster prevention plans.

[0083] To give a specific example, if a user selects the earthquake preparedness category, the device will display a scenario in which an earthquake occurs in the home, and the user will experience choosing a safe place and taking evacuation action. At that time, it is expected that a prompt message such as "Please tell me the best way to act safely when an earthquake occurs" will be entered into the system.

[0084] In this way, the present invention functions as a system that provides users with practical and effective opportunities for disaster prevention learning.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The user launches the disaster prevention learning application from their device and enters their personal authentication information on the login screen. This information includes a user ID and password. The device then sends this authentication information to the server. The output of this server uses the authentication information as a search key in the database.

[0088] Step 2:

[0089] The server searches the database for user information based on the received authentication information and verifies the user's legitimacy using an information authentication method. The data processing performed in this process involves comparing the input information with the records in the database. If user authentication is successful based on the results, the authentication status is output to the terminal.

[0090] Step 3:

[0091] The server retrieves the authenticated user's profile information and past learning history, and uses a generative AI model to select the most suitable disaster prevention content. User attributes and history data are used as input, and selected content information is generated as output. This process involves the selection of personalized content based on user attributes.

[0092] Step 4:

[0093] The server sends the selected disaster prevention content to the terminal. Based on the received content, the terminal uses a content display device to show the user a list of disaster prevention categories. It receives content data as input and displays a category list to the user as output.

[0094] Step 5:

[0095] The user selects a disaster prevention category of interest. The terminal sends the selection to the server, and a simulation of that category is executed through a virtual situation presentation system. The user's selection is used as input, and the start of the simulation is triggered as output.

[0096] Step 6:

[0097] The user performs actions in response to a given virtual disaster scenario within the simulation. The terminal records these actions in real time. It receives user instructions as input and generates an operation log as output. Appropriate feedback is provided to the user in real time.

[0098] Step 7:

[0099] After the simulation ends, the terminal sends the recorded operation data to the server. The server uses behavioral analysis tools to analyze the user's actions and evaluate the degree of knowledge retention. The user's past operation logs are input into this process. The evaluation results become the analysis output and are sent back to the terminal.

[0100] Step 8:

[0101] The terminal presents the received evaluation results to the user and administers a quiz using problem-solving tools. The user answers the quiz, and the results are sent to the server. The quiz answer data is generated as input, and the evaluated score is generated as output.

[0102] Step 9:

[0103] Based on the user's learning progress and history, the server uses a content suggestion system to propose the most suitable disaster prevention content for the next learning session through an AI model. The suggested content is displayed to the user via their device. This supports continuous learning.

[0104] Step 10:

[0105] Users can invite other participants to the app using collaborative methods to jointly develop disaster preparedness plans. This function generates an invitation link based on the information entered by the user, and the output allows for direct collaborative editing of the disaster preparedness plan with others. This aims to improve collaborative disaster preparedness awareness.

[0106] (Application Example 1)

[0107] 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."

[0108] In modern urban environments, raising disaster preparedness awareness and establishing effective disaster prevention knowledge are crucial challenges. However, disaster prevention education based on traditional methods often limits the understanding and practical application of the learned content. Furthermore, a lack of coordination throughout the community can prevent the full utilization of response capabilities during crises. Therefore, there is a need for a learning system that allows for the practical acquisition of disaster prevention knowledge, as well as mechanisms that promote coordination and collaborative planning throughout the community.

[0109] 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.

[0110] This invention includes an authentication means for receiving authentication information from a user and authenticating the user based on that information, a presentation means for providing disaster response exercises in a virtual environment through an information device accessible to citizens, and a response means for jointly creating and implementing disaster prevention plans in cooperation with the local community. This enables users to interactively experience disaster prevention simulations and to formulate and implement effective disaster prevention plans in cooperation with the entire community.

[0111] An "authentication method" is a system that receives authentication information from a user, verifies the user based on that information, and performs authentication.

[0112] A "display means" is a device that provides authenticated users with a selection of disaster prevention content and presents learning content according to the user's selection through the screen of an information terminal.

[0113] A "simulation means" is a mechanism that presents a virtual disaster situation and allows the user to perform actions in response to that situation to execute the simulation.

[0114] The "analysis method" is a system that analyzes user actions recorded during the simulation and evaluates user behavior based on those records.

[0115] "Presentation method" refers to a method of providing disaster response exercises in a virtual environment through information devices accessible to citizens.

[0116] "Response measures" refer to methods and processes for collaborating with local communities to jointly create and effectively implement disaster prevention plans.

[0117] This invention is a system designed to enable effective learning and practice of disaster prevention knowledge. The following process is used to realize this system.

[0118] The server first verifies the authentication information received from the user via a secure communication protocol and performs authentication. This is done using an authentication API and a secure protocol (e.g., HTTPS). Once authentication is complete, the server retrieves the user's profile information, selects relevant disaster prevention content from a database (e.g., MySQL® or MongoDB), and sends it to the terminal in JSON format or another appropriate format.

[0119] The device displays disaster prevention content through a user interface. This display can utilize touch interfaces and voice guidance, allowing users to experience a virtual disaster simulation based on the displayed content. Here, AR technology (e.g., ARCore) and game engines (e.g., Unity) are used to provide a realistic experience.

[0120] When a user operates a device and runs a simulation, the operation history is sent to a server and analyzed by an analysis engine (e.g., TENSORFLOW®). The analysis results are used to evaluate the user's behavior patterns and suggest future disaster prevention content. Throughout this cycle, users can track their learning progress.

[0121] Furthermore, by using presentation tools, citizens can participate in the creation and simulation of disaster prevention plans involving the entire community from their own smartphones. This allows multiple users to raise disaster prevention awareness together using collaborative tools.

[0122] As a concrete example, if we consider an earthquake simulation in a certain region, users can check safe evacuation routes from their homes in a virtual urban environment and learn what actions they should take during an evacuation.

[0123] An example of a prompt statement generated by the AI ​​is, "What algorithms and data structures should be used to build a user feedback system based on urban disaster simulations?" In this way, the present invention aims to improve efficiency and strengthen collaboration in disaster prevention education.

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The user accesses the application through their device. Here, the user enters authentication information (e.g., user ID and password). This information is then transmitted from the device to the server using a secure protocol.

[0127] Step 2:

[0128] The server verifies the received authentication information using an authentication API. If successful, it retrieves data related to the user profile from the database and sends it back to the terminal as a response along with the authentication result. The output response includes initial settings for the user and options for learning content.

[0129] Step 3:

[0130] The terminal receives a response from the server and displays a selection menu of disaster prevention content on the user interface. The user can then select a disaster prevention category of interest from this menu. The user's selection is recorded by the terminal and used for the next simulation.

[0131] Step 4:

[0132] Based on the category selected by the user, the device starts a virtual disaster scenario. During this simulation, the real world is simulated using AR technology, allowing the user to interactively experience the situation. The user's actions are recorded in real time.

[0133] Step 5:

[0134] The device sends user operation data it records to the server. The server uses an analysis engine to analyze the operation data and evaluate the user's behavior and reactions. This engine uses a generative AI model to obtain the analysis results. The output is used to customize the next learning content.

[0135] Step 6:

[0136] Based on the analysis results, the server determines the most suitable disaster prevention content to provide next and sends it to the terminal. The terminal then presents the received new content to the user, facilitating continued learning.

[0137] Step 7:

[0138] Users can participate in creating disaster prevention plans for their own communities using the new disaster prevention content presented. This allows for collaboration with community members through the use of various linking tools.

[0139] 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.

[0140] This invention provides an interactive disaster prevention learning system that combines an emotion engine, designed to allow users to concentrate more effectively and progress through the learning process. Specific embodiments are described below.

[0141] First, the user launches the application from their device and enters their authentication information on the login screen. The server authenticates the user based on the received authentication information and retrieves their profile data. If authentication is successful, a selection of disaster prevention content best suited to the device is displayed. The user selects a disaster prevention category of interest and begins learning.

[0142] During learning, the device utilizes an emotion engine to analyze the user's facial expressions and voice in real time, capturing the user's emotional state. This emotional data is sent to a server, where analysis evaluates how the user's current emotions are affecting the learning process. Based on the emotional analysis, the device dynamically adjusts the difficulty level and content of the learning material. For example, if the user is confused, the device will provide explanations using simpler examples.

[0143] Furthermore, the simulation method provides feedback based on emotional data. When the user is relaxed, more challenging situations can be presented to motivate them. The server provides emotionally responsive feedback and carefully observes the user's reactions. This tailored feedback allows the user to learn more effectively.

[0144] Once a learning phase is complete, users take a quiz through a quiz administration system, and the server analyzes the results. The system also considers the user's emotional state, as determined by the emotion engine, and highlights particularly important items as feedback if retention is low. When suggesting disaster prevention content for the next learning session, the user's emotional motivation is also taken into account.

[0145] As a concrete example, suppose a user selects flood control measures and begins learning. When the emotion engine detects anxiety from the user's facial expression, the device displays more basic learning materials incorporating numerous real-world examples, with the aim of alleviating anxiety. As learning progresses and the user calms down, situation-specific simulations are provided, enabling the user to choose appropriate actions during a disaster. In this way, the present invention realizes a flexible learning environment tailored to the user, providing a more meaningful learning experience.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] The user launches the application from their device and enters their login information. The device then sends the entered information to the server.

[0149] Step 2:

[0150] The server authenticates the user based on the login information it receives. If authentication is successful, the server retrieves the user's profile information and proceeds to the next step.

[0151] Step 3:

[0152] The server selects appropriate disaster prevention content based on the user's profile and sends that information to the device. The device then displays the user the disaster prevention category (e.g., earthquake, flood, typhoon, etc.).

[0153] Step 4:

[0154] The user selects a disaster prevention category of interest. The device begins displaying learning content related to the selected category.

[0155] Step 5:

[0156] As users progress through the learning content, the device activates an emotion engine that analyzes the user's facial expressions and voice in real time.

[0157] Step 6:

[0158] The device sends the analysis results from the emotion engine to the server. The server evaluates the emotional state and sends instructions to the device to adjust the learning difficulty and content based on the results.

[0159] Step 7:

[0160] Based on emotional data, the device adjusts the difficulty level and pace of learning content, providing personalized learning materials. For example, if a user is feeling anxious, it will display gentler explanations or additional hints.

[0161] Step 8:

[0162] During the learning process, the device presents a virtual disaster scenario using simulation tools. The user then selects and responds to the scenario.

[0163] Step 9:

[0164] The user's reactions during the simulation are also detected by the emotion engine. Based on this data, the server analyzes the user's suitability and provides appropriate advice and feedback in real time.

[0165] Step 10:

[0166] After the learning session is complete, the device starts a quiz using the quiz administration method. The user then answers the quiz questions.

[0167] Step 11:

[0168] The server analyzes the quiz results, taking into account user sentiment data, to evaluate the level of knowledge retention. Based on this evaluation, it generates suggestions for the next learning session.

[0169] Step 12:

[0170] Using the proposed method, the server selects the next learning content and develops a learning plan that takes emotional data into consideration to motivate the user. The terminal then presents this plan to the user.

[0171] (Example 2)

[0172] 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".

[0173] In modern society, there is a demand for educational systems that cater to individual learning needs. However, conventional systems have the problem of reduced learning effectiveness because they provide uniform content without considering the user's emotional state. Furthermore, there is a lack of mechanisms to individually evaluate the level of understanding of educational content and suggest the next learning content based on that evaluation. Therefore, there is a need to build a new system that can dynamically analyze the user's emotional state and adjust the learning experience accordingly.

[0174] 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.

[0175] In this invention, the server includes authentication means for receiving authentication information from a user and authenticating the user based on that information; display means for providing the authenticated user with a selection of educational content and displaying learning content according to the user's selection; and emotion processing means for analyzing the emotional state and dynamically adjusting the educational content based on the analysis results. This makes it possible to provide an optimal learning experience tailored to the user's emotional state and improve individual learning efficiency.

[0176] "Authentication means" refers to a function that authenticates a user to the system based on authentication information received from the user.

[0177] "Display means" refers to a function that provides authenticated users with a selection of educational content and displays learning content based on the user's selection.

[0178] "Emotional processing means" refers to a function that analyzes the user's emotional state in real time and dynamically adjusts educational content based on the analysis results.

[0179] A "simulation method" is a function that presents a virtual situation and allows the user to perform actions corresponding to that situation to execute the simulation.

[0180] "Analysis means" refers to a function that records user actions in a simulation and analyzes user behavior based on those records.

[0181] The "suggestion method" is a function that provides feedback to users based on analysis results and suggests the next educational content.

[0182] "Test implementation means" refers to a function that conducts tests to evaluate the degree of knowledge retention based on user actions.

[0183] "Collaboration means" refers to a function that provides collaboration features for users to invite other members and create plans together.

[0184] This invention provides an interactive learning system that combines an emotion processing engine. The purpose of the invention is to enable users to learn more effectively.

[0185] First, the user launches the learning application from their device and logs in by entering their username and password. The device used here is expected to be a personal computer or smartphone equipped with a camera and microphone. The server then receives the authentication information sent from the device and authenticates the user using the authentication method. If authentication is successful, the server retrieves the user's profile data from the database and sends it to the device.

[0186] Next, the authenticated user is presented with a selection of educational content. The user chooses a category of interest from the various available options and begins learning. The device provides the user with the learning content through a display mechanism that shows the selected content. At this time, an emotion processing mechanism is activated, analyzing the user's facial expressions and voice data in real time. This allows the system to understand the user's emotional state and dynamically adjust the educational content based on the results. Furthermore, by utilizing a generative AI model, the system generates emotion-responsive feedback to optimize the learning experience.

[0187] Furthermore, a crucial function is the simulation mechanism. It presents the user with a virtual learning scenario, and the user performs actions corresponding to that scenario. These actions are recorded by the system and later analyzed by the server's analysis mechanism. Based on this, the suggestion mechanism provides the content for the next learning session. At this time, the analysis results from the emotion processing engine are also taken into consideration.

[0188] As a concrete example, suppose a user chooses to learn history and is studying relevant dates and events. If the emotion engine detects that the user is becoming confused during the learning process, the device will provide simpler explanations and visuals to aid understanding. Such adjustments enhance the user's learning effectiveness and allow for smoother progress.

[0189] As an example of a prompt, inputting "What content adjustments should be made next when the user indicates 'confusion'?" into the generating AI model can produce more effective feedback. In this way, the system provides a flexible learning environment tailored to the user, realizing excellent educational support that addresses individual needs.

[0190] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0191] Step 1:

[0192] The user launches the disaster prevention learning application using their device and enters their authentication information on the login screen. The entered authentication information is sent from the device to the server. The server compares the received authentication information with its database and authenticates the user. If successful, the server retrieves the user's profile data and sends it to the device to notify the user that they have been authenticated.

[0193] Step 2:

[0194] The device displays learning content options based on user profile data received from the server. The user selects a category of interest, such as flood prevention or earthquake prevention. This selection is processed on the device, and the selected content information is sent to the server. The server then uses this information to send the necessary learning data to the device.

[0195] Step 3:

[0196] The user begins learning based on the content displayed on the device. At this point, the device's emotion processing function activates, capturing the user's facial expressions and voice data through the camera and microphone. This data is collected in real time and used to evaluate their emotional state. Once the data is analyzed, the emotional state is sent to the server.

[0197] Step 4:

[0198] The server analyzes the emotional data sent from the device and evaluates the user's current emotional state. Based on the emotional state obtained from the analysis, the server sends appropriate feedback and instructions to adjust the content to the device. For example, if the user is confused, the server instructs the device to provide simple examples or more detailed explanations.

[0199] Step 5:

[0200] The device executes instructions received from the server and adjusts the content according to the user's emotional state. Specifically, it may lower the difficulty level of the learning materials or add visual aids. This dynamic adjustment allows users to learn at their own pace and provides a learning environment that is easy to follow.

[0201] Step 6:

[0202] As part of the learning process, the device provides users with simulations using virtual situations. Users can manipulate the given options within the simulation and perform practical responses. The simulation results are recorded by the device, sent to a server, and analyzed.

[0203] Step 7:

[0204] The server analyzes user behavior based on simulation result logs and thoroughly evaluates the effectiveness of learning and areas for improvement. Based on this analysis, it sends suggestions for the next learning session and necessary feedback to the user's device. The user receives this feedback and is then presented with suggestions for the next learning session based on their learning history.

[0205] Through these steps, the system provides a customized educational experience that incorporates the user's emotional state and learning data.

[0206] (Application Example 2)

[0207] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0208] Current disaster prevention education systems provide uniform educational content without considering the user's emotional state, making it difficult for users to concentrate on learning and hindering effective knowledge retention. Furthermore, there is a lack of real-time feedback based on emotional analysis, highlighting the need for a dynamic learning environment tailored to individual users.

[0209] 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.

[0210] In this invention, the server includes authentication means for receiving and authenticating authentication information from a user; presentation means for providing authenticated users with a selection of disaster prevention information and displaying educational content according to the user's selection; emotion analysis means for analyzing the user's expressions and voice to identify their emotional state; and educational adjustment means for dynamically adjusting the difficulty level and presentation content of the educational material according to the emotional state. This makes it possible to provide an effective and personalized learning experience that is adapted to the user's emotional state.

[0211] "Authentication means" refers to a function that receives authentication information from a user, verifies the user based on that information, and identifies the systems or services that the user can access.

[0212] The "presentation method" refers to a function that provides authenticated users with various options for disaster prevention information and displays appropriate educational content according to the user's selection.

[0213] A "simulation experiment tool" is a function that presents users with a virtual emergency situation and has them perform actions according to that situation, thereby allowing users to conduct a simulated experiment to prepare for realistic scenarios.

[0214] The "analysis means" is a function that analyzes user behavior based on recorded user actions during a simulated experiment and evaluates learning progress and understanding.

[0215] The "suggestion method" is a function that provides feedback to the user based on the analysis results and recommends disaster prevention information necessary for the next learning session.

[0216] "Emotional analysis means" refers to a function that analyzes the user's expressions and voice in real time to understand their emotional state.

[0217] "Educational adjustment tools" are functions that dynamically change the difficulty level and presentation method of learning content based on acquired emotional states, providing an optimal learning experience for each individual user.

[0218] To realize this invention, a system is constructed in which a home robot or individual learning terminal interacts with the user. The authentication means receives login information when the user accesses the terminal and performs authentication on the system. This login information is transmitted to a secure server for verification.

[0219] The server presents authenticated users with a selection of disaster prevention information options, allowing them to choose educational content of interest. During this process, the server uses the sentiment analysis capabilities built into the terminal to analyze the user's expressions and voice in real time. Possible software used includes sentiment analysis libraries. The analyzed sentiment data is sent to the server, where educational adjustment mechanisms dynamically adjust the difficulty level and presentation of the learning content. This allows users to receive an appropriate learning experience tailored to their emotional state.

[0220] The hardware used includes home robots and dedicated terminals equipped with cameras and microphones. For example, when a user receives education on "flood prevention measures," the system detects the user's anxiety using emotion analysis and displays basic information on the terminal to alleviate that anxiety. Then, as the user performs actions appropriate to the situation, a simulation of a virtual emergency is initiated using simulation tools.

[0221] By utilizing a generative AI model, an example of a prompt statement could be, "Explain how a system for providing disaster preparedness education at home should adjust its content based on the user's emotions." In this way, a more effective and personalized learning experience for the user is achieved.

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The user accesses the device and enters authentication information on the login screen. The device sends this information to the server. The server authenticates the user based on the received authentication information, and if authentication is successful, retrieves the user's profile data. This process verifies the user's information, allowing them to proceed to the next step. The input is authentication information, and the output is profile data.

[0225] Step 2:

[0226] The server presents authenticated users with a selection of disaster prevention information. Users select the disaster prevention category they are interested in. The terminal displays educational content corresponding to this selection. The input is the user's profile and disaster prevention category selection, and the output is the display of learning content. At this point, the user is ready to begin their individual learning activities.

[0227] Step 3:

[0228] As the user progresses through the learning process, their facial expressions and voice are captured in real time via the camera and microphone on the device. The device uses emotion analysis to analyze the user's emotional state. The resulting emotional data is sent to a server. The input is the user's facial expressions and voice data, and the output is the analyzed emotional data.

[0229] Step 4:

[0230] The server receives emotional data and uses educational adjustment mechanisms to dynamically adjust the current learning content according to the user's emotions. For example, if the user is confused, simpler learning materials will be displayed on the device. The input is emotional data and the current learning content, and the output is the adjusted learning content. This improves the user's learning experience.

[0231] Step 5:

[0232] A hypothetical emergency situation is presented, and the user performs corresponding actions on their terminal. These actions are executed as a simulation using a simulation tool. The user's actions are recorded by the terminal, and this data is sent to a server. The input is the user's actions, and the output is the execution of the simulation and the record of those actions.

[0233] Step 6:

[0234] The server analyzes the operations recorded by the simulation tool and provides the user with feedback based on the analysis results. The analysis tool evaluates the user's learning progress and understanding. The input is the operation record, and the output is the analysis results and feedback.

[0235] Step 7:

[0236] The server uses a suggestion mechanism to propose disaster prevention information necessary for the next learning session based on the analysis results. Information tailored to the user's learning curve is displayed on the terminal, enabling more effective preparation for the next learning opportunity. The input is the analysis results, and the output is the suggestions for the next learning session.

[0237] An example of a prompt using a generative AI model is: "Explain how a system for providing disaster preparedness education at home should adjust its content based on the user's emotions."

[0238] 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.

[0239] 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.

[0240] 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.

[0241] [Second Embodiment]

[0242] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0243] 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.

[0244] 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).

[0245] 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.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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".

[0254] This invention provides an interactive disaster prevention learning system designed to enable users to effectively acquire disaster prevention knowledge and actions. Specific embodiments are described below.

[0255] The user launches the application from their device and enters their personal authentication information on the login screen. The server receives this information and authenticates the user using the appropriate authentication method. If this process is successful, the server selects the most appropriate disaster prevention content based on the user's profile information and sends it to the device.

[0256] The terminal displays a list of disaster prevention categories (e.g., earthquakes, typhoons, floods, etc.) to the user based on information received from the server. The user selects a category of interest and starts interactive learning content related to that category.

[0257] During the learning process, the device displays selected content, and the user experiences a virtual disaster scenario based on that content. The simulation allows the user to choose actions for each situation and receive real-time feedback based on those choices.

[0258] Subsequently, the user takes a quiz based on what they have learned through a quiz administration system. The server evaluates the user's answers and uses the data obtained through analysis to check the level of knowledge retention. The evaluation results are sent to the terminal and presented to the user as feedback.

[0259] The proposed method allows the server to comprehensively analyze the user's learning history and evaluation results to suggest disaster prevention content deemed optimal for the next learning session. This suggestion is then notified to the user via their device.

[0260] In addition, using collaborative methods, users can invite family members and community members to the app to jointly create disaster preparedness plans. This feature is designed to promote the sharing of disaster preparedness awareness and the effective implementation of the plans.

[0261] For example, if a user selects the earthquake preparedness category, the simulation will show a scenario where an earthquake occurs in a home. The user selects which piece of furniture to hide under, and receives real-time feedback on whether their selection is correct. In the subsequent quiz, questions such as "Where is the safest place to be during an earthquake?" are asked, and the user's level of understanding is evaluated.

[0262] In this way, the present invention is a system that enables users to learn practical and in-depth disaster prevention knowledge.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] The user launches the application from their device and enters their personal authentication information on the login screen.

[0266] Step 2:

[0267] The terminal sends user authentication information to the server, and the server uses that information to verify credentials against a database and authenticate the user.

[0268] Step 3:

[0269] The server retrieves the profile information of the authenticated user and selects the most suitable disaster prevention content based on that information. The server then sends that content information to the terminal.

[0270] Step 4:

[0271] The terminal displays content information received from the server to the user. The user selects a disaster prevention category (earthquake, typhoon, flood, etc.) based on their interests.

[0272] Step 5:

[0273] Interactive learning content related to the selected category is displayed on the device. The user then begins learning based on that content.

[0274] Step 6:

[0275] As the learning process progresses, the device uses simulation tools to present virtual disaster scenarios. Users select actions according to the situation and advance the simulation.

[0276] Step 7:

[0277] The server records the user's selection content and its results in real time, and evaluates the user's behavior using analysis means. This evaluation result is immediately fed back to the user through the terminal.

[0278] Step 8:

[0279] After the simulation ends, the terminal presents a quiz to the user using the quiz implementation means. The quiz is based on the content learned in the simulation.

[0280] Step 9:

[0281] When the quiz is completed, the server analyzes the user's answers and evaluates the degree of knowledge retention. The evaluation result and feedback are sent to the terminal.

[0282] Step 10:

[0283] Using the proposal means, the server selects the disaster prevention content to be learned next based on the learning history and evaluation results. This information is proposed to the user via the terminal.

[0284] Step 11:

[0285] By means of cooperation, the user invites other members to the application and provides an option for jointly creating a disaster prevention plan. The generated plan is shared among the members through the terminal.

[0286] (Example 1)

[0287] Next, 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".

[0288] Conventional disaster prevention learning systems have faced challenges such as difficulty in providing individually optimized disaster prevention content to users and a lack of effective means for providing feedback on learning results. Furthermore, they lacked sufficient functionality for collaborative disaster prevention planning, making it difficult for users to easily work together.

[0289] 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.

[0290] In this invention, the server includes an information authentication means that receives authentication information from a user and authenticates the user based on that authentication information; a content display means that provides the authenticated user with options to select disaster prevention content according to the user's attributes and displays content according to the user's selection; and a virtual situation presentation means that presents a virtual disaster situation and allows the user to perform operations according to that situation to execute a simulation. This enables the provision of disaster prevention content optimized for the user, highly effective feedback for learning, and cooperation in jointly formulating a disaster prevention plan.

[0291] An "information authentication method" is a function that verifies the legitimacy of a user based on the authentication information received from that user.

[0292] The "content display means" is a function that provides authenticated users with the most suitable disaster prevention learning content as a selection based on their attribute information, and displays the learning content based on that selection.

[0293] A "virtual situation presentation means" is a function that presents a virtual disaster scenario to the user and accepts user actions or executes simulations according to that situation.

[0294] "Behavioral analysis means" refers to a function that analyzes user behavior based on user actions recorded in simulations.

[0295] The "content suggestion method" is a function that suggests the most suitable disaster prevention content for the next learning session based on the user's learning history and operation results.

[0296] A "problem-solving tool" is a function that evaluates the degree of knowledge retention based on user actions and provides preliminary learning such as quizzes and tests based on that evaluation.

[0297] "Collaboration provision means" refers to a function that provides the necessary collaborative features for users to invite other participants and create disaster prevention plans together.

[0298] This invention provides an interactive learning system that enables users to effectively acquire disaster prevention knowledge. This system is primarily operated by a server, terminals, and the user.

[0299] The server first receives authentication information entered by the user and uses this information to verify the user's legitimacy using an information authentication method. If this authentication is successful, the server uses a generative AI model to consider the user's attribute information and past learning history to select the most suitable disaster prevention content. The server then sends this selected content to the terminal.

[0300] The terminal uses a content display mechanism to show the user a list of disaster prevention categories (e.g., earthquakes, floods, etc.) based on the information received from the server. When the user selects a category of interest, a simulation related to that category is started using a virtual situation presentation mechanism. This allows the user to experience operations in a virtual disaster situation and receive real-time feedback.

[0301] Once the simulation is complete, the user automatically participates in a quiz through a problem-solving tool. The server uses behavioral analysis tools to evaluate the user's knowledge retention based on their quiz answers. The evaluation results are then sent back to the terminal and presented to the user as feedback.

[0302] Furthermore, the server uses the content proposal means to consider the user's learning history and the degree of knowledge fixation, and proposes disaster prevention content for the next learning. The user can also invite other participants to the application through the cooperation providing means and jointly formulate a disaster prevention plan.

[0303] For example, when the user selects the category of earthquake countermeasures, the terminal shows a scenario where an earthquake occurs in the house, and the user experiences choosing a safe place and taking evacuation actions. At that time, it is assumed that a prompt sentence such as "Please teach me the optimal method to act safely when an earthquake occurs" will be input into the system.

[0304] In this way, the present invention functions as a system that provides users with a practical and effective opportunity for disaster prevention learning.

[0305] The flow of the specific process in Example 1 will be described using FIG. 11.

[0306] Step 1:

[0307] The user launches the disaster prevention learning application from the terminal and enters personal authentication information on the login screen. The input information includes a user ID and a password. The terminal sends this authentication information to the server. The authentication information serves as a search key for the database.

[0308] <​​​​​​​​​​​​The server retrieves the authenticated user's profile information and past learning history, and uses a generative AI model to select the most suitable disaster prevention content. User attributes and history data are used as input, and selected content information is generated as output. This process involves the selection of personalized content based on user attributes.

[0312] Step 4:

[0313] The server sends the selected disaster prevention content to the terminal. Based on the received content, the terminal uses a content display device to show the user a list of disaster prevention categories. It receives content data as input and displays a category list to the user as output.

[0314] Step 5:

[0315] The user selects a disaster prevention category of interest. The terminal sends the selection to the server, and a simulation of that category is executed through a virtual situation presentation system. The user's selection is used as input, and the start of the simulation is triggered as output.

[0316] Step 6:

[0317] The user performs actions in response to a given virtual disaster scenario within the simulation. The terminal records these actions in real time. It receives user instructions as input and generates an operation log as output. Appropriate feedback is provided to the user in real time.

[0318] Step 7:

[0319] After the simulation ends, the terminal sends the recorded operation data to the server. The server uses behavioral analysis tools to analyze the user's actions and evaluate the degree of knowledge retention. The user's past operation logs are input into this process. The evaluation results become the analysis output and are sent back to the terminal.

[0320] Step 8:

[0321] The terminal presents the received evaluation results to the user and administers a quiz using problem-solving tools. The user answers the quiz, and the results are sent to the server. The quiz answer data is generated as input, and the evaluated score is generated as output.

[0322] Step 9:

[0323] Based on the user's learning progress and history, the server uses a content suggestion system to propose the most suitable disaster prevention content for the next learning session through an AI model. The suggested content is displayed to the user via their device. This supports continuous learning.

[0324] Step 10:

[0325] Users can invite other participants to the app using collaborative methods to jointly develop disaster preparedness plans. This function generates an invitation link based on the information entered by the user, and the output allows for direct collaborative editing of the disaster preparedness plan with others. This aims to improve collaborative disaster preparedness awareness.

[0326] (Application Example 1)

[0327] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0328] In modern urban environments, raising disaster preparedness awareness and establishing effective disaster prevention knowledge are crucial challenges. However, disaster prevention education based on traditional methods often limits the understanding and practical application of the learned content. Furthermore, a lack of coordination throughout the community can prevent the full utilization of response capabilities during crises. Therefore, there is a need for a learning system that allows for the practical acquisition of disaster prevention knowledge, as well as mechanisms that promote coordination and collaborative planning throughout the community.

[0329] 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.

[0330] This invention includes an authentication means for receiving authentication information from a user and authenticating the user based on that information, a presentation means for providing disaster response exercises in a virtual environment through an information device accessible to citizens, and a response means for jointly creating and implementing disaster prevention plans in cooperation with the local community. This enables users to interactively experience disaster prevention simulations and to formulate and implement effective disaster prevention plans in cooperation with the entire community.

[0331] An "authentication method" is a system that receives authentication information from a user, verifies the user based on that information, and performs authentication.

[0332] A "display means" is a device that provides authenticated users with a selection of disaster prevention content and presents learning content according to the user's selection through the screen of an information terminal.

[0333] A "simulation mechanism" is a system that presents a virtual disaster situation and allows the user to perform actions in response to that situation to execute the simulation.

[0334] The "analysis method" is a system that analyzes user actions recorded during the simulation and evaluates user behavior based on those records.

[0335] "Presentation method" refers to a method of providing disaster response exercises in a virtual environment through information devices accessible to citizens.

[0336] "Response measures" refer to methods and processes for collaborating with local communities to jointly create and effectively implement disaster prevention plans.

[0337] This invention is a system designed to enable effective learning and practice of disaster prevention knowledge. The following process is used to realize this system.

[0338] The server first verifies the authentication information received from the user via a secure communication protocol and performs authentication. This is done using an authentication API and a secure protocol (e.g., HTTPS). Once authentication is complete, the server retrieves the user's profile information, selects relevant disaster prevention content from a database (e.g., MySQL or MongoDB), and sends it to the terminal in JSON format or another appropriate format.

[0339] The device displays disaster prevention content through a user interface. This display can utilize touch interfaces and voice guidance, allowing users to experience a virtual disaster simulation based on the displayed content. Here, AR technology (e.g., ARCore) and game engines (e.g., Unity) are used to provide a realistic experience.

[0340] When a user operates a device and runs a simulation, the operation history is sent to a server and analyzed by an analysis engine (e.g., TensorFlow). The analysis results are used to evaluate the user's behavior patterns and suggest future disaster prevention content. Throughout this cycle, users can track their learning progress.

[0341] Furthermore, by using presentation tools, citizens can participate in the creation and simulation of disaster prevention plans involving the entire community from their own smartphones. This allows multiple users to raise disaster prevention awareness together using collaborative tools.

[0342] As a concrete example, if we consider an earthquake simulation in a certain region, users can check safe evacuation routes from their homes in a virtual urban environment and learn what actions they should take during an evacuation.

[0343] An example of a prompt statement generated by the AI ​​is, "What algorithms and data structures should be used to build a user feedback system based on urban disaster simulations?" In this way, the present invention aims to improve efficiency and strengthen collaboration in disaster prevention education.

[0344] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0345] Step 1:

[0346] The user accesses the application through their device. Here, the user enters authentication information (e.g., user ID and password). This information is then transmitted from the device to the server using a secure protocol.

[0347] Step 2:

[0348] The server verifies the received authentication information using an authentication API. If successful, it retrieves data related to the user profile from the database and sends it back to the terminal as a response along with the authentication result. The output response includes initial settings for the user and options for learning content.

[0349] Step 3:

[0350] The terminal receives a response from the server and displays a selection menu of disaster prevention content on the user interface. The user can then select a disaster prevention category of interest from this menu. The user's selection is recorded by the terminal and used for the next simulation.

[0351] Step 4:

[0352] Based on the category selected by the user, the device starts a virtual disaster scenario. During this simulation, the real world is simulated using AR technology, allowing the user to interactively experience the situation. The user's actions are recorded in real time.

[0353] Step 5:

[0354] The device sends user operation data it records to the server. The server uses an analysis engine to analyze the operation data and evaluate the user's behavior and reactions. This engine uses a generative AI model to obtain the analysis results. The output is used to customize the next learning content.

[0355] Step 6:

[0356] Based on the analysis results, the server determines the most suitable disaster prevention content to provide next and sends it to the terminal. The terminal then presents the received new content to the user, facilitating continued learning.

[0357] Step 7:

[0358] Users can participate in creating disaster prevention plans for their own communities using the new disaster prevention content presented. This allows for collaboration with community members through the use of various linking tools.

[0359] 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.

[0360] This invention provides an interactive disaster prevention learning system that combines an emotion engine, designed to allow users to concentrate more effectively and progress through the learning process. Specific embodiments are described below.

[0361] First, the user launches the application from their device and enters their authentication information on the login screen. The server authenticates the user based on the received authentication information and retrieves their profile data. If authentication is successful, a selection of disaster prevention content best suited to the device is displayed. The user selects a disaster prevention category of interest and begins learning.

[0362] During learning, the device utilizes an emotion engine to analyze the user's facial expressions and voice in real time, capturing the user's emotional state. This emotional data is sent to a server, where analysis evaluates how the user's current emotions are affecting the learning process. Based on the emotional analysis, the device dynamically adjusts the difficulty level and content of the learning material. For example, if the user is confused, the device will provide explanations using simpler examples.

[0363] Furthermore, the simulation method provides feedback based on emotional data. When the user is relaxed, more challenging situations can be presented to motivate them. The server provides emotionally responsive feedback and carefully observes the user's reactions. This tailored feedback allows the user to learn more effectively.

[0364] Once a learning phase is complete, users take a quiz through a quiz administration system, and the server analyzes the results. The system also considers the user's emotional state, as determined by the emotion engine, and highlights particularly important items as feedback if retention is low. When suggesting disaster prevention content for the next learning session, the user's emotional motivation is also taken into account.

[0365] As a concrete example, suppose a user selects flood prevention measures and begins learning. When the emotion engine detects anxiety from the user's facial expression, the device displays more basic learning materials incorporating numerous real-world examples, with the aim of alleviating anxiety. As learning progresses and the user calms down, situation-specific simulations are provided, enabling the user to choose appropriate actions during a disaster. In this way, the present invention realizes a flexible learning environment tailored to the user, providing a more meaningful learning experience.

[0366] The following describes the processing flow.

[0367] Step 1:

[0368] The user launches the application from their device and enters their login information. The device then sends the entered information to the server.

[0369] Step 2:

[0370] The server authenticates the user based on the login information it receives. If authentication is successful, the server retrieves the user's profile information and proceeds to the next step.

[0371] Step 3:

[0372] The server selects appropriate disaster prevention content based on the user's profile and sends that information to the device. The device then displays the user the disaster prevention category (e.g., earthquake, flood, typhoon, etc.).

[0373] Step 4:

[0374] The user selects a disaster prevention category of interest. The device begins displaying learning content related to the selected category.

[0375] Step 5:

[0376] As users progress through the learning content, the device activates an emotion engine that analyzes the user's facial expressions and voice in real time.

[0377] Step 6:

[0378] The device sends the analysis results from the emotion engine to the server. The server evaluates the emotional state and sends instructions to the device to adjust the learning difficulty and content based on the results.

[0379] Step 7:

[0380] Based on emotional data, the device adjusts the difficulty level and pace of learning content, providing personalized learning materials. For example, if a user is feeling anxious, it will display gentler explanations or additional hints.

[0381] Step 8:

[0382] During the learning process, the device presents a virtual disaster scenario using simulation tools. The user then selects and responds to the scenario.

[0383] Step 9:

[0384] The user's reactions during the simulation are also detected by the emotion engine. Based on this data, the server analyzes the user's suitability and provides appropriate advice and feedback in real time.

[0385] Step 10:

[0386] After the learning session is complete, the device starts a quiz using the quiz administration method. The user then answers the quiz questions.

[0387] Step 11:

[0388] The server analyzes the quiz results, taking into account user sentiment data, to evaluate the level of knowledge retention. Based on this evaluation, it generates suggestions for the next learning session.

[0389] Step 12:

[0390] Using the proposed method, the server selects the next learning content and develops a learning plan that takes emotional data into consideration to motivate the user. The terminal then presents this plan to the user.

[0391] (Example 2)

[0392] 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".

[0393] In modern society, there is a demand for educational systems that cater to individual learning needs. However, conventional systems have the problem of reduced learning effectiveness because they provide uniform content without considering the user's emotional state. Furthermore, there is a lack of mechanisms to individually evaluate the level of understanding of educational content and suggest the next learning content based on that evaluation. Therefore, there is a need to build a new system that can dynamically analyze the user's emotional state and adjust the learning experience accordingly.

[0394] 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.

[0395] In this invention, the server includes authentication means for receiving authentication information from a user and authenticating the user based on that information; display means for providing the authenticated user with a selection of educational content and displaying learning content according to the user's selection; and emotion processing means for analyzing the emotional state and dynamically adjusting the educational content based on the analysis results. This makes it possible to provide an optimal learning experience tailored to the user's emotional state and improve individual learning efficiency.

[0396] "Authentication means" refers to a function that authenticates a user to the system based on authentication information received from the user.

[0397] "Display means" refers to a function that provides authenticated users with a selection of educational content and displays learning content based on the user's selection.

[0398] "Emotional processing means" refers to a function that analyzes the user's emotional state in real time and dynamically adjusts educational content based on the analysis results.

[0399] A "simulation method" is a function that presents a virtual situation and allows the user to perform actions corresponding to that situation to execute the simulation.

[0400] "Analysis means" refers to a function that records user actions in a simulation and analyzes user behavior based on those records.

[0401] The "suggestion method" is a function that provides feedback to users based on analysis results and suggests the next educational content.

[0402] "Test implementation means" refers to a function that conducts tests to evaluate the degree of knowledge retention based on user actions.

[0403] "Collaboration means" refers to a function that provides collaboration features for users to invite other members and create plans together.

[0404] This invention provides an interactive learning system that combines an emotion processing engine. The purpose of the invention is to enable users to learn more effectively.

[0405] First, the user launches the learning application from their device and logs in by entering their username and password. The device used here is expected to be a personal computer or smartphone equipped with a camera and microphone. The server then receives the authentication information sent from the device and authenticates the user using the authentication method. If authentication is successful, the server retrieves the user's profile data from the database and sends it to the device.

[0406] Next, the authenticated user is presented with a selection of educational content. The user chooses a category of interest from the various available options and begins learning. The device provides the user with the learning content through a display mechanism that shows the selected content. At this time, an emotion processing mechanism is activated, analyzing the user's facial expressions and voice data in real time. This allows the system to understand the user's emotional state and dynamically adjust the educational content based on the results. Furthermore, by utilizing a generative AI model, the system generates emotion-responsive feedback to optimize the learning experience.

[0407] Furthermore, a crucial function is the simulation mechanism. It presents the user with a virtual learning scenario, and the user performs actions corresponding to that scenario. These actions are recorded by the system and later analyzed by the server's analysis mechanism. Based on this, the suggestion mechanism provides the content for the next learning session. At this time, the analysis results from the emotion processing engine are also taken into consideration.

[0408] As a concrete example, suppose a user chooses to learn history and is studying relevant dates and events. If the emotion engine detects that the user is becoming confused during the learning process, the device will provide simpler explanations and visuals to aid understanding. Such adjustments enhance the user's learning effectiveness and allow for smoother progress.

[0409] As an example of a prompt, inputting "What content adjustments should be made next when the user indicates 'confusion'?" into the generating AI model can produce more effective feedback. In this way, the system provides a flexible learning environment tailored to the user, realizing excellent educational support that addresses individual needs.

[0410] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0411] Step 1:

[0412] The user launches the disaster prevention learning application using their device and enters their authentication information on the login screen. The entered authentication information is sent from the device to the server. The server compares the received authentication information with its database and authenticates the user. If successful, the server retrieves the user's profile data and sends it to the device to notify the user that they have been authenticated.

[0413] Step 2:

[0414] The device displays learning content options based on user profile data received from the server. The user selects a category of interest, such as flood prevention or earthquake prevention. This selection is processed on the device, and the selected content information is sent to the server. The server then uses this information to send the necessary learning data to the device.

[0415] Step 3:

[0416] The user begins learning based on the content displayed on the device. At this point, the device's emotion processing function activates, capturing the user's facial expressions and voice data through the camera and microphone. This data is collected in real time and used to evaluate their emotional state. Once the data is analyzed, the emotional state is sent to the server.

[0417] Step 4:

[0418] The server analyzes the emotional data sent from the device and evaluates the user's current emotional state. Based on the emotional state obtained from the analysis, the server sends appropriate feedback and instructions to adjust the content to the device. For example, if the user is confused, the server instructs the device to provide simple examples or more detailed explanations.

[0419] Step 5:

[0420] The device executes instructions received from the server and adjusts the content according to the user's emotional state. Specifically, it may lower the difficulty level of the learning materials or add visual aids. This dynamic adjustment allows users to learn at their own pace and provides a learning environment that is easy to follow.

[0421] Step 6:

[0422] As part of the learning process, the device provides users with simulations using virtual situations. Users can manipulate the given options within the simulation and perform practical responses. The simulation results are recorded by the device, sent to a server, and analyzed.

[0423] Step 7:

[0424] The server analyzes user behavior based on simulation result logs and thoroughly evaluates the effectiveness of learning and areas for improvement. Based on this analysis, it sends suggestions for the next learning session and necessary feedback to the user's device. The user receives this feedback and is then presented with suggestions for the next learning session based on their learning history.

[0425] Through these steps, the system provides a customized educational experience that incorporates the user's emotional state and learning data.

[0426] (Application Example 2)

[0427] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0428] Current disaster prevention education systems provide uniform educational content without considering the user's emotional state, making it difficult for users to concentrate on learning and hindering effective knowledge retention. Furthermore, there is a lack of real-time feedback based on emotional analysis, highlighting the need for a dynamic learning environment tailored to individual users.

[0429] 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.

[0430] In this invention, the server includes authentication means for receiving and authenticating authentication information from a user; presentation means for providing authenticated users with a selection of disaster prevention information and displaying educational content according to the user's selection; emotion analysis means for analyzing the user's expressions and voice to identify their emotional state; and educational adjustment means for dynamically adjusting the difficulty level and presentation content of the educational material according to the emotional state. This makes it possible to provide an effective and personalized learning experience that is adapted to the user's emotional state.

[0431] "Authentication means" refers to a function that receives authentication information from a user, verifies the user based on that information, and identifies the systems or services that the user can access.

[0432] The "presentation method" refers to a function that provides authenticated users with various options for disaster prevention information and displays appropriate educational content according to the user's selection.

[0433] A "simulation experiment tool" is a function that presents users with a virtual emergency situation and has them perform actions according to that situation, thereby allowing users to conduct a simulated experiment to prepare for realistic scenarios.

[0434] The "analysis means" is a function that analyzes user behavior based on recorded user actions during a simulated experiment and evaluates learning progress and understanding.

[0435] The "suggestion method" is a function that provides feedback to the user based on the analysis results and recommends disaster prevention information necessary for the next learning session.

[0436] "Emotional analysis means" refers to a function that analyzes the user's expressions and voice in real time to understand their emotional state.

[0437] "Educational adjustment tools" are functions that dynamically change the difficulty level and presentation method of learning content based on acquired emotional states, providing an optimal learning experience for each individual user.

[0438] To realize this invention, a system is constructed in which a home robot or individual learning terminal interacts with the user. The authentication means receives login information when the user accesses the terminal and performs authentication on the system. This login information is transmitted to a secure server for verification.

[0439] The server presents authenticated users with a selection of disaster prevention information options, allowing them to choose educational content of interest. During this process, the server uses the sentiment analysis capabilities built into the terminal to analyze the user's expressions and voice in real time. Possible software used includes sentiment analysis libraries. The analyzed sentiment data is sent to the server, where educational adjustment mechanisms dynamically adjust the difficulty level and presentation of the learning content. This allows users to receive an appropriate learning experience tailored to their emotional state.

[0440] The hardware used includes home robots and dedicated terminals equipped with cameras and microphones. For example, when a user receives education on "flood prevention measures," the system detects the user's anxiety using emotion analysis and displays basic information on the terminal to alleviate that anxiety. Then, as the user performs actions appropriate to the situation, a simulation of a virtual emergency is initiated using simulation tools.

[0441] By utilizing a generative AI model, an example of a prompt statement could be, "Explain how a system for providing disaster preparedness education at home should adjust its content based on the user's emotions." In this way, a more effective and personalized learning experience for the user is achieved.

[0442] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0443] Step 1:

[0444] The user accesses the device and enters authentication information on the login screen. The device sends this information to the server. The server authenticates the user based on the received authentication information, and if authentication is successful, retrieves the user's profile data. This process verifies the user's information, allowing them to proceed to the next step. The input is authentication information, and the output is profile data.

[0445] Step 2:

[0446] The server presents authenticated users with a selection of disaster prevention information. Users select the disaster prevention category they are interested in. The terminal displays educational content corresponding to this selection. The input is the user's profile and disaster prevention category selection, and the output is the display of learning content. At this point, the user is ready to begin their individual learning activities.

[0447] Step 3:

[0448] As the user progresses through the learning process, their facial expressions and voice are captured in real time via the camera and microphone on the device. The device uses emotion analysis to analyze the user's emotional state. The resulting emotional data is sent to a server. The input is the user's facial expressions and voice data, and the output is the analyzed emotional data.

[0449] Step 4:

[0450] The server receives emotional data and uses educational adjustment mechanisms to dynamically adjust the current learning content according to the user's emotions. For example, if the user is confused, simpler learning materials will be displayed on the device. The input is emotional data and the current learning content, and the output is the adjusted learning content. This improves the user's learning experience.

[0451] Step 5:

[0452] A hypothetical emergency situation is presented, and the user performs corresponding actions on their terminal. These actions are executed as a simulation using a simulation tool. The user's actions are recorded by the terminal, and this data is sent to a server. The input is the user's actions, and the output is the execution of the simulation and the record of those actions.

[0453] Step 6:

[0454] The server analyzes the operations recorded by the simulation device and provides the user with feedback based on the analysis results. The user's learning progress and understanding are evaluated through the analysis. The input is the operation record, and the output is the analysis results and feedback.

[0455] Step 7:

[0456] The server uses a suggestion mechanism to propose disaster prevention information necessary for the next learning session based on the analysis results. Information tailored to the user's learning curve is displayed on the terminal, enabling more effective preparation for the next learning opportunity. The input is the analysis results, and the output is the suggestions for the next learning session.

[0457] An example of a prompt using a generative AI model is: "Explain how a system for providing disaster preparedness education at home should adjust its content based on the user's emotions."

[0458] 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.

[0459] 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.

[0460] 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.

[0461] [Third Embodiment]

[0462] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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".

[0474] This invention provides an interactive disaster prevention learning system designed to enable users to effectively acquire disaster prevention knowledge and actions. Specific embodiments are described below.

[0475] The user launches the application from their device and enters their personal authentication information on the login screen. The server receives this information and authenticates the user using the appropriate authentication method. If this process is successful, the server selects the most appropriate disaster prevention content based on the user's profile information and sends it to the device.

[0476] The terminal displays a list of disaster prevention categories (e.g., earthquakes, typhoons, floods, etc.) to the user based on information received from the server. The user selects a category of interest and starts interactive learning content related to that category.

[0477] During the learning process, the device displays selected content, and the user experiences a virtual disaster scenario based on that content. The simulation allows the user to choose actions for each situation and receive real-time feedback based on those choices.

[0478] Subsequently, the user takes a quiz based on what they have learned through a quiz administration system. The server evaluates the user's answers and uses the data obtained through analysis to check the level of knowledge retention. The evaluation results are sent to the terminal and presented to the user as feedback.

[0479] The proposed method allows the server to comprehensively analyze the user's learning history and evaluation results to suggest disaster prevention content deemed optimal for the next learning session. This suggestion is then notified to the user via their device.

[0480] In addition, using collaborative methods, users can invite family members and community members to the app to jointly create disaster preparedness plans. This feature is designed to promote the sharing of disaster preparedness awareness and the effective implementation of the plans.

[0481] For example, if a user selects the earthquake preparedness category, the simulation will show a scenario where an earthquake occurs in a home. The user selects which piece of furniture to hide under, and receives real-time feedback on whether their selection is correct. In the subsequent quiz, questions such as "Where is the safest place to be during an earthquake?" are asked, and the user's level of understanding is evaluated.

[0482] In this way, the present invention is a system that enables users to learn practical and in-depth disaster prevention knowledge.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] The user launches the application from their device and enters their personal authentication information on the login screen.

[0486] Step 2:

[0487] The terminal sends user authentication information to the server, and the server uses that information to verify credentials against a database and authenticate the user.

[0488] Step 3:

[0489] The server retrieves the profile information of the authenticated user and selects the most suitable disaster prevention content based on that information. The server then sends that content information to the terminal.

[0490] Step 4:

[0491] The terminal displays content information received from the server to the user. The user selects a disaster prevention category (earthquake, typhoon, flood, etc.) based on their interests.

[0492] Step 5:

[0493] Interactive learning content related to the selected category is displayed on the device. The user then begins learning based on that content.

[0494] Step 6:

[0495] As the learning process progresses, the device uses simulation tools to present virtual disaster scenarios. Users select actions according to the situation and advance the simulation.

[0496] Step 7:

[0497] The server records the user's choices and their results in real time, and uses analytical tools to evaluate the user's behavior. This evaluation result is immediately fed back to the user through the terminal.

[0498] Step 8:

[0499] After the simulation ends, the device will present the user with a quiz using a quiz administration method. The quiz will be based on what was learned during the simulation.

[0500] Step 9:

[0501] Once the quiz is completed, the server analyzes the user's answers and evaluates their knowledge retention. The evaluation results and feedback are sent to the device.

[0502] Step 10:

[0503] Using the proposed method, the server selects the next disaster prevention content to be learned based on the learning history and evaluation results. This information is then suggested to the user via the terminal.

[0504] Step 11:

[0505] The collaboration mechanism allows users to invite other members to the app and provides an option for collaboratively creating disaster preparedness plans. The generated plans are shared among members via their devices.

[0506] (Example 1)

[0507] 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."

[0508] Conventional disaster prevention learning systems have faced challenges such as difficulty in providing individually optimized disaster prevention content to users and a lack of effective means for providing feedback on learning results. Furthermore, they lacked sufficient functionality for collaborative disaster prevention planning, making it difficult for users to easily work together.

[0509] 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.

[0510] In this invention, the server includes an information authentication means that receives authentication information from a user and authenticates the user based on that authentication information; a content display means that provides the authenticated user with options to select disaster prevention content according to the user's attributes and displays content according to the user's selection; and a virtual situation presentation means that presents a virtual disaster situation and allows the user to perform operations according to that situation to execute a simulation. This enables the provision of disaster prevention content optimized for the user, highly effective feedback for learning, and cooperation in jointly formulating a disaster prevention plan.

[0511] An "information authentication method" is a function that verifies the legitimacy of a user based on the authentication information received from that user.

[0512] The "content display means" is a function that provides authenticated users with the most suitable disaster prevention learning content as a selection based on their attribute information, and displays the learning content based on that selection.

[0513] A "virtual situation presentation means" is a function that presents a virtual disaster scenario to the user and accepts user actions or executes simulations according to that situation.

[0514] "Behavioral analysis means" refers to a function that analyzes user behavior based on user actions recorded in simulations.

[0515] The "content suggestion method" is a function that suggests the most suitable disaster prevention content for the next learning session based on the user's learning history and operation results.

[0516] A "problem-solving tool" is a function that evaluates the degree of knowledge retention based on user actions and provides preliminary learning such as quizzes and tests based on that evaluation.

[0517] "Collaboration provision means" refers to a function that provides the necessary collaborative features for users to invite other participants and create disaster prevention plans together.

[0518] This invention provides an interactive learning system that enables users to effectively acquire disaster prevention knowledge. This system is primarily operated by a server, terminals, and the user.

[0519] The server first receives authentication information entered by the user and uses this information to verify the user's legitimacy using an information authentication method. If this authentication is successful, the server uses a generative AI model to consider the user's attribute information and past learning history to select the most suitable disaster prevention content. The server then sends this selected content to the terminal.

[0520] The terminal uses a content display mechanism to show the user a list of disaster prevention categories (e.g., earthquakes, floods, etc.) based on the information received from the server. When the user selects a category of interest, a simulation related to that category is started using a virtual situation presentation mechanism. This allows the user to experience operations in a virtual disaster situation and receive real-time feedback.

[0521] Once the simulation is complete, the user automatically participates in a quiz through a problem-solving tool. The server uses behavioral analysis tools to evaluate the user's knowledge retention based on their quiz answers. The evaluation results are then sent back to the terminal and presented to the user as feedback.

[0522] Furthermore, the server uses a content suggestion mechanism to consider the user's learning history and knowledge retention level, and suggests disaster prevention content for the next learning session. Users can also invite other participants to the application through a collaborative provision mechanism and jointly develop disaster prevention plans.

[0523] To give a specific example, if a user selects the earthquake preparedness category, the device will display a scenario in which an earthquake occurs in the home, and the user will experience choosing a safe place and taking evacuation action. At that time, it is expected that a prompt message such as "Please tell me the best way to act safely when an earthquake occurs" will be entered into the system.

[0524] In this way, the present invention functions as a system that provides users with practical and effective opportunities for disaster prevention learning.

[0525] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0526] Step 1:

[0527] The user launches the disaster prevention learning application from their device and enters their personal authentication information on the login screen. This information includes a user ID and password. The device then sends this authentication information to the server. The output of this server uses the authentication information as a search key in the database.

[0528] Step 2:

[0529] The server searches the database for user information based on the received authentication information and verifies the user's legitimacy using an information authentication method. The data processing performed in this process involves comparing the input information with the records in the database. If user authentication is successful based on the results, the authentication status is output to the terminal.

[0530] Step 3:

[0531] The server retrieves the authenticated user's profile information and past learning history, and uses a generative AI model to select the most suitable disaster prevention content. User attributes and history data are used as input, and selected content information is generated as output. This process involves the selection of personalized content based on user attributes.

[0532] Step 4:

[0533] The server sends the selected disaster prevention content to the terminal. Based on the received content, the terminal uses a content display device to show the user a list of disaster prevention categories. It receives content data as input and displays a category list to the user as output.

[0534] Step 5:

[0535] The user selects a disaster prevention category of interest. The terminal sends the selection to the server, and a simulation of that category is executed through a virtual situation presentation system. The user's selection is used as input, and the start of the simulation is triggered as output.

[0536] Step 6:

[0537] The user performs actions in response to a given virtual disaster scenario within the simulation. The terminal records these actions in real time. It receives user instructions as input and generates an operation log as output. Appropriate feedback is provided to the user in real time.

[0538] Step 7:

[0539] After the simulation ends, the terminal sends the recorded operation data to the server. The server uses behavioral analysis tools to analyze the user's actions and evaluate the degree of knowledge retention. The user's past operation logs are input into this process. The evaluation results become the analysis output and are sent back to the terminal.

[0540] Step 8:

[0541] The terminal presents the received evaluation results to the user and administers a quiz using problem-solving tools. The user answers the quiz, and the results are sent to the server. The quiz answer data is generated as input, and the evaluated score is generated as output.

[0542] Step 9:

[0543] Based on the user's learning progress and history, the server uses a content suggestion system to propose the most suitable disaster prevention content for the next learning session through an AI model. The suggested content is displayed to the user via their device. This supports continuous learning.

[0544] Step 10:

[0545] Users can invite other participants to the app using collaborative methods to jointly develop disaster preparedness plans. This function generates an invitation link based on the information entered by the user, and the output allows for direct collaborative editing of the disaster preparedness plan with others. This aims to improve collaborative disaster preparedness awareness.

[0546] (Application Example 1)

[0547] 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."

[0548] In modern urban environments, raising disaster preparedness awareness and establishing effective disaster prevention knowledge are crucial challenges. However, disaster prevention education based on traditional methods often limits the understanding and practical application of the learned content. Furthermore, a lack of coordination throughout the community can prevent the full utilization of response capabilities during crises. Therefore, there is a need for a learning system that allows for the practical acquisition of disaster prevention knowledge, as well as mechanisms that promote coordination and collaborative planning throughout the community.

[0549] 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.

[0550] This invention includes an authentication means for receiving authentication information from a user and authenticating the user based on that information, a presentation means for providing disaster response exercises in a virtual environment through an information device accessible to citizens, and a response means for jointly creating and implementing disaster prevention plans in cooperation with the local community. This enables users to interactively experience disaster prevention simulations and to formulate and implement effective disaster prevention plans in cooperation with the entire community.

[0551] An "authentication method" is a system that receives authentication information from a user, verifies the user based on that information, and performs authentication.

[0552] A "display means" is a device that provides authenticated users with a selection of disaster prevention content and presents learning content according to the user's selection through the screen of an information terminal.

[0553] A "simulation mechanism" is a system that presents a virtual disaster situation and allows the user to perform actions in response to that situation to execute the simulation.

[0554] The "analysis method" is a system that analyzes user actions recorded during the simulation and evaluates user behavior based on those records.

[0555] "Presentation method" refers to a method of providing disaster response exercises in a virtual environment through information devices accessible to citizens.

[0556] "Response measures" refer to methods and processes for collaborating with local communities to jointly create and effectively implement disaster prevention plans.

[0557] This invention is a system designed to enable effective learning and practice of disaster prevention knowledge. The following process is used to realize this system.

[0558] The server first verifies the authentication information received from the user via a secure communication protocol and performs authentication. This is done using an authentication API and a secure protocol (e.g., HTTPS). Once authentication is complete, the server retrieves the user's profile information, selects relevant disaster prevention content from a database (e.g., MySQL or MongoDB), and sends it to the terminal in JSON format or another appropriate format.

[0559] The device displays disaster prevention content through a user interface. This display can utilize touch interfaces and voice guidance, allowing users to experience a virtual disaster simulation based on the displayed content. Here, AR technology (e.g., ARCore) and game engines (e.g., Unity) are used to provide a realistic experience.

[0560] When a user operates a device and runs a simulation, the operation history is sent to a server and analyzed by an analysis engine (e.g., TensorFlow). The analysis results are used to evaluate the user's behavior patterns and suggest future disaster prevention content. Throughout this cycle, users can track their learning progress.

[0561] Furthermore, by using presentation tools, citizens can participate in the creation and simulation of disaster prevention plans involving the entire community from their own smartphones. This allows multiple users to raise disaster prevention awareness together using collaborative tools.

[0562] As a concrete example, if we consider an earthquake simulation in a certain region, users can check safe evacuation routes from their homes in a virtual urban environment and learn what actions they should take during an evacuation.

[0563] An example of a prompt statement generated by the AI ​​is, "What algorithms and data structures should be used to build a user feedback system based on urban disaster simulations?" In this way, the present invention aims to improve efficiency and strengthen collaboration in disaster prevention education.

[0564] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0565] Step 1:

[0566] The user accesses the application through their device. Here, the user enters authentication information (e.g., user ID and password). This information is then transmitted from the device to the server using a secure protocol.

[0567] Step 2:

[0568] The server verifies the received authentication information using an authentication API. If successful, it retrieves data related to the user profile from the database and sends it back to the terminal as a response along with the authentication result. The output response includes initial settings for the user and options for learning content.

[0569] Step 3:

[0570] The terminal receives a response from the server and displays a selection menu of disaster prevention content on the user interface. The user can then select a disaster prevention category of interest from this menu. The user's selection is recorded by the terminal and used for the next simulation.

[0571] Step 4:

[0572] Based on the category selected by the user, the device starts a virtual disaster scenario. During this simulation, the real world is simulated using AR technology, allowing the user to interactively experience the situation. The user's actions are recorded in real time.

[0573] Step 5:

[0574] The device sends user operation data it records to the server. The server uses an analysis engine to analyze the operation data and evaluate the user's behavior and reactions. This engine uses a generative AI model to obtain the analysis results. The output is used to customize the next learning content.

[0575] Step 6:

[0576] Based on the analysis results, the server determines the most suitable disaster prevention content to provide next and sends it to the terminal. The terminal then presents the received new content to the user, facilitating continued learning.

[0577] Step 7:

[0578] Users can participate in creating disaster prevention plans for their own communities using the new disaster prevention content presented. This allows for collaboration with community members through the use of various linking tools.

[0579] 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.

[0580] This invention provides an interactive disaster prevention learning system that combines an emotion engine, designed to allow users to concentrate more effectively and progress through the learning process. Specific embodiments are described below.

[0581] First, the user launches the application from their device and enters their authentication information on the login screen. The server authenticates the user based on the received authentication information and retrieves their profile data. If authentication is successful, a selection of disaster prevention content best suited to the device is displayed. The user selects a disaster prevention category of interest and begins learning.

[0582] During learning, the device utilizes an emotion engine to analyze the user's facial expressions and voice in real time, capturing the user's emotional state. This emotional data is sent to a server, where analysis evaluates how the user's current emotions are affecting the learning process. Based on the emotional analysis, the device dynamically adjusts the difficulty level and content of the learning material. For example, if the user is confused, the device will provide explanations using simpler examples.

[0583] Furthermore, the simulation method provides feedback based on emotional data. When the user is relaxed, more challenging situations can be presented to motivate them. The server provides emotionally responsive feedback and carefully observes the user's reactions. This tailored feedback allows the user to learn more effectively.

[0584] Once a learning phase is complete, users take a quiz through a quiz administration system, and the server analyzes the results. The system also considers the user's emotional state, as determined by the emotion engine, and highlights particularly important items as feedback if retention is low. When suggesting disaster prevention content for the next learning session, the user's emotional motivation is also taken into account.

[0585] As a concrete example, suppose a user selects flood prevention measures and begins learning. When the emotion engine detects anxiety from the user's facial expression, the device displays more basic learning materials incorporating numerous real-world examples, with the aim of alleviating anxiety. As learning progresses and the user calms down, situation-specific simulations are provided, enabling the user to choose appropriate actions during a disaster. In this way, the present invention realizes a flexible learning environment tailored to the user, providing a more meaningful learning experience.

[0586] The following describes the processing flow.

[0587] Step 1:

[0588] The user launches the application from their device and enters their login information. The device then sends the entered information to the server.

[0589] Step 2:

[0590] The server authenticates the user based on the login information it receives. If authentication is successful, the server retrieves the user's profile information and proceeds to the next step.

[0591] Step 3:

[0592] The server selects appropriate disaster prevention content based on the user's profile and sends that information to the device. The device then displays the user the disaster prevention category (e.g., earthquake, flood, typhoon, etc.).

[0593] Step 4:

[0594] The user selects a disaster prevention category of interest. The device begins displaying learning content related to the selected category.

[0595] Step 5:

[0596] As users progress through the learning content, the device activates an emotion engine that analyzes the user's facial expressions and voice in real time.

[0597] Step 6:

[0598] The device sends the analysis results from the emotion engine to the server. The server evaluates the emotional state and sends instructions to the device to adjust the learning difficulty and content based on the results.

[0599] Step 7:

[0600] Based on emotional data, the device adjusts the difficulty level and pace of learning content, providing personalized learning materials. For example, if a user is feeling anxious, it will display gentler explanations or additional hints.

[0601] Step 8:

[0602] During the learning process, the device presents a virtual disaster scenario using simulation tools. The user then selects and responds to the scenario.

[0603] Step 9:

[0604] The user's reactions during the simulation are also detected by the emotion engine. Based on this data, the server analyzes the user's suitability and provides appropriate advice and feedback in real time.

[0605] Step 10:

[0606] After the learning session is complete, the device starts a quiz using the quiz administration method. The user then answers the quiz questions.

[0607] Step 11:

[0608] The server analyzes the quiz results, taking into account user sentiment data, to evaluate the level of knowledge retention. Based on this evaluation, it generates suggestions for the next learning session.

[0609] Step 12:

[0610] Using the proposed method, the server selects the next learning content and develops a learning plan that takes emotional data into consideration to motivate the user. The terminal then presents this plan to the user.

[0611] (Example 2)

[0612] 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."

[0613] In modern society, there is a demand for educational systems that cater to individual learning needs. However, conventional systems have the problem of reduced learning effectiveness because they provide uniform content without considering the user's emotional state. Furthermore, there is a lack of mechanisms to individually evaluate the level of understanding of educational content and suggest the next learning content based on that evaluation. Therefore, there is a need to build a new system that can dynamically analyze the user's emotional state and adjust the learning experience accordingly.

[0614] 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.

[0615] In this invention, the server includes authentication means for receiving authentication information from a user and authenticating the user based on that information; display means for providing the authenticated user with a selection of educational content and displaying learning content according to the user's selection; and emotion processing means for analyzing the emotional state and dynamically adjusting the educational content based on the analysis results. This makes it possible to provide an optimal learning experience tailored to the user's emotional state and improve individual learning efficiency.

[0616] "Authentication means" refers to a function that authenticates a user to the system based on authentication information received from the user.

[0617] "Display means" refers to a function that provides authenticated users with a selection of educational content and displays learning content based on the user's selection.

[0618] "Emotional processing means" refers to a function that analyzes the user's emotional state in real time and dynamically adjusts educational content based on the analysis results.

[0619] A "simulation method" is a function that presents a virtual situation and allows the user to perform actions corresponding to that situation to execute the simulation.

[0620] "Analysis means" refers to a function that records user actions in a simulation and analyzes user behavior based on those records.

[0621] The "suggestion method" is a function that provides feedback to users based on analysis results and suggests the next educational content.

[0622] "Test implementation means" refers to a function that conducts tests to evaluate the degree of knowledge retention based on user actions.

[0623] "Collaboration means" refers to a function that provides collaboration features for users to invite other members and create plans together.

[0624] This invention provides an interactive learning system that combines an emotion processing engine. The purpose of the invention is to enable users to learn more effectively.

[0625] First, the user launches the learning application from their device and logs in by entering their username and password. The device used here is expected to be a personal computer or smartphone equipped with a camera and microphone. The server then receives the authentication information sent from the device and authenticates the user using the authentication method. If authentication is successful, the server retrieves the user's profile data from the database and sends it to the device.

[0626] Next, the authenticated user is presented with a selection of educational content. The user chooses a category of interest from the various available options and begins learning. The device provides the user with the learning content through a display mechanism that shows the selected content. At this time, an emotion processing mechanism is activated, analyzing the user's facial expressions and voice data in real time. This allows the system to understand the user's emotional state and dynamically adjust the educational content based on the results. Furthermore, by utilizing a generative AI model, the system generates emotion-responsive feedback to optimize the learning experience.

[0627] Furthermore, a crucial function is the simulation mechanism. It presents the user with a virtual learning scenario, and the user performs actions corresponding to that scenario. These actions are recorded by the system and later analyzed by the server's analysis mechanism. Based on this, the suggestion mechanism provides the content for the next learning session. At this time, the analysis results from the emotion processing engine are also taken into consideration.

[0628] As a concrete example, suppose a user chooses to learn history and is studying relevant dates and events. If the emotion engine detects that the user is becoming confused during the learning process, the device will provide simpler explanations and visuals to aid understanding. Such adjustments enhance the user's learning effectiveness and allow for smoother progress.

[0629] As an example of a prompt, inputting "What content adjustments should be made next when the user indicates 'confusion'?" into the generating AI model can produce more effective feedback. In this way, the system provides a flexible learning environment tailored to the user, realizing excellent educational support that addresses individual needs.

[0630] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0631] Step 1:

[0632] The user launches the disaster prevention learning application using their device and enters their authentication information on the login screen. The entered authentication information is sent from the device to the server. The server compares the received authentication information with its database and authenticates the user. If successful, the server retrieves the user's profile data and sends it to the device to notify the user that they have been authenticated.

[0633] Step 2:

[0634] The device displays learning content options based on user profile data received from the server. The user selects a category of interest, such as flood prevention or earthquake prevention. This selection is processed on the device, and the selected content information is sent to the server. The server then uses this information to send the necessary learning data to the device.

[0635] Step 3:

[0636] The user begins learning based on the content displayed on the device. At this point, the device's emotion processing function activates, capturing the user's facial expressions and voice data through the camera and microphone. This data is collected in real time and used to evaluate their emotional state. Once the data is analyzed, the emotional state is sent to the server.

[0637] Step 4:

[0638] The server analyzes the emotional data sent from the device and evaluates the user's current emotional state. Based on the emotional state obtained from the analysis, the server sends appropriate feedback and instructions to adjust the content to the device. For example, if the user is confused, the server instructs the device to provide simple examples or more detailed explanations.

[0639] Step 5:

[0640] The device executes instructions received from the server and adjusts the content according to the user's emotional state. Specifically, it may lower the difficulty level of the learning materials or add visual aids. This dynamic adjustment allows users to learn at their own pace and provides a learning environment that is easy to follow.

[0641] Step 6:

[0642] As part of the learning process, the device provides users with simulations using virtual situations. Users can manipulate the given options within the simulation and perform practical responses. The simulation results are recorded by the device, sent to a server, and analyzed.

[0643] Step 7:

[0644] The server analyzes user behavior based on simulation result logs and thoroughly evaluates the effectiveness of learning and areas for improvement. Based on this analysis, it sends suggestions for the next learning session and necessary feedback to the user's device. The user receives this feedback and is then presented with suggestions for the next learning session based on their learning history.

[0645] Through these steps, the system provides a customized educational experience that incorporates the user's emotional state and learning data.

[0646] (Application Example 2)

[0647] 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."

[0648] Current disaster prevention education systems provide uniform educational content without considering the user's emotional state, making it difficult for users to concentrate on learning and hindering effective knowledge retention. Furthermore, there is a lack of real-time feedback based on emotional analysis, highlighting the need for a dynamic learning environment tailored to individual users.

[0649] 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.

[0650] In this invention, the server includes authentication means for receiving and authenticating authentication information from a user; presentation means for providing authenticated users with a selection of disaster prevention information and displaying educational content according to the user's selection; emotion analysis means for analyzing the user's expressions and voice to identify their emotional state; and educational adjustment means for dynamically adjusting the difficulty level and presentation content of the educational material according to the emotional state. This makes it possible to provide an effective and personalized learning experience that is adapted to the user's emotional state.

[0651] "Authentication means" refers to a function that receives authentication information from a user, verifies the user based on that information, and identifies the systems or services that the user can access.

[0652] The "presentation method" refers to a function that provides authenticated users with various options for disaster prevention information and displays appropriate educational content according to the user's selection.

[0653] A "simulation experiment tool" is a function that presents users with a virtual emergency situation and has them perform actions according to that situation, thereby allowing users to conduct a simulated experiment to prepare for realistic scenarios.

[0654] The "analysis means" is a function that analyzes user behavior based on recorded user actions during a simulated experiment and evaluates learning progress and understanding.

[0655] The "suggestion method" is a function that provides feedback to the user based on the analysis results and recommends disaster prevention information necessary for the next learning session.

[0656] "Emotional analysis means" refers to a function that analyzes the user's expressions and voice in real time to understand their emotional state.

[0657] "Educational adjustment tools" are functions that dynamically change the difficulty level and presentation method of learning content based on acquired emotional states, providing an optimal learning experience for each individual user.

[0658] To realize this invention, a system is constructed in which a home robot or individual learning terminal interacts with the user. The authentication means receives login information when the user accesses the terminal and performs authentication on the system. This login information is transmitted to a secure server for verification.

[0659] The server presents authenticated users with a selection of disaster prevention information options, allowing them to choose educational content of interest. During this process, the server uses the sentiment analysis capabilities built into the terminal to analyze the user's expressions and voice in real time. Possible software used includes sentiment analysis libraries. The analyzed sentiment data is sent to the server, where educational adjustment mechanisms dynamically adjust the difficulty level and presentation of the learning content. This allows users to receive an appropriate learning experience tailored to their emotional state.

[0660] The hardware used includes home robots and dedicated terminals equipped with cameras and microphones. For example, when a user receives education on "flood prevention measures," the system detects the user's anxiety using emotion analysis and displays basic information on the terminal to alleviate that anxiety. Then, as the user performs actions appropriate to the situation, a simulation of a virtual emergency is initiated using simulation tools.

[0661] By utilizing a generative AI model, an example of a prompt statement could be, "Explain how a system for providing disaster preparedness education at home should adjust its content based on the user's emotions." In this way, a more effective and personalized learning experience for the user is achieved.

[0662] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0663] Step 1:

[0664] The user accesses the device and enters authentication information on the login screen. The device sends this information to the server. The server authenticates the user based on the received authentication information, and if authentication is successful, retrieves the user's profile data. This process verifies the user's information, allowing them to proceed to the next step. The input is authentication information, and the output is profile data.

[0665] Step 2:

[0666] The server presents authenticated users with a selection of disaster prevention information. Users select the disaster prevention category they are interested in. The terminal displays educational content corresponding to this selection. The input is the user's profile and disaster prevention category selection, and the output is the display of learning content. At this point, the user is ready to begin their individual learning activities.

[0667] Step 3:

[0668] As the user progresses through the learning process, their facial expressions and voice are captured in real time via the camera and microphone on the device. The device uses emotion analysis to analyze the user's emotional state. The resulting emotional data is sent to a server. The input is the user's facial expressions and voice data, and the output is the analyzed emotional data.

[0669] Step 4:

[0670] The server receives emotional data and uses educational adjustment mechanisms to dynamically adjust the current learning content according to the user's emotions. For example, if the user is confused, simpler learning materials will be displayed on the device. The input is emotional data and the current learning content, and the output is the adjusted learning content. This improves the user's learning experience.

[0671] Step 5:

[0672] A hypothetical emergency situation is presented, and the user performs corresponding actions on their terminal. These actions are executed as a simulation using a simulation tool. The user's actions are recorded by the terminal, and this data is sent to a server. The input is the user's actions, and the output is the execution of the simulation and the record of those actions.

[0673] Step 6:

[0674] The server analyzes the operations recorded by the simulation device and provides the user with feedback based on the analysis results. The user's learning progress and understanding are evaluated through the analysis. The input is the operation record, and the output is the analysis results and feedback.

[0675] Step 7:

[0676] The server uses a suggestion mechanism to propose disaster prevention information necessary for the next learning session based on the analysis results. Information tailored to the user's learning curve is displayed on the terminal, enabling more effective preparation for the next learning opportunity. The input is the analysis results, and the output is the suggestions for the next learning session.

[0677] An example of a prompt using a generative AI model is: "Explain how a system for providing disaster preparedness education at home should adjust its content based on the user's emotions."

[0678] 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.

[0679] 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.

[0680] 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.

[0681] [Fourth Embodiment]

[0682] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0683] 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.

[0684] 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).

[0685] 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.

[0686] 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.

[0687] 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).

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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".

[0695] This invention provides an interactive disaster prevention learning system designed to enable users to effectively acquire disaster prevention knowledge and actions. Specific embodiments are described below.

[0696] The user launches the application from their device and enters their personal authentication information on the login screen. The server receives this information and authenticates the user using the appropriate authentication method. If this process is successful, the server selects the most appropriate disaster prevention content based on the user's profile information and sends it to the device.

[0697] The terminal displays a list of disaster prevention categories (e.g., earthquakes, typhoons, floods, etc.) to the user based on information received from the server. The user selects a category of interest and starts interactive learning content related to that category.

[0698] During the learning process, the device displays selected content, and the user experiences a virtual disaster scenario based on that content. The simulation allows the user to choose actions for each situation and receive real-time feedback based on those choices.

[0699] Subsequently, the user takes a quiz based on what they have learned through a quiz administration system. The server evaluates the user's answers and uses the data obtained through analysis to check the level of knowledge retention. The evaluation results are sent to the terminal and presented to the user as feedback.

[0700] The proposed method allows the server to comprehensively analyze the user's learning history and evaluation results to suggest disaster prevention content deemed optimal for the next learning session. This suggestion is then notified to the user via their device.

[0701] In addition, using collaborative methods, users can invite family members and community members to the app to jointly create disaster preparedness plans. This feature is designed to promote the sharing of disaster preparedness awareness and the effective implementation of the plans.

[0702] For example, if a user selects the earthquake preparedness category, the simulation will show a scenario where an earthquake occurs in a home. The user selects which piece of furniture to hide under, and receives real-time feedback on whether their selection is correct. In the subsequent quiz, questions such as "Where is the safest place to be during an earthquake?" are asked, and the user's level of understanding is evaluated.

[0703] In this way, the present invention is a system that enables users to learn practical and in-depth disaster prevention knowledge.

[0704] The following describes the processing flow.

[0705] Step 1:

[0706] The user launches the application from their device and enters their personal authentication information on the login screen.

[0707] Step 2:

[0708] The terminal sends user authentication information to the server, and the server uses that information to verify credentials against a database and authenticate the user.

[0709] Step 3:

[0710] The server retrieves the profile information of the authenticated user and selects the most suitable disaster prevention content based on that information. The server then sends that content information to the terminal.

[0711] Step 4:

[0712] The terminal displays content information received from the server to the user. The user selects a disaster prevention category (earthquake, typhoon, flood, etc.) based on their interests.

[0713] Step 5:

[0714] Interactive learning content related to the selected category is displayed on the device. The user then begins learning based on that content.

[0715] Step 6:

[0716] As the learning process progresses, the device uses simulation tools to present virtual disaster scenarios. Users select actions according to the situation and advance the simulation.

[0717] Step 7:

[0718] The server records the user's choices and their results in real time, and uses analytical tools to evaluate the user's behavior. This evaluation result is immediately fed back to the user through the terminal.

[0719] Step 8:

[0720] After the simulation ends, the device will present the user with a quiz using a quiz administration method. The quiz will be based on what was learned during the simulation.

[0721] Step 9:

[0722] Once the quiz is completed, the server analyzes the user's answers and evaluates their knowledge retention. The evaluation results and feedback are sent to the device.

[0723] Step 10:

[0724] Using the proposed method, the server selects the next disaster prevention content to be learned based on the learning history and evaluation results. This information is then suggested to the user via the terminal.

[0725] Step 11:

[0726] The collaboration mechanism allows users to invite other members to the app and provides an option for collaboratively creating disaster preparedness plans. The generated plans are shared among members via their devices.

[0727] (Example 1)

[0728] 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".

[0729] Conventional disaster prevention learning systems have faced challenges such as difficulty in providing individually optimized disaster prevention content to users and a lack of effective means for providing feedback on learning results. Furthermore, they lacked sufficient functionality for collaborative disaster prevention planning, making it difficult for users to easily work together.

[0730] 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.

[0731] In this invention, the server includes an information authentication means that receives authentication information from a user and authenticates the user based on that authentication information; a content display means that provides the authenticated user with options to select disaster prevention content according to the user's attributes and displays content according to the user's selection; and a virtual situation presentation means that presents a virtual disaster situation and allows the user to perform operations according to that situation to execute a simulation. This enables the provision of disaster prevention content optimized for the user, highly effective feedback for learning, and cooperation in jointly formulating a disaster prevention plan.

[0732] An "information authentication method" is a function that verifies the legitimacy of a user based on the authentication information received from that user.

[0733] The "content display means" is a function that provides authenticated users with the most suitable disaster prevention learning content as a selection based on their attribute information, and displays the learning content based on that selection.

[0734] A "virtual situation presentation means" is a function that presents a virtual disaster scenario to the user and accepts user actions or executes simulations according to that situation.

[0735] "Behavioral analysis means" refers to a function that analyzes user behavior based on user actions recorded in simulations.

[0736] The "content suggestion method" is a function that suggests the most suitable disaster prevention content for the next learning session based on the user's learning history and operation results.

[0737] A "problem-solving tool" is a function that evaluates the degree of knowledge retention based on user actions and provides preliminary learning such as quizzes and tests based on that evaluation.

[0738] "Collaboration provision means" refers to a function that provides the necessary collaborative features for users to invite other participants and create disaster prevention plans together.

[0739] This invention provides an interactive learning system that enables users to effectively acquire disaster prevention knowledge. This system is primarily operated by a server, terminals, and the user.

[0740] The server first receives authentication information entered by the user and uses this information to verify the user's legitimacy using an information authentication method. If this authentication is successful, the server uses a generative AI model to consider the user's attribute information and past learning history to select the most suitable disaster prevention content. The server then sends this selected content to the terminal.

[0741] The terminal uses a content display mechanism to show the user a list of disaster prevention categories (e.g., earthquakes, floods, etc.) based on the information received from the server. When the user selects a category of interest, a simulation related to that category is started using a virtual situation presentation mechanism. This allows the user to experience operations in a virtual disaster situation and receive real-time feedback.

[0742] Once the simulation is complete, the user automatically participates in a quiz through a problem-solving tool. The server uses behavioral analysis tools to evaluate the user's knowledge retention based on their quiz answers. The evaluation results are then sent back to the terminal and presented to the user as feedback.

[0743] Furthermore, the server uses a content suggestion mechanism to consider the user's learning history and knowledge retention level, and suggests disaster prevention content for the next learning session. Users can also invite other participants to the application through a collaborative provision mechanism and jointly develop disaster prevention plans.

[0744] To give a specific example, if a user selects the earthquake preparedness category, the device will display a scenario in which an earthquake occurs in the home, and the user will experience choosing a safe place and taking evacuation action. At that time, it is expected that a prompt message such as "Please tell me the best way to act safely when an earthquake occurs" will be entered into the system.

[0745] In this way, the present invention functions as a system that provides users with practical and effective opportunities for disaster prevention learning.

[0746] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0747] Step 1:

[0748] The user launches the disaster prevention learning application from their device and enters their personal authentication information on the login screen. This information includes a user ID and password. The device then sends this authentication information to the server. The output of this server uses the authentication information as a search key in the database.

[0749] Step 2:

[0750] The server searches the database for user information based on the received authentication information and verifies the user's legitimacy using an information authentication method. The data processing performed in this process involves comparing the input information with the records in the database. If user authentication is successful based on the results, the authentication status is output to the terminal.

[0751] Step 3:

[0752] The server retrieves the authenticated user's profile information and past learning history, and uses a generative AI model to select the most suitable disaster prevention content. User attributes and history data are used as input, and selected content information is generated as output. This process involves the selection of personalized content based on user attributes.

[0753] Step 4:

[0754] The server sends the selected disaster prevention content to the terminal. Based on the received content, the terminal uses a content display device to show the user a list of disaster prevention categories. It receives content data as input and displays a category list to the user as output.

[0755] Step 5:

[0756] The user selects a disaster prevention category of interest. The terminal sends the selection to the server, and a simulation of that category is executed through a virtual situation presentation system. The user's selection is used as input, and the start of the simulation is triggered as output.

[0757] Step 6:

[0758] The user performs actions in response to a given virtual disaster scenario within the simulation. The terminal records these actions in real time. It receives user instructions as input and generates an operation log as output. Appropriate feedback is provided to the user in real time.

[0759] Step 7:

[0760] After the simulation ends, the terminal sends the recorded operation data to the server. The server uses behavioral analysis tools to analyze the user's actions and evaluate the degree of knowledge retention. The user's past operation logs are input into this process. The evaluation results become the analysis output and are sent back to the terminal.

[0761] Step 8:

[0762] The terminal presents the received evaluation results to the user and administers a quiz using problem-solving tools. The user answers the quiz, and the results are sent to the server. The quiz answer data is generated as input, and the evaluated score is generated as output.

[0763] Step 9:

[0764] Based on the user's learning progress and history, the server uses a content suggestion system to propose the most suitable disaster prevention content for the next learning session through an AI model. The suggested content is displayed to the user via their device. This supports continuous learning.

[0765] Step 10:

[0766] Users can invite other participants to the app using collaborative methods to jointly develop disaster preparedness plans. This function generates an invitation link based on the information entered by the user, and the output allows for direct collaborative editing of the disaster preparedness plan with others. This aims to improve collaborative disaster preparedness awareness.

[0767] (Application Example 1)

[0768] 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".

[0769] In modern urban environments, raising disaster preparedness awareness and establishing effective disaster prevention knowledge are crucial challenges. However, disaster prevention education based on traditional methods often limits the understanding and practical application of the learned content. Furthermore, a lack of coordination throughout the community can prevent the full utilization of response capabilities during crises. Therefore, there is a need for a learning system that allows for the practical acquisition of disaster prevention knowledge, as well as mechanisms that promote coordination and collaborative planning throughout the community.

[0770] 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.

[0771] This invention includes an authentication means for receiving authentication information from a user and authenticating the user based on that information, a presentation means for providing disaster response exercises in a virtual environment through an information device accessible to citizens, and a response means for jointly creating and implementing disaster prevention plans in cooperation with the local community. This enables users to interactively experience disaster prevention simulations and to formulate and implement effective disaster prevention plans in cooperation with the entire community.

[0772] An "authentication method" is a system that receives authentication information from a user, verifies the user based on that information, and performs authentication.

[0773] A "display means" is a device that provides authenticated users with a selection of disaster prevention content and presents learning content according to the user's selection through the screen of an information terminal.

[0774] A "simulation mechanism" is a system that presents a virtual disaster situation and allows the user to perform actions in response to that situation to execute the simulation.

[0775] The "analysis method" is a system that analyzes user actions recorded during the simulation and evaluates user behavior based on those records.

[0776] "Presentation method" refers to a method of providing disaster response exercises in a virtual environment through information devices accessible to citizens.

[0777] "Response measures" refer to methods and processes for collaborating with local communities to jointly create and effectively implement disaster prevention plans.

[0778] This invention is a system designed to enable effective learning and practice of disaster prevention knowledge. The following process is used to realize this system.

[0779] The server first verifies the authentication information received from the user via a secure communication protocol and performs authentication. This is done using an authentication API and a secure protocol (e.g., HTTPS). Once authentication is complete, the server retrieves the user's profile information, selects relevant disaster prevention content from a database (e.g., MySQL or MongoDB), and sends it to the terminal in JSON format or another appropriate format.

[0780] The device displays disaster prevention content through a user interface. This display can utilize touch interfaces and voice guidance, allowing users to experience a virtual disaster simulation based on the displayed content. Here, AR technology (e.g., ARCore) and game engines (e.g., Unity) are used to provide a realistic experience.

[0781] When a user operates a device and runs a simulation, the operation history is sent to a server and analyzed by an analysis engine (e.g., TensorFlow). The analysis results are used to evaluate the user's behavior patterns and suggest future disaster prevention content. Throughout this cycle, users can track their learning progress.

[0782] Furthermore, by using presentation tools, citizens can participate in the creation and simulation of disaster prevention plans involving the entire community from their own smartphones. This allows multiple users to raise disaster prevention awareness together using collaborative tools.

[0783] As a concrete example, if we consider an earthquake simulation in a certain region, users can check safe evacuation routes from their homes in a virtual urban environment and learn what actions they should take during an evacuation.

[0784] An example of a prompt statement generated by the AI ​​is, "What algorithms and data structures should be used to build a user feedback system based on urban disaster simulations?" In this way, the present invention aims to improve efficiency and strengthen collaboration in disaster prevention education.

[0785] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0786] Step 1:

[0787] The user accesses the application through their device. Here, the user enters authentication information (e.g., user ID and password). This information is then transmitted from the device to the server using a secure protocol.

[0788] Step 2:

[0789] The server verifies the received authentication information using an authentication API. If successful, it retrieves data related to the user profile from the database and sends it back to the terminal as a response along with the authentication result. The output response includes initial settings for the user and options for learning content.

[0790] Step 3:

[0791] The terminal receives a response from the server and displays a selection menu of disaster prevention content on the user interface. The user can then select a disaster prevention category of interest from this menu. The user's selection is recorded by the terminal and used for the next simulation.

[0792] Step 4:

[0793] Based on the category selected by the user, the device starts a virtual disaster scenario. During this simulation, the real world is simulated using AR technology, allowing the user to interactively experience the situation. The user's actions are recorded in real time.

[0794] Step 5:

[0795] The device sends user operation data it records to the server. The server uses an analysis engine to analyze the operation data and evaluate the user's behavior and reactions. This engine uses a generative AI model to obtain the analysis results. The output is used to customize the next learning content.

[0796] Step 6:

[0797] Based on the analysis results, the server determines the most suitable disaster prevention content to provide next and sends it to the terminal. The terminal then presents the received new content to the user, facilitating continued learning.

[0798] Step 7:

[0799] Users can participate in creating disaster prevention plans for their own communities using the new disaster prevention content presented. This allows for collaboration with community members through the use of various linking tools.

[0800] 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.

[0801] This invention provides an interactive disaster prevention learning system that combines an emotion engine, designed to allow users to concentrate more effectively and progress through the learning process. Specific embodiments are described below.

[0802] First, the user launches the application from their device and enters their authentication information on the login screen. The server authenticates the user based on the received authentication information and retrieves their profile data. If authentication is successful, a selection of disaster prevention content best suited to the device is displayed. The user selects a disaster prevention category of interest and begins learning.

[0803] During learning, the device utilizes an emotion engine to analyze the user's facial expressions and voice in real time, capturing the user's emotional state. This emotional data is sent to a server, where analysis evaluates how the user's current emotions are affecting the learning process. Based on the emotional analysis, the device dynamically adjusts the difficulty level and content of the learning material. For example, if the user is confused, the device will provide explanations using simpler examples.

[0804] Furthermore, the simulation method provides feedback based on emotional data. When the user is relaxed, more challenging situations can be presented to motivate them. The server provides emotionally responsive feedback and carefully observes the user's reactions. This tailored feedback allows the user to learn more effectively.

[0805] Once a learning phase is complete, users take a quiz through a quiz administration system, and the server analyzes the results. The system also considers the user's emotional state, as determined by the emotion engine, and highlights particularly important items as feedback if retention is low. When suggesting disaster prevention content for the next learning session, the user's emotional motivation is also taken into account.

[0806] As a concrete example, suppose a user selects flood prevention measures and begins learning. When the emotion engine detects anxiety from the user's facial expression, the device displays more basic learning materials incorporating numerous real-world examples, with the aim of alleviating anxiety. As learning progresses and the user calms down, situation-specific simulations are provided, enabling the user to choose appropriate actions during a disaster. In this way, the present invention realizes a flexible learning environment tailored to the user, providing a more meaningful learning experience.

[0807] The following describes the processing flow.

[0808] Step 1:

[0809] The user launches the application from their device and enters their login information. The device then sends the entered information to the server.

[0810] Step 2:

[0811] The server authenticates the user based on the login information it receives. If authentication is successful, the server retrieves the user's profile information and proceeds to the next step.

[0812] Step 3:

[0813] The server selects appropriate disaster prevention content based on the user's profile and sends that information to the device. The device then displays the user the disaster prevention category (e.g., earthquake, flood, typhoon, etc.).

[0814] Step 4:

[0815] The user selects a disaster prevention category of interest. The device begins displaying learning content related to the selected category.

[0816] Step 5:

[0817] As users progress through the learning content, the device activates an emotion engine that analyzes the user's facial expressions and voice in real time.

[0818] Step 6:

[0819] The device sends the analysis results from the emotion engine to the server. The server evaluates the emotional state and sends instructions to the device to adjust the learning difficulty and content based on the results.

[0820] Step 7:

[0821] Based on emotional data, the device adjusts the difficulty level and pace of learning content, providing personalized learning materials. For example, if a user is feeling anxious, it will display gentler explanations or additional hints.

[0822] Step 8:

[0823] During the learning process, the device presents a virtual disaster scenario using simulation tools. The user then selects and responds to the scenario.

[0824] Step 9:

[0825] The user's reactions during the simulation are also detected by the emotion engine. Based on this data, the server analyzes the user's suitability and provides appropriate advice and feedback in real time.

[0826] Step 10:

[0827] After the learning session is complete, the device starts a quiz using the quiz administration method. The user then answers the quiz questions.

[0828] Step 11:

[0829] The server analyzes the quiz results, taking into account user sentiment data, to evaluate the level of knowledge retention. Based on this evaluation, it generates suggestions for the next learning session.

[0830] Step 12:

[0831] Using the proposed method, the server selects the next learning content and develops a learning plan that takes emotional data into consideration to motivate the user. The terminal then presents this plan to the user.

[0832] (Example 2)

[0833] 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".

[0834] In modern society, there is a demand for educational systems that cater to individual learning needs. However, conventional systems have the problem of reduced learning effectiveness because they provide uniform content without considering the user's emotional state. Furthermore, there is a lack of mechanisms to individually evaluate the level of understanding of educational content and suggest the next learning content based on that evaluation. Therefore, there is a need to build a new system that can dynamically analyze the user's emotional state and adjust the learning experience accordingly.

[0835] 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.

[0836] In this invention, the server includes authentication means for receiving authentication information from a user and authenticating the user based on that information; display means for providing the authenticated user with a selection of educational content and displaying learning content according to the user's selection; and emotion processing means for analyzing the emotional state and dynamically adjusting the educational content based on the analysis results. This makes it possible to provide an optimal learning experience tailored to the user's emotional state and improve individual learning efficiency.

[0837] "Authentication means" refers to a function that authenticates a user to the system based on authentication information received from the user.

[0838] "Display means" refers to a function that provides authenticated users with a selection of educational content and displays learning content based on the user's selection.

[0839] "Emotional processing means" refers to a function that analyzes the user's emotional state in real time and dynamically adjusts educational content based on the analysis results.

[0840] A "simulation method" is a function that presents a virtual situation and allows the user to perform actions corresponding to that situation to execute the simulation.

[0841] "Analysis means" refers to a function that records user actions in a simulation and analyzes user behavior based on those records.

[0842] The "suggestion method" is a function that provides feedback to users based on analysis results and suggests the next educational content.

[0843] "Test implementation means" refers to a function that conducts tests to evaluate the degree of knowledge retention based on user actions.

[0844] "Collaboration means" refers to a function that provides collaboration features for users to invite other members and create plans together.

[0845] This invention provides an interactive learning system that combines an emotion processing engine. The purpose of the invention is to enable users to learn more effectively.

[0846] First, the user launches the learning application from their device and logs in by entering their username and password. The device used here is expected to be a personal computer or smartphone equipped with a camera and microphone. The server then receives the authentication information sent from the device and authenticates the user using the authentication method. If authentication is successful, the server retrieves the user's profile data from the database and sends it to the device.

[0847] Next, the authenticated user is presented with a selection of educational content. The user chooses a category of interest from the various available options and begins learning. The device provides the user with the learning content through a display mechanism that shows the selected content. At this time, an emotion processing mechanism is activated, analyzing the user's facial expressions and voice data in real time. This allows the system to understand the user's emotional state and dynamically adjust the educational content based on the results. Furthermore, by utilizing a generative AI model, the system generates emotion-responsive feedback to optimize the learning experience.

[0848] Furthermore, a crucial function is the simulation mechanism. It presents the user with a virtual learning scenario, and the user performs actions corresponding to that scenario. These actions are recorded by the system and later analyzed by the server's analysis mechanism. Based on this, the suggestion mechanism provides the content for the next learning session. At this time, the analysis results from the emotion processing engine are also taken into consideration.

[0849] As a concrete example, suppose a user chooses to learn history and is studying relevant dates and events. If the emotion engine detects that the user is becoming confused during the learning process, the device will provide simpler explanations and visuals to aid understanding. Such adjustments enhance the user's learning effectiveness and allow for smoother progress.

[0850] As an example of a prompt, inputting "What content adjustments should be made next when the user indicates 'confusion'?" into the generating AI model can produce more effective feedback. In this way, the system provides a flexible learning environment tailored to the user, realizing excellent educational support that addresses individual needs.

[0851] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0852] Step 1:

[0853] The user launches the disaster prevention learning application using their device and enters their authentication information on the login screen. The entered authentication information is sent from the device to the server. The server compares the received authentication information with its database and authenticates the user. If successful, the server retrieves the user's profile data and sends it to the device to notify the user that they have been authenticated.

[0854] Step 2:

[0855] The device displays learning content options based on user profile data received from the server. The user selects a category of interest, such as flood prevention or earthquake prevention. This selection is processed on the device, and the selected content information is sent to the server. The server then uses this information to send the necessary learning data to the device.

[0856] Step 3:

[0857] The user begins learning based on the content displayed on the device. At this point, the device's emotion processing function activates, capturing the user's facial expressions and voice data through the camera and microphone. This data is collected in real time and used to evaluate their emotional state. Once the data is analyzed, the emotional state is sent to the server.

[0858] Step 4:

[0859] The server analyzes the emotional data sent from the device and evaluates the user's current emotional state. Based on the emotional state obtained from the analysis, the server sends appropriate feedback and instructions to adjust the content to the device. For example, if the user is confused, the server instructs the device to provide simple examples or more detailed explanations.

[0860] Step 5:

[0861] The device executes instructions received from the server and adjusts the content according to the user's emotional state. Specifically, it may lower the difficulty level of the learning materials or add visual aids. This dynamic adjustment allows users to learn at their own pace and provides a learning environment that is easy to follow.

[0862] Step 6:

[0863] As part of the learning process, the device provides users with simulations using virtual situations. Users can manipulate the given options within the simulation and perform practical responses. The simulation results are recorded by the device, sent to a server, and analyzed.

[0864] Step 7:

[0865] The server analyzes user behavior based on simulation result logs and thoroughly evaluates the effectiveness of learning and areas for improvement. Based on this analysis, it sends suggestions for the next learning session and necessary feedback to the user's device. The user receives this feedback and is then presented with suggestions for the next learning session based on their learning history.

[0866] Through these steps, the system provides a customized educational experience that incorporates the user's emotional state and learning data.

[0867] (Application Example 2)

[0868] 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".

[0869] Current disaster prevention education systems provide uniform educational content without considering the user's emotional state, making it difficult for users to concentrate on learning and hindering effective knowledge retention. Furthermore, there is a lack of real-time feedback based on emotional analysis, highlighting the need for a dynamic learning environment tailored to individual users.

[0870] 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.

[0871] In this invention, the server includes authentication means for receiving and authenticating authentication information from a user; presentation means for providing authenticated users with a selection of disaster prevention information and displaying educational content according to the user's selection; emotion analysis means for analyzing the user's expressions and voice to identify their emotional state; and educational adjustment means for dynamically adjusting the difficulty level and presentation content of the educational material according to the emotional state. This makes it possible to provide an effective and personalized learning experience that is adapted to the user's emotional state.

[0872] "Authentication means" refers to a function that receives authentication information from a user, verifies the user based on that information, and identifies the systems or services that the user can access.

[0873] The "presentation method" refers to a function that provides authenticated users with various options for disaster prevention information and displays appropriate educational content according to the user's selection.

[0874] A "simulation experiment tool" is a function that presents users with a virtual emergency situation and has them perform actions according to that situation, thereby allowing users to conduct a simulated experiment to prepare for realistic scenarios.

[0875] The "analysis means" is a function that analyzes user behavior based on recorded user actions during a simulated experiment and evaluates learning progress and understanding.

[0876] The "suggestion method" is a function that provides feedback to the user based on the analysis results and recommends disaster prevention information necessary for the next learning session.

[0877] "Emotional analysis means" refers to a function that analyzes the user's expressions and voice in real time to understand their emotional state.

[0878] "Educational adjustment tools" are functions that dynamically change the difficulty level and presentation method of learning content based on acquired emotional states, providing an optimal learning experience for each individual user.

[0879] To realize this invention, a system is constructed in which a home robot or individual learning terminal interacts with the user. The authentication means receives login information when the user accesses the terminal and performs authentication on the system. This login information is transmitted to a secure server for verification.

[0880] The server presents authenticated users with a selection of disaster prevention information options, allowing them to choose educational content of interest. During this process, the server uses the sentiment analysis capabilities built into the terminal to analyze the user's expressions and voice in real time. Possible software used includes sentiment analysis libraries. The analyzed sentiment data is sent to the server, where educational adjustment mechanisms dynamically adjust the difficulty level and presentation of the learning content. This allows users to receive an appropriate learning experience tailored to their emotional state.

[0881] The hardware used includes home robots and dedicated terminals equipped with cameras and microphones. For example, when a user receives education on "flood prevention measures," the system detects the user's anxiety using emotion analysis and displays basic information on the terminal to alleviate that anxiety. Then, as the user performs actions appropriate to the situation, a simulation of a virtual emergency is initiated using simulation tools.

[0882] By utilizing a generative AI model, an example of a prompt statement could be, "Explain how a system for providing disaster preparedness education at home should adjust its content based on the user's emotions." In this way, a more effective and personalized learning experience for the user is achieved.

[0883] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0884] Step 1:

[0885] The user accesses the device and enters authentication information on the login screen. The device sends this information to the server. The server authenticates the user based on the received authentication information, and if authentication is successful, retrieves the user's profile data. This process verifies the user's information, allowing them to proceed to the next step. The input is authentication information, and the output is profile data.

[0886] Step 2:

[0887] The server presents authenticated users with a selection of disaster prevention information. Users select the disaster prevention category they are interested in. The terminal displays educational content corresponding to this selection. The input is the user's profile and disaster prevention category selection, and the output is the display of learning content. At this point, the user is ready to begin their individual learning activities.

[0888] Step 3:

[0889] As the user progresses through the learning process, their facial expressions and voice are captured in real time via the camera and microphone on the device. The device uses emotion analysis to analyze the user's emotional state. The resulting emotional data is sent to a server. The input is the user's facial expressions and voice data, and the output is the analyzed emotional data.

[0890] Step 4:

[0891] The server receives emotional data and uses educational adjustment mechanisms to dynamically adjust the current learning content according to the user's emotions. For example, if the user is confused, simpler learning materials will be displayed on the device. The input is emotional data and the current learning content, and the output is the adjusted learning content. This improves the user's learning experience.

[0892] Step 5:

[0893] A hypothetical emergency situation is presented, and the user performs corresponding actions on their terminal. These actions are executed as a simulation using a simulation tool. The user's actions are recorded by the terminal, and this data is sent to a server. The input is the user's actions, and the output is the execution of the simulation and the record of those actions.

[0894] Step 6:

[0895] The server analyzes the operations recorded by the simulation device and provides the user with feedback based on the analysis results. The user's learning progress and understanding are evaluated through the analysis. The input is the operation record, and the output is the analysis results and feedback.

[0896] Step 7:

[0897] The server uses a suggestion mechanism to propose disaster prevention information necessary for the next learning session based on the analysis results. Information tailored to the user's learning curve is displayed on the terminal, enabling more effective preparation for the next learning opportunity. The input is the analysis results, and the output is the suggestions for the next learning session.

[0898] An example of a prompt using a generative AI model is: "Explain how a system for providing disaster preparedness education at home should adjust its content based on the user's emotions."

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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."

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0920] The following is further disclosed regarding the embodiments described above.

[0921] (Claim 1)

[0922] An authentication method that receives authentication information from the user and authenticates the user based on that authentication information,

[0923] A display means that provides authenticated users with a selection of disaster prevention content and displays learning content according to the user's selection,

[0924] A simulation method that presents a virtual disaster situation and allows the user to perform actions according to that situation to run the simulation,

[0925] An analytical method that records user actions in a simulation and analyzes user behavior based on those records,

[0926] A system that provides feedback on analysis results to users and includes a suggestion mechanism to propose future disaster prevention content.

[0927] (Claim 2)

[0928] The system according to claim 1, further comprising a quiz implementation means for conducting a quiz to evaluate the degree of knowledge retention based on user operations.

[0929] (Claim 3)

[0930] The system according to claim 1, further comprising a means for providing a collaborative function that allows users to invite other members and jointly create a disaster prevention plan.

[0931] "Example 1"

[0932] (Claim 1)

[0933] An information authentication method that receives authentication information from the user and authenticates the user based on that authentication information,

[0934] A content display means that provides authenticated users with options to select disaster prevention measures according to the user's attributes, and displays the content according to the user's selection,

[0935] A virtual situation presentation means that presents a virtual disaster situation and allows the user to perform actions according to that situation to run a simulation,

[0936] A behavioral analysis method that records user actions in a simulation and analyzes user behavior based on that record,

[0937] A system that reports analysis results to users and includes a content suggestion mechanism to propose future disaster prevention measures.

[0938] (Claim 2)

[0939] The system according to claim 1, further comprising a problem-solving means for performing problem-solving that evaluates the degree of knowledge retention based on user operations.

[0940] (Claim 3)

[0941] The system according to claim 1, further comprising a means for providing a collaborative function that allows users to invite other participants and jointly create a disaster prevention plan.

[0942] "Application Example 1"

[0943] (Claim 1)

[0944] An authentication method that receives authentication information from the user and authenticates the user based on that authentication information,

[0945] A display means that provides authenticated users with a selection of disaster prevention content and displays learning content according to the user's selection,

[0946] A simulation method that presents a virtual disaster situation and allows the user to perform actions according to that situation to run the simulation,

[0947] An analytical method that records user actions in a simulation and analyzes user behavior based on those records,

[0948] A means of providing disaster response exercises in a virtual environment through information devices accessible to citizens,

[0949] A system that includes measures for collaboratively creating and implementing disaster prevention plans in cooperation with local communities.

[0950] (Claim 2)

[0951] The system according to claim 1, further comprising a quiz implementation means for conducting a quiz to evaluate the degree of knowledge retention based on user operations.

[0952] (Claim 3)

[0953] The system according to claim 1, further comprising a means for providing a collaborative function that allows users to invite other members and jointly create a disaster prevention plan.

[0954] "Example 2 of combining an emotion engine"

[0955] (Claim 1)

[0956] An authentication method that receives authentication information from the user and authenticates the user based on that authentication information,

[0957] A display means that provides authenticated users with a selection of educational content and displays learning content according to the user's selection,

[0958] An emotion processing mechanism that analyzes emotional states and dynamically adjusts educational content based on the analysis results,

[0959] A simulation method that presents a virtual situation and allows the user to perform actions corresponding to that situation to execute the simulation,

[0960] An analytical method that records user actions in a simulation and analyzes user behavior based on those records,

[0961] A system that provides feedback on analysis results to users and includes a suggestion mechanism to propose future educational content.

[0962] (Claim 2)

[0963] The system according to claim 1, further comprising a means for conducting a test to evaluate the degree of knowledge retention based on user operations.

[0964] (Claim 3)

[0965] The system according to claim 1, further comprising a means for providing collaboration functionality for users to invite other members and collaboratively create plans.

[0966] "Application example 2 when combining with an emotional engine"

[0967] (Claim 1)

[0968] An authentication method that receives authentication information from the user and authenticates the user based on that authentication information,

[0969] A presentation method that provides authenticated users with options for disaster prevention information and displays educational content according to the user's selection,

[0970] A simulation experiment method that presents a virtual emergency situation and allows the user to perform actions according to that situation to execute a simulated experiment,

[0971] An analytical method for recording user actions during a simulated experiment and analyzing user behavior based on those records,

[0972] A method for providing feedback on analysis results to users and suggesting future disaster prevention information,

[0973] An emotion analysis means that analyzes the user's expressions and voice to identify their emotional state,

[0974] A system that includes educational adjustment mechanisms to dynamically adjust the difficulty level and content of educational materials according to the emotional state of the user.

[0975] (Claim 2)

[0976] The system according to claim 1, further comprising task implementation means for conducting tasks that evaluate the degree of knowledge retention based on user operations.

[0977] (Claim 3)

[0978] The system according to claim 1, further comprising a cooperation means that provides a collaborative function for a user to invite other members and jointly create a safety plan. [Explanation of Symbols]

[0979] 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 authentication method that receives authentication information from the user and authenticates the user based on that authentication information, A display means that provides authenticated users with a selection of disaster prevention content and displays learning content according to the user's selection, A simulation method that presents a virtual disaster situation and allows the user to perform actions according to that situation to run the simulation, An analytical method that records user actions in a simulation and analyzes user behavior based on those records, A means of providing disaster response exercises in a virtual environment through information devices accessible to citizens, A system that includes measures for collaboratively creating and implementing disaster prevention plans in cooperation with local communities.

2. The system according to claim 1, further comprising a quiz implementation means for conducting a quiz to evaluate the degree of knowledge retention based on user operations.

3. The system according to claim 1, further comprising a means for providing a collaborative function that allows users to invite other members and jointly create a disaster prevention plan.

Images