system

Abstract

The system according to this embodiment aims to provide realistic disaster prevention simulations and conduct training on appropriate evacuation actions. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a training unit, and an evaluation unit. The reception unit inputs information to start a disaster prevention simulation for a designated area. The generation unit analyzes the information input by the reception unit and generates a realistic disaster prevention simulation on the metaverse. The training unit conducts evacuation action training based on the simulation generated by the generation unit. The evaluation unit evaluates the evacuation action performed by the training unit and provides feedback.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide a realistic disaster prevention simulation, and there is a problem that training for appropriate evacuation behavior in an actual disaster is insufficient.

[0005] The system according to the embodiment aims to provide a realistic disaster prevention simulation and conduct training for appropriate evacuation behavior.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a training unit, and an evaluation unit. The reception unit inputs information to start a disaster prevention simulation for a designated area. The generation unit analyzes the information input by the reception unit and generates a realistic disaster prevention simulation on the metaverse. The training unit conducts evacuation action training based on the simulation generated by the generation unit. The evaluation unit evaluates the evacuation action performed by the training unit and provides feedback. [Effects of the Invention]

[0007] The system according to this embodiment can provide realistic disaster prevention simulations and conduct training on appropriate evacuation actions. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The disaster prevention simulation system according to an embodiment of the present invention is a system that provides a realistic disaster prevention simulation on the metaverse using a generative AI. This disaster prevention simulation system takes input information to start a disaster prevention simulation for a designated area, and the generative AI analyzes the input information to generate a realistic disaster prevention simulation on the metaverse. Based on the generated simulation, the user trains on evacuation actions on the metaverse. This system is expected to be widely adopted by various organizations such as local governments, companies, and schools. Using modern technology, it aims to prevent isolation and panic, help people take calm and appropriate actions, and improve the overall disaster prevention capabilities of the region. For example, the user inputs information to start a disaster prevention simulation for a designated area. For example, information such as earthquake intensity, liquefaction occurrence, and fire location is input. This information is input to the generative AI. Next, the generative AI analyzes the input information and generates a realistic disaster prevention simulation on the metaverse. The generative AI reproduces natural disasters such as earthquake intensity, liquefaction, and fire based on map data and disaster data of the designated area. For example, it reproduces how buildings shake in accordance with the earthquake intensity and how the ground sinks due to liquefaction. Based on the generated disaster simulations, users train in evacuation procedures within the metaverse. For example, they train on how to evacuate in the event of an earthquake or how to deal with a fire. By experiencing realistic disaster scenarios within the metaverse, users learn appropriate evacuation actions. This system enables realistic experiences that were difficult to achieve with conventional disaster training. For example, even in areas where field exercises are difficult, or for people with insufficient actual disaster experience, they can learn appropriate evacuation actions through realistic disaster simulations within the metaverse. This is expected to improve the overall disaster preparedness of the region, build safer communities, and enhance preparedness for future disasters. Thus, the disaster simulation system can realistically reproduce disaster simulations for designated areas and conduct evacuation action training and evaluation.

[0029] The disaster prevention simulation system according to the embodiment comprises a reception unit, a generation unit, a training unit, and an evaluation unit. The reception unit inputs information for starting a disaster prevention simulation for a designated area. This information includes, but is not limited to, earthquake intensity, liquefaction occurrence, and fire locations. For example, the reception unit can receive input from a user for starting a disaster prevention simulation for a designated area. The generation unit uses a generation AI to analyze the information input by the reception unit and generates a realistic disaster prevention simulation on the metaverse. For example, the generation unit reproduces natural disasters such as earthquake intensity, liquefaction, and fires based on map data and disaster data for the designated area. For example, the generation unit reproduces how buildings shake in response to earthquake intensity and how the ground sinks due to liquefaction. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit inputs map data and disaster data for a specified area into the generation AI, and has the generation AI perform the reproduction of natural disasters such as earthquake intensity, liquefaction, and fire. The training unit conducts evacuation training based on the simulations generated by the generation unit. The training unit conducts training on, for example, how to evacuate in the event of an earthquake, or how to deal with a fire. For example, the training unit conducts training on checking evacuation routes and heading to evacuation sites in the event of an earthquake. The training unit can also learn how to use a fire extinguisher and train on how to evacuate from a fire scene in the event of a fire. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit inputs the simulations generated by the generation unit into the AI, and has the AI ​​perform evacuation training. The evaluation unit evaluates the evacuation actions performed by the training unit and provides feedback. For example, the evaluation unit evaluates the accuracy and speed of the evacuation actions and points out areas for improvement. For example, the evaluation unit reports the evaluation results of the evacuation actions and points out areas for improvement.Furthermore, the evaluation unit can adjust the content of the next training session based on the evaluation results of the evacuation behavior. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the evacuation behavior performed by the training unit into the AI ​​and have the AI ​​perform the evaluation and provide feedback. As a result, the disaster prevention simulation system according to the embodiment can realistically reproduce disaster prevention simulations in a designated area and conduct training and evaluation of evacuation behavior.

[0030] The reception unit inputs information to start a disaster prevention simulation for a designated area. This information includes, but is not limited to, earthquake intensity, liquefaction occurrence, and fire locations. For example, the reception unit can receive input from users to start a disaster prevention simulation for a designated area. Specifically, users can select map data for the area they want to simulate through a dedicated interface and input detailed information such as earthquake intensity, time of occurrence, liquefaction locations, and fire locations. This allows the reception unit to perform the initial simulation settings based on the conditions specified by the user. Furthermore, the reception unit can automatically acquire supplementary information to improve the accuracy of the simulation by referring to past disaster data and weather data. For example, it can evaluate the probability of earthquake occurrence and liquefaction risk in the designated area based on past earthquake data and publicly available information from the Japan Meteorological Agency, and reflect this in the simulation. This allows the reception unit to integrate the information entered by the user with the supplementary information and prepare to start a more realistic and accurate disaster prevention simulation.

[0031] The generation unit uses a generation AI to analyze information entered by the reception unit and generate a realistic disaster prevention simulation on the metaverse. For example, the generation unit reproduces natural disasters such as earthquake intensity, liquefaction, and fire based on map data and disaster data for a specified area. For example, the generation unit reproduces how buildings shake in response to earthquake intensity and how the ground sinks due to liquefaction. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input map data and disaster data for a specified area into the generation AI and have the generation AI reproduce natural disasters such as earthquake intensity, liquefaction, and fire. The generation AI generates the simulation in real time based on the input data, allowing the user to visually confirm it on the metaverse. For example, the generation AI considers the structure and materials of buildings, the characteristics of the terrain, etc., to simulate the effects of earthquake shaking and liquefaction in detail. Furthermore, based on factors such as the location of the fire, wind direction, and type of burning material, the system can realistically reproduce how a fire spreads and how smoke disperses. This allows the generation unit to provide a near-realistic disaster prevention simulation based on user-specified conditions, offering a useful tool for visually understanding the situation during a disaster.

[0032] The training unit conducts evacuation training based on simulations generated by the generation unit. For example, the training unit conducts training on how to evacuate in the event of an earthquake or how to deal with a fire. For example, in the event of an earthquake, the training unit conducts training on identifying evacuation routes and heading to evacuation sites. The training unit can also teach how to use a fire extinguisher and how to evacuate from a fire scene in the event of a fire. Some or all of the above processes in the training unit may be performed using AI, or not. For example, the training unit can input simulations generated by the generation unit into the AI ​​and have the AI ​​perform evacuation training. The AI ​​monitors the user's actions in real time and provides appropriate feedback. For example, if the user takes the wrong evacuation route or uses a fire extinguisher incorrectly, the AI ​​will immediately point it out and prompt the user to take the correct action. The training unit can also prepare multiple scenarios to train users to respond to various situations. For example, it can simulate evacuation actions under different conditions, such as daytime and nighttime, or sunny and rainy weather, to enable users to respond to diverse situations. This allows the training department to help users act quickly and appropriately in the event of an actual disaster.

[0033] The evaluation department evaluates the evacuation actions performed by the training department and provides feedback. For example, the evaluation department assesses the accuracy and speed of the evacuation actions and points out areas for improvement. For example, the evaluation department reports the evaluation results of the evacuation actions and points out areas for improvement. The evaluation department can also adjust the content of the next training based on the evaluation results of the evacuation actions. Some or all of the above processes in the evaluation department may be performed using AI, or not. For example, the evaluation department can input data on the evacuation actions performed by the training department into the AI ​​and have the AI ​​perform the evaluation and provide feedback. The AI ​​analyzes the user's behavior data in detail and quantifies the performance of the evacuation actions. For example, it evaluates the time from the start to the completion of the evacuation, the appropriateness of the selection of evacuation routes, the accuracy of the use of fire extinguishers, etc., and calculates an overall score. Furthermore, the AI ​​can also evaluate the user's progress by comparing it with past training data. This allows the evaluation department to understand the degree of improvement the user has achieved and reflect it in the next training. The evaluation department can also provide the user with specific areas for improvement and set goals for the next training. This allows the evaluation department to provide support for users to continuously improve their skills and enhance their ability to respond in the event of an actual disaster.

[0034] The generation unit can reproduce natural disasters such as earthquake intensity, liquefaction, and fires based on map data and disaster data for a specified area. For example, the generation unit can reproduce how buildings shake in accordance with earthquake intensity and how the ground sinks due to liquefaction, based on map data for a specified area. The generation unit can also reproduce the location and extent of a fire, based on disaster data for a specified area. For example, the generation unit can realistically reproduce natural disasters such as earthquake intensity, liquefaction, and fires by combining map data and disaster data for a specified area. This makes it possible to generate realistic disaster prevention simulations based on map data and disaster data for a specified area. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input map data and disaster data for a specified area into a generation AI and have the generation AI perform the reproduction of natural disasters such as earthquake intensity, liquefaction, and fires.

[0035] The training unit can conduct training on how to evacuate in the event of an earthquake, how to deal with a fire, and so on. For example, the training unit can conduct training on identifying evacuation routes and heading to evacuation sites in the event of an earthquake. For example, the training unit can also teach how to use a fire extinguisher and how to evacuate from a fire scene in the event of a fire. For example, the training unit can teach how to secure furniture in the event of an earthquake and conduct training on earthquake preparedness. This allows for training on evacuation actions in response to disasters such as earthquakes and fires. Some or all of the above processes in the training unit may be performed using AI, for example, or not using AI. For example, the training unit can input simulations generated by the generation unit into the AI ​​and have the AI ​​perform evacuation action training.

[0036] The evaluation unit can evaluate the evacuation actions performed by the training unit and provide feedback. For example, the evaluation unit can evaluate the accuracy and speed of the evacuation actions and point out areas for improvement. For example, the evaluation unit can report the evaluation results of the evacuation actions and point out areas for improvement. The evaluation unit can also adjust the content of the next training based on the evaluation results of the evacuation actions. For example, the evaluation unit can provide feedback to correct errors in selecting evacuation routes based on the evaluation results of the evacuation actions. For example, the evaluation unit can provide feedback to improve evacuation speed based on the evaluation results of the evacuation actions. For example, the evaluation unit can provide feedback to strengthen decision-making skills during evacuation based on the evaluation results of the evacuation actions. In this way, evaluation and feedback on evacuation actions can be performed. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on evacuation actions performed by the training unit into AI and have the AI ​​perform the evaluation and provide feedback.

[0037] The reception unit can improve the accuracy of input information by referring to past disaster data. For example, the reception unit can refer to past earthquake data and supplement the input information based on seismic intensity and damage. For example, the reception unit can refer to past fire data and supplement the input information based on the location and extent of the fire. For example, the reception unit can refer to past liquefaction data and supplement the input information based on geology and topography. In this way, the accuracy of input information can be improved by referring to past disaster data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past disaster data into AI and have AI perform the supplementation of input information.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display frequently used input items based on information the user has previously entered. For example, the reception desk can prioritize suggesting input methods the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest information related to a specific disaster scenario based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history into AI and have the AI ​​suggest the optimal input method.

[0039] The reception unit can prioritize acquiring highly relevant information based on the user's geographical location when obtaining input information. For example, the reception unit can prioritize acquiring nearby disaster information based on the user's current location. For example, the reception unit can acquire information that takes into account region-specific disaster risks based on the user's geographical location. For example, the reception unit can acquire highly relevant information by referring to past disaster history based on the user's geographical location. This enables efficient information acquisition by prioritizing the acquisition of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI and have AI acquire highly relevant information.

[0040] The reception unit can analyze the user's social media activity when acquiring input information and obtain relevant information. For example, the reception unit can analyze the user's social media posts and acquire information based on their interests regarding disasters. For example, the reception unit can analyze posts from the user's social media followers and friends and acquire relevant disaster information. For example, the reception unit can acquire nearby disaster information based on the user's social media location information. In this way, relevant information can be efficiently acquired by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​acquire relevant information.

[0041] The generation unit can improve the accuracy of the simulation by referring to past disaster data. For example, the generation unit can reproduce seismic intensity and damage based on past earthquake data. For example, the generation unit can reproduce the location and extent of fire outbreaks based on past fire data. For example, the generation unit can reproduce geology and topography based on past liquefaction data. In this way, the accuracy of the simulation can be improved by referring to past disaster data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past disaster data into a generation AI and have the generation AI perform the simulation accuracy improvement.

[0042] The generation unit can perform realistic reproductions based on detailed topographic data of a specified area during simulation generation. For example, the generation unit can realistically reproduce the shaking patterns of an earthquake based on the topographic data of a specified area. For example, the generation unit can realistically reproduce the occurrence of liquefaction based on the topographic data of a specified area. For example, the generation unit can realistically reproduce the extent of fire spread based on the topographic data of a specified area. This improves the accuracy of the simulation by performing realistic reproductions based on detailed topographic data of a specified area. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input detailed topographic data of a specified area into a generation AI and have the generation AI perform realistic reproductions.

[0043] The generation unit can generate highly relevant scenarios based on the user's geographical location information during simulation generation. For example, the generation unit can generate nearby disaster scenarios based on the user's current location. For example, the generation unit can generate scenarios that take into account region-specific disaster risks based on the user's geographical location information. For example, the generation unit can generate highly relevant scenarios by referring to past disaster history based on the user's geographical location information. This enables efficient simulation by generating highly relevant scenarios based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI execute the generation of highly relevant scenarios.

[0044] The generation unit can analyze the user's social media activity and generate relevant scenarios during simulation generation. For example, the generation unit can analyze the user's social media posts and generate scenarios based on their concerns about disasters. For example, the generation unit can analyze posts from the user's social media followers and friends and generate relevant disaster scenarios. For example, the generation unit can generate nearby disaster scenarios based on the user's social media location information. This allows for the efficient generation of relevant scenarios by analyzing the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI generate relevant scenarios.

[0045] The training unit can improve the accuracy of training by referring to past training data. For example, the training unit can provide training content that reinforces the user's weaknesses based on past training data. For example, the training unit can provide training content that strengthens the user's strengths based on past training data. For example, the training unit can evaluate the user's progress based on past training data and provide optimal training content. In this way, the accuracy of training can be improved by referring to past training data. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input past training data into AI and have AI perform the task of improving the accuracy of training.

[0046] The training unit can analyze the user's past evacuation behavior during training and propose the optimal training method. For example, the training unit can analyze the user's past evacuation behavior and propose a training method to correct errors in selecting evacuation routes. For example, the training unit can analyze the user's past evacuation behavior and propose a training method to improve evacuation speed. For example, the training unit can analyze the user's past evacuation behavior and propose a training method to strengthen decision-making skills during evacuation. In this way, by analyzing the user's past evacuation behavior, the optimal training method can be proposed. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the user's past evacuation behavior data into AI and have the AI ​​propose the optimal training method.

[0047] The training unit can provide highly relevant training content based on the user's geographical location information during training. For example, the training unit can provide training content that takes into account nearby disaster risks based on the user's current location. For example, the training unit can provide training content that takes into account region-specific disaster risks based on the user's geographical location information. For example, the training unit can provide highly relevant training content by referring to past disaster history based on the user's geographical location information. This enables efficient training by providing highly relevant training content based on the user's geographical location information. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the user's geographical location information into AI and have the AI ​​perform the task of providing highly relevant training content.

[0048] The training department can analyze users' social media activity during training and provide relevant training content. For example, the training department can analyze users' social media posts and provide training content based on their concerns regarding disasters. For example, the training department can analyze posts from users' social media followers and friends and provide relevant training content. For example, the training department can provide training content that takes into account nearby disaster risks based on the user's social media location information. In this way, relevant training content can be efficiently provided by analyzing users' social media activity. Some or all of the above processing in the training department may be performed using AI, for example, or without AI. For example, the training department can input user social media activity data into AI and have the AI ​​provide relevant training content.

[0049] The evaluation unit can improve the accuracy of evaluations by referring to past evaluation data. For example, the evaluation unit can provide feedback that strengthens the user's weaknesses based on past evaluation data. For example, the evaluation unit can provide feedback that strengthens the user's strengths based on past evaluation data. For example, the evaluation unit can evaluate the user's progress based on past evaluation data and provide optimal feedback. In this way, the accuracy of evaluations can be improved by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation data into AI and have AI perform the task of improving the accuracy of evaluations.

[0050] The evaluation unit can analyze the user's past training history during evaluation and provide optimal feedback. For example, the evaluation unit can analyze the user's past training history and provide feedback to correct errors in selecting evacuation routes. For example, the evaluation unit can analyze the user's past training history and provide feedback to improve evacuation speed. For example, the evaluation unit can analyze the user's past training history and provide feedback to strengthen decision-making during evacuation. In this way, by analyzing the user's past training history, optimal feedback can be provided. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past training history data into AI and have AI perform the task of providing optimal feedback.

[0051] The evaluation unit can provide highly relevant feedback based on the user's geographical location information during evaluation. For example, the evaluation unit can provide feedback that considers nearby disaster risks based on the user's current location. For example, the evaluation unit can provide feedback that considers region-specific disaster risks based on the user's geographical location information. For example, the evaluation unit can provide highly relevant feedback by referring to past disaster history based on the user's geographical location information. This enables efficient feedback by providing highly relevant feedback based on the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location information into AI and have AI perform the task of providing highly relevant feedback.

[0052] The evaluation unit can analyze the user's social media activity during the evaluation process and provide relevant feedback. For example, the evaluation unit can analyze the user's social media posts and provide feedback based on their concerns regarding disasters. For example, the evaluation unit can analyze posts from the user's social media followers and friends and provide relevant feedback. For example, the evaluation unit can provide feedback that takes into account nearby disaster risks based on the user's social media location information. This allows for the efficient provision of relevant feedback by analyzing the user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's social media activity data into AI and have the AI ​​provide relevant feedback.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The disaster prevention simulation system can also be equipped with a health management unit that monitors the user's health status. The health management unit acquires vital data such as the user's heart rate, blood pressure, and body temperature in real time, and evaluates the user's health status based on this data. For example, if a user's heart rate suddenly increases during evacuation, the health management unit can issue an alert prompting the user to take a break. It can also adjust evacuation routes and methods according to the user's health status. This ensures safe evacuation actions that take the user's health into consideration.

[0055] The disaster prevention simulation system can also include a skill evaluation unit that assesses the user's skill level. The skill evaluation unit evaluates the user's disaster prevention skills based on past training data. For example, it evaluates whether the user was able to accurately select an evacuation route and whether they correctly understood how to use a fire extinguisher during a fire. The skill evaluation unit can customize the training content according to the user's skill level. This provides training optimized for the user's skill level, resulting in effective disaster prevention education.

[0056] The disaster prevention simulation system can also be equipped with a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit analyzes the user's behavioral patterns based on past evacuation actions and training data. For example, it analyzes the user's tendencies when selecting evacuation routes and their reaction speed during training. Based on the user's behavioral patterns, the behavioral analysis unit can suggest training content and evacuation routes. This provides training optimized for the user's behavioral patterns, enabling effective disaster prevention education.

[0057] The disaster prevention simulation system can also include a communication evaluation unit that assesses the user's communication skills. This unit evaluates how users communicate with other users during training. For example, it assesses whether users can accurately convey evacuation instructions and whether they can cooperate smoothly with other users. The communication evaluation unit can customize the training content according to the user's communication skills. This allows for the provision of training that improves users' communication skills, resulting in effective disaster prevention education.

[0058] The disaster prevention simulation system can also include a learning analysis unit that analyzes the user's learning style. The learning analysis unit analyzes what learning methods are most effective for the user. For example, if the user prefers visual information, it provides training content that heavily utilizes visuals. If the user prefers auditory information, it provides training content that heavily utilizes audio guidance. The learning analysis unit can customize the training content according to the user's learning style. This provides training optimized for the user's learning style, resulting in effective disaster prevention education.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk inputs the information necessary to start the disaster prevention simulation for the designated area. This information includes, for example, earthquake intensity, liquefaction status, and fire locations. Users can input this information through the reception desk. Step 2: The generation unit analyzes the information entered by the reception unit and generates a realistic disaster prevention simulation on the metaverse. Based on map data and disaster data for the specified area, the generation unit reproduces natural disasters such as earthquake intensity, liquefaction, and fire. Processing in the generation unit may also be performed using generation AI. Step 3: The training unit conducts evacuation training based on the simulations generated by the generation unit. The training unit simulates evacuation actions in the event of an earthquake or fire, and provides training on things like confirming evacuation routes and how to use fire extinguishers. Processing in the training unit may also be done using AI. Step 4: The evaluation department evaluates the evacuation actions performed by the training department and provides feedback. The evaluation department assesses the accuracy and speed of the evacuation actions and points out areas for improvement. It can also adjust the content of the next training session based on the evaluation results. The processing in the evaluation department may be carried out using AI.

[0061] (Example of form 2) The disaster prevention simulation system according to an embodiment of the present invention is a system that provides a realistic disaster prevention simulation on the metaverse using a generative AI. This disaster prevention simulation system takes input information to start a disaster prevention simulation for a designated area, and the generative AI analyzes the input information to generate a realistic disaster prevention simulation on the metaverse. Based on the generated simulation, the user trains on evacuation actions on the metaverse. This system is expected to be widely adopted by various organizations such as local governments, companies, and schools. Using modern technology, it aims to prevent isolation and panic, help people take calm and appropriate actions, and improve the overall disaster prevention capabilities of the region. For example, the user inputs information to start a disaster prevention simulation for a designated area. For example, information such as earthquake intensity, liquefaction occurrence, and fire location is input. This information is input to the generative AI. Next, the generative AI analyzes the input information and generates a realistic disaster prevention simulation on the metaverse. The generative AI reproduces natural disasters such as earthquake intensity, liquefaction, and fire based on map data and disaster data of the designated area. For example, it reproduces how buildings shake in accordance with the earthquake intensity and how the ground sinks due to liquefaction. Based on the generated disaster simulations, users train in evacuation procedures within the metaverse. For example, they train on how to evacuate in the event of an earthquake or how to deal with a fire. By experiencing realistic disaster scenarios within the metaverse, users learn appropriate evacuation actions. This system enables realistic experiences that were difficult to achieve with conventional disaster training. For example, even in areas where field exercises are difficult, or for people with insufficient actual disaster experience, they can learn appropriate evacuation actions through realistic disaster simulations within the metaverse. This is expected to improve the overall disaster preparedness of the region, build safer communities, and enhance preparedness for future disasters. Thus, the disaster simulation system can realistically reproduce disaster simulations for designated areas and conduct evacuation action training and evaluation.

[0062] The disaster prevention simulation system according to the embodiment comprises a reception unit, a generation unit, a training unit, and an evaluation unit. The reception unit inputs information for starting a disaster prevention simulation for a designated area. This information includes, but is not limited to, earthquake intensity, liquefaction occurrence, and fire locations. For example, the reception unit can receive input from a user for starting a disaster prevention simulation for a designated area. The generation unit uses a generation AI to analyze the information input by the reception unit and generates a realistic disaster prevention simulation on the metaverse. For example, the generation unit reproduces natural disasters such as earthquake intensity, liquefaction, and fires based on map data and disaster data for the designated area. For example, the generation unit reproduces how buildings shake in response to earthquake intensity and how the ground sinks due to liquefaction. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit inputs map data and disaster data for a specified area into the generation AI, and has the generation AI perform the reproduction of natural disasters such as earthquake intensity, liquefaction, and fire. The training unit conducts evacuation training based on the simulations generated by the generation unit. The training unit conducts training on, for example, how to evacuate in the event of an earthquake, or how to deal with a fire. For example, the training unit conducts training on checking evacuation routes and heading to evacuation sites in the event of an earthquake. The training unit can also learn how to use a fire extinguisher and train on how to evacuate from a fire scene in the event of a fire. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit inputs the simulations generated by the generation unit into the AI, and has the AI ​​perform evacuation training. The evaluation unit evaluates the evacuation actions performed by the training unit and provides feedback. For example, the evaluation unit evaluates the accuracy and speed of the evacuation actions and points out areas for improvement. For example, the evaluation unit reports the evaluation results of the evacuation actions and points out areas for improvement.Furthermore, the evaluation unit can adjust the content of the next training session based on the evaluation results of the evacuation behavior. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the evacuation behavior performed by the training unit into the AI ​​and have the AI ​​perform the evaluation and provide feedback. As a result, the disaster prevention simulation system according to the embodiment can realistically reproduce disaster prevention simulations in a designated area and conduct training and evaluation of evacuation behavior.

[0063] The reception unit inputs information to start a disaster prevention simulation for a designated area. This information includes, but is not limited to, earthquake intensity, liquefaction occurrence, and fire locations. For example, the reception unit can receive input from users to start a disaster prevention simulation for a designated area. Specifically, users can select map data for the area they want to simulate through a dedicated interface and input detailed information such as earthquake intensity, time of occurrence, liquefaction locations, and fire locations. This allows the reception unit to perform the initial simulation settings based on the conditions specified by the user. Furthermore, the reception unit can automatically acquire supplementary information to improve the accuracy of the simulation by referring to past disaster data and weather data. For example, it can evaluate the probability of earthquake occurrence and liquefaction risk in the designated area based on past earthquake data and publicly available information from the Japan Meteorological Agency, and reflect this in the simulation. This allows the reception unit to integrate the information entered by the user with the supplementary information and prepare to start a more realistic and accurate disaster prevention simulation.

[0064] The generation unit uses a generation AI to analyze information entered by the reception unit and generate a realistic disaster prevention simulation on the metaverse. For example, the generation unit reproduces natural disasters such as earthquake intensity, liquefaction, and fire based on map data and disaster data for a specified area. For example, the generation unit reproduces how buildings shake in response to earthquake intensity and how the ground sinks due to liquefaction. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input map data and disaster data for a specified area into the generation AI and have the generation AI reproduce natural disasters such as earthquake intensity, liquefaction, and fire. The generation AI generates the simulation in real time based on the input data, allowing the user to visually confirm it on the metaverse. For example, the generation AI considers the structure and materials of buildings, the characteristics of the terrain, etc., to simulate the effects of earthquake shaking and liquefaction in detail. Furthermore, based on factors such as the location of the fire, wind direction, and type of burning material, the system can realistically reproduce how a fire spreads and how smoke disperses. This allows the generation unit to provide a near-realistic disaster prevention simulation based on user-specified conditions, offering a useful tool for visually understanding the situation during a disaster.

[0065] The training unit conducts evacuation training based on simulations generated by the generation unit. For example, the training unit conducts training on how to evacuate in the event of an earthquake or how to deal with a fire. For example, in the event of an earthquake, the training unit conducts training on identifying evacuation routes and heading to evacuation sites. The training unit can also teach how to use a fire extinguisher and how to evacuate from a fire scene in the event of a fire. Some or all of the above processes in the training unit may be performed using AI, or not. For example, the training unit can input simulations generated by the generation unit into the AI ​​and have the AI ​​perform evacuation training. The AI ​​monitors the user's actions in real time and provides appropriate feedback. For example, if the user takes the wrong evacuation route or uses a fire extinguisher incorrectly, the AI ​​will immediately point it out and prompt the user to take the correct action. The training unit can also prepare multiple scenarios to train users to respond to various situations. For example, it can simulate evacuation actions under different conditions, such as daytime and nighttime, or sunny and rainy weather, to enable users to respond to diverse situations. This allows the training department to help users act quickly and appropriately in the event of an actual disaster.

[0066] The evaluation department evaluates the evacuation actions performed by the training department and provides feedback. For example, the evaluation department assesses the accuracy and speed of the evacuation actions and points out areas for improvement. For example, the evaluation department reports the evaluation results of the evacuation actions and points out areas for improvement. The evaluation department can also adjust the content of the next training based on the evaluation results of the evacuation actions. Some or all of the above processes in the evaluation department may be performed using AI, or not. For example, the evaluation department can input data on the evacuation actions performed by the training department into the AI ​​and have the AI ​​perform the evaluation and provide feedback. The AI ​​analyzes the user's behavior data in detail and quantifies the performance of the evacuation actions. For example, it evaluates the time from the start to the completion of the evacuation, the appropriateness of the selection of evacuation routes, the accuracy of the use of fire extinguishers, etc., and calculates an overall score. Furthermore, the AI ​​can also evaluate the user's progress by comparing it with past training data. This allows the evaluation department to understand the degree of improvement the user has achieved and reflect it in the next training. The evaluation department can also provide the user with specific areas for improvement and set goals for the next training. This allows the evaluation department to provide support for users to continuously improve their skills and enhance their ability to respond in the event of an actual disaster.

[0067] The generation unit can reproduce natural disasters such as earthquake intensity, liquefaction, and fires based on map data and disaster data for a specified area. For example, the generation unit can reproduce how buildings shake in accordance with earthquake intensity and how the ground sinks due to liquefaction, based on map data for a specified area. The generation unit can also reproduce the location and extent of a fire, based on disaster data for a specified area. For example, the generation unit can realistically reproduce natural disasters such as earthquake intensity, liquefaction, and fires by combining map data and disaster data for a specified area. This makes it possible to generate realistic disaster prevention simulations based on map data and disaster data for a specified area. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input map data and disaster data for a specified area into a generation AI and have the generation AI perform the reproduction of natural disasters such as earthquake intensity, liquefaction, and fires.

[0068] The training unit can conduct training on how to evacuate in the event of an earthquake, how to deal with a fire, and so on. For example, the training unit can conduct training on identifying evacuation routes and heading to evacuation sites in the event of an earthquake. For example, the training unit can also teach how to use a fire extinguisher and how to evacuate from a fire scene in the event of a fire. For example, the training unit can teach how to secure furniture in the event of an earthquake and conduct training on earthquake preparedness. This allows for training on evacuation actions in response to disasters such as earthquakes and fires. Some or all of the above processes in the training unit may be performed using AI, for example, or not using AI. For example, the training unit can input simulations generated by the generation unit into the AI ​​and have the AI ​​perform evacuation action training.

[0069] The evaluation unit can evaluate the evacuation actions performed by the training unit and provide feedback. For example, the evaluation unit can evaluate the accuracy and speed of the evacuation actions and point out areas for improvement. For example, the evaluation unit can report the evaluation results of the evacuation actions and point out areas for improvement. The evaluation unit can also adjust the content of the next training based on the evaluation results of the evacuation actions. For example, the evaluation unit can provide feedback to correct errors in selecting evacuation routes based on the evaluation results of the evacuation actions. For example, the evaluation unit can provide feedback to improve evacuation speed based on the evaluation results of the evacuation actions. For example, the evaluation unit can provide feedback to strengthen decision-making skills during evacuation based on the evaluation results of the evacuation actions. In this way, evaluation and feedback on evacuation actions can be performed. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on evacuation actions performed by the training unit into AI and have the AI ​​perform the evaluation and provide feedback.

[0070] The reception unit can estimate the user's emotions and prioritize input information based on the estimated emotions. For example, if the user is nervous, the reception unit will prioritize inputting important information and postpone detailed information. For example, if the user is relaxed, the reception unit will prioritize inputting detailed information to improve the overall simulation accuracy. For example, if the user is in a hurry, the reception unit will only input the most important information and quickly start the simulation. This enables efficient information input by prioritizing input information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception unit can improve the accuracy of input information by referring to past disaster data. For example, the reception unit can refer to past earthquake data and supplement the input information based on seismic intensity and damage. For example, the reception unit can refer to past fire data and supplement the input information based on the location and extent of the fire. For example, the reception unit can refer to past liquefaction data and supplement the input information based on geology and topography. In this way, the accuracy of input information can be improved by referring to past disaster data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past disaster data into AI and have AI perform the supplementation of input information.

[0072] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display frequently used input items based on information the user has previously entered. For example, the reception desk can prioritize suggesting input methods the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest information related to a specific disaster scenario based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history into AI and have the AI ​​suggest the optimal input method.

[0073] The reception unit can estimate the user's emotions and adjust the display method of input information based on the estimated user emotions. For example, if the user is nervous, the reception unit provides a simple and highly visible display method. For example, if the user is relaxed, the reception unit provides a display method that includes detailed information. For example, if the user is in a hurry, the reception unit provides a display method that gets straight to the point. This allows for highly visible displays by adjusting the display method of input information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0074] The reception unit can prioritize acquiring highly relevant information based on the user's geographical location when obtaining input information. For example, the reception unit can prioritize acquiring nearby disaster information based on the user's current location. For example, the reception unit can acquire information that takes into account region-specific disaster risks based on the user's geographical location. For example, the reception unit can acquire highly relevant information by referring to past disaster history based on the user's geographical location. This enables efficient information acquisition by prioritizing the acquisition of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI and have AI acquire highly relevant information.

[0075] The reception unit can analyze the user's social media activity when acquiring input information and obtain relevant information. For example, the reception unit can analyze the user's social media posts and acquire information based on their interests regarding disasters. For example, the reception unit can analyze posts from the user's social media followers and friends and acquire relevant disaster information. For example, the reception unit can acquire nearby disaster information based on the user's social media location information. In this way, relevant information can be efficiently acquired by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​acquire relevant information.

[0076] The generation unit can estimate the user's emotions and adjust the realism of the simulation based on the estimated user emotions. For example, if the user is tense, the generation unit will lower the realism of the simulation to reduce stress. For example, if the user is relaxed, the generation unit will increase the realism of the simulation to provide an experience closer to a real disaster. For example, if the user is excited, the generation unit will adjust the realism of the simulation to provide an appropriate level of stimulation. In this way, by adjusting the realism of the simulation based on the user's emotions, an appropriate level of experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The generation unit can improve the accuracy of the simulation by referring to past disaster data. For example, the generation unit can reproduce seismic intensity and damage based on past earthquake data. For example, the generation unit can reproduce the location and extent of fire outbreaks based on past fire data. For example, the generation unit can reproduce geology and topography based on past liquefaction data. In this way, the accuracy of the simulation can be improved by referring to past disaster data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past disaster data into a generation AI and have the generation AI perform the simulation accuracy improvement.

[0078] The generation unit can perform realistic reproductions based on detailed topographic data of a specified area during simulation generation. For example, the generation unit can realistically reproduce the shaking patterns of an earthquake based on the topographic data of a specified area. For example, the generation unit can realistically reproduce the occurrence of liquefaction based on the topographic data of a specified area. For example, the generation unit can realistically reproduce the extent of fire spread based on the topographic data of a specified area. This improves the accuracy of the simulation by performing realistic reproductions based on detailed topographic data of a specified area. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input detailed topographic data of a specified area into a generation AI and have the generation AI perform realistic reproductions.

[0079] The generation unit can estimate the user's emotions and adjust the difficulty of the simulation based on the estimated emotions. For example, if the user is tense, the generation unit can lower the difficulty of the simulation to reduce stress. For example, if the user is relaxed, the generation unit can increase the difficulty of the simulation to provide an experience closer to a real disaster. For example, if the user is excited, the generation unit can adjust the difficulty of the simulation to provide an appropriate level of stimulation. In this way, by adjusting the difficulty of the simulation based on the user's emotions, an appropriate level of experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The generation unit can generate highly relevant scenarios based on the user's geographical location information during simulation generation. For example, the generation unit can generate nearby disaster scenarios based on the user's current location. For example, the generation unit can generate scenarios that take into account region-specific disaster risks based on the user's geographical location information. For example, the generation unit can generate highly relevant scenarios by referring to past disaster history based on the user's geographical location information. This enables efficient simulation by generating highly relevant scenarios based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI execute the generation of highly relevant scenarios.

[0081] The generation unit can analyze the user's social media activity and generate relevant scenarios during simulation generation. For example, the generation unit can analyze the user's social media posts and generate scenarios based on their concerns about disasters. For example, the generation unit can analyze posts from the user's social media followers and friends and generate relevant disaster scenarios. For example, the generation unit can generate nearby disaster scenarios based on the user's social media location information. This allows for the efficient generation of relevant scenarios by analyzing the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI generate relevant scenarios.

[0082] The training unit can estimate the user's emotions and adjust the training content based on the estimated emotions. For example, if the user is nervous, the training unit provides simple and highly visual training content. If the user is relaxed, the training unit provides training content that includes detailed information. If the user is in a hurry, the training unit provides training content that gets straight to the point. In this way, appropriate training can be provided by adjusting the training content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The training unit can improve the accuracy of training by referring to past training data. For example, the training unit can provide training content that reinforces the user's weaknesses based on past training data. For example, the training unit can provide training content that strengthens the user's strengths based on past training data. For example, the training unit can evaluate the user's progress based on past training data and provide optimal training content. In this way, the accuracy of training can be improved by referring to past training data. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input past training data into AI and have AI perform the task of improving the accuracy of training.

[0084] The training unit can analyze the user's past evacuation behavior during training and propose the optimal training method. For example, the training unit can analyze the user's past evacuation behavior and propose a training method to correct errors in selecting evacuation routes. For example, the training unit can analyze the user's past evacuation behavior and propose a training method to improve evacuation speed. For example, the training unit can analyze the user's past evacuation behavior and propose a training method to strengthen decision-making skills during evacuation. In this way, by analyzing the user's past evacuation behavior, the optimal training method can be proposed. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the user's past evacuation behavior data into AI and have the AI ​​propose the optimal training method.

[0085] The training unit can estimate the user's emotions and adjust the training pace based on the estimated emotions. For example, if the user is nervous, the training unit can slow down the training pace to deepen understanding. For example, if the user is relaxed, the training unit can speed up the training pace to proceed efficiently. For example, if the user is in a hurry, the training unit can adjust the training pace to provide concise content. In this way, appropriate training can be provided by adjusting the training pace based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0086] The training unit can provide highly relevant training content based on the user's geographical location information during training. For example, the training unit can provide training content that takes into account nearby disaster risks based on the user's current location. For example, the training unit can provide training content that takes into account region-specific disaster risks based on the user's geographical location information. For example, the training unit can provide highly relevant training content by referring to past disaster history based on the user's geographical location information. This enables efficient training by providing highly relevant training content based on the user's geographical location information. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the user's geographical location information into AI and have the AI ​​perform the task of providing highly relevant training content.

[0087] The training department can analyze users' social media activity during training and provide relevant training content. For example, the training department can analyze users' social media posts and provide training content based on their concerns regarding disasters. For example, the training department can analyze posts from users' social media followers and friends and provide relevant training content. For example, the training department can provide training content that takes into account nearby disaster risks based on the user's social media location information. In this way, relevant training content can be efficiently provided by analyzing users' social media activity. Some or all of the above processing in the training department may be performed using AI, for example, or without AI. For example, the training department can input user social media activity data into AI and have the AI ​​provide relevant training content.

[0088] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is tense, the evaluation unit may relax the evaluation criteria to reduce stress. For example, if the user is relaxed, the evaluation unit may tighten the evaluation criteria to perform an evaluation closer to that of an actual disaster. For example, if the user is excited, the evaluation unit may adjust the evaluation criteria to provide an appropriate level of feedback. This allows for the provision of appropriate feedback by adjusting the evaluation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The evaluation unit can improve the accuracy of evaluations by referring to past evaluation data. For example, the evaluation unit can provide feedback that strengthens the user's weaknesses based on past evaluation data. For example, the evaluation unit can provide feedback that strengthens the user's strengths based on past evaluation data. For example, the evaluation unit can evaluate the user's progress based on past evaluation data and provide optimal feedback. In this way, the accuracy of evaluations can be improved by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation data into AI and have AI perform the task of improving the accuracy of evaluations.

[0090] The evaluation unit can analyze the user's past training history during evaluation and provide optimal feedback. For example, the evaluation unit can analyze the user's past training history and provide feedback to correct errors in selecting evacuation routes. For example, the evaluation unit can analyze the user's past training history and provide feedback to improve evacuation speed. For example, the evaluation unit can analyze the user's past training history and provide feedback to strengthen decision-making during evacuation. In this way, by analyzing the user's past training history, optimal feedback can be provided. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past training history data into AI and have AI perform the task of providing optimal feedback.

[0091] The evaluation unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is tense, the evaluation unit will prioritize positive feedback to reduce stress. For example, if the user is relaxed, the evaluation unit will provide detailed feedback to deepen understanding. For example, if the user is in a hurry, the evaluation unit will provide concise feedback. In this way, appropriate feedback can be provided by adjusting the content of the feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0092] The evaluation unit can provide highly relevant feedback based on the user's geographical location information during evaluation. For example, the evaluation unit can provide feedback that considers nearby disaster risks based on the user's current location. For example, the evaluation unit can provide feedback that considers region-specific disaster risks based on the user's geographical location information. For example, the evaluation unit can provide highly relevant feedback by referring to past disaster history based on the user's geographical location information. This enables efficient feedback by providing highly relevant feedback based on the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location information into AI and have AI perform the task of providing highly relevant feedback.

[0093] The evaluation unit can analyze the user's social media activity during the evaluation process and provide relevant feedback. For example, the evaluation unit can analyze the user's social media posts and provide feedback based on their concerns regarding disasters. For example, the evaluation unit can analyze posts from the user's social media followers and friends and provide relevant feedback. For example, the evaluation unit can provide feedback that takes into account nearby disaster risks based on the user's social media location information. This allows for the efficient provision of relevant feedback by analyzing the user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's social media activity data into AI and have the AI ​​provide relevant feedback.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The disaster prevention simulation system can also be equipped with a health management unit that monitors the user's health status. The health management unit acquires vital data such as the user's heart rate, blood pressure, and body temperature in real time, and evaluates the user's health status based on this data. For example, if a user's heart rate suddenly increases during evacuation, the health management unit can issue an alert prompting the user to take a break. It can also adjust evacuation routes and methods according to the user's health status. This ensures safe evacuation actions that take the user's health into consideration.

[0096] The disaster prevention simulation system can also include a skill evaluation unit that assesses the user's skill level. The skill evaluation unit evaluates the user's disaster prevention skills based on past training data. For example, it evaluates whether the user was able to accurately select an evacuation route and whether they correctly understood how to use a fire extinguisher during a fire. The skill evaluation unit can customize the training content according to the user's skill level. This provides training optimized for the user's skill level, resulting in effective disaster prevention education.

[0097] The disaster prevention simulation system can also be equipped with a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit analyzes the user's behavioral patterns based on past evacuation actions and training data. For example, it analyzes the user's tendencies when selecting evacuation routes and their reaction speed during training. Based on the user's behavioral patterns, the behavioral analysis unit can suggest training content and evacuation routes. This provides training optimized for the user's behavioral patterns, enabling effective disaster prevention education.

[0098] The disaster prevention simulation system can also include a communication evaluation unit that assesses the user's communication skills. This unit evaluates how users communicate with other users during training. For example, it assesses whether users can accurately convey evacuation instructions and whether they can cooperate smoothly with other users. The communication evaluation unit can customize the training content according to the user's communication skills. This allows for the provision of training that improves users' communication skills, resulting in effective disaster prevention education.

[0099] The disaster prevention simulation system can also be equipped with a psychological management unit that monitors the user's psychological state. This unit analyzes the user's facial expressions and tone of voice to evaluate their psychological state. For example, if a user is experiencing stress during training, the psychological management unit can provide advice on how to relax. It can also adjust the training content and simulation difficulty level according to the user's psychological state. This enables effective disaster prevention education that takes the user's psychological state into consideration.

[0100] The disaster prevention simulation system can also include a learning analysis unit that analyzes the user's learning style. The learning analysis unit analyzes what learning methods are most effective for the user. For example, if the user prefers visual information, it provides training content that heavily utilizes visuals. If the user prefers auditory information, it provides training content that heavily utilizes audio guidance. The learning analysis unit can customize the training content according to the user's learning style. This provides training optimized for the user's learning style, resulting in effective disaster prevention education.

[0101] The disaster prevention simulation system can further estimate the user's emotions and adjust the training feedback based on those emotions. For example, if the user is tense, positive feedback is prioritized to reduce stress. If the user is relaxed, detailed feedback is provided to deepen understanding. If the user is in a hurry, concise feedback is provided. This allows for the provision of appropriate feedback by adjusting the content based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The disaster prevention simulation system can further estimate the user's emotions and adjust the training pace based on those emotions. For example, if the user is nervous, the training pace can be slowed down to deepen understanding. If the user is relaxed, the training pace can be increased for more efficient progress. If the user is in a hurry, the training pace can be adjusted to provide concise and to-the-point content. This allows for appropriate training by adjusting the training pace based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The disaster prevention simulation system can further estimate the user's emotions and adjust the simulation's realism based on those emotions. For example, if the user is tense, the simulation's realism can be lowered to reduce stress. If the user is relaxed, the simulation's realism can be increased to provide an experience closer to a real disaster. If the user is excited, the simulation's realism can be adjusted to provide an appropriate level of stimulation. In this way, by adjusting the simulation's realism based on the user's emotions, an appropriate level of experience can be provided. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The disaster prevention simulation system can further estimate the user's emotions and adjust the simulation difficulty based on those emotions. For example, if the user is tense, the simulation difficulty can be lowered to reduce stress. If the user is relaxed, the simulation difficulty can be increased to provide an experience closer to a real disaster. If the user is excited, the simulation difficulty can be adjusted to provide an appropriate level of stimulation. In this way, by adjusting the simulation difficulty based on the user's emotions, an appropriate level of experience can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk inputs the information necessary to start the disaster prevention simulation for the designated area. This information includes, for example, earthquake intensity, liquefaction status, and fire locations. Users can input this information through the reception desk. Step 2: The generation unit analyzes the information entered by the reception unit and generates a realistic disaster prevention simulation on the metaverse. Based on map data and disaster data for the specified area, the generation unit reproduces natural disasters such as earthquake intensity, liquefaction, and fire. Processing in the generation unit may also be performed using generation AI. Step 3: The training unit conducts evacuation training based on the simulations generated by the generation unit. The training unit simulates evacuation actions in the event of an earthquake or fire, and provides training on things like confirming evacuation routes and how to use fire extinguishers. Processing in the training unit may also be done using AI. Step 4: The evaluation department evaluates the evacuation actions performed by the training department and provides feedback. The evaluation department assesses the accuracy and speed of the evacuation actions and points out areas for improvement. It can also adjust the content of the next training session based on the evaluation results. The processing in the evaluation department may be carried out using AI.

[0107] 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.

[0108] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the reception unit, generation unit, training unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which receives information from the user to start a disaster prevention simulation for a specified area. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI and generates a realistic disaster prevention simulation on the metaverse. The training unit is implemented by the control unit 46A of the smart device 14, which conducts evacuation training based on the generated simulation. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which evaluates the training results and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0114] 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.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0116] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] 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.

[0118] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, training unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which receives information from the user to start a disaster prevention simulation for a specified area. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI and generates a realistic disaster prevention simulation on the metaverse. The training unit is implemented, for example, by the control unit 46A of the smart glasses 214, which conducts evacuation action training based on the generated simulation. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which evaluates the training results and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0132] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] 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.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, training unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs information to start a disaster prevention simulation for a specified area. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the input information using a generation AI and generates a realistic disaster prevention simulation on the metaverse. The training unit is implemented by the control unit 46A of the headset terminal 314, where it conducts evacuation training based on the generated simulation. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it evaluates the training results and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0148] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] 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.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, generation unit, training unit, and evaluation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which receives information from the user to start a disaster prevention simulation for a specified area. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI and generates a realistic disaster prevention simulation on the metaverse. The training unit is implemented, for example, by the control unit 46A of the robot 414, which conducts evacuation training based on the generated simulation. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which evaluates the training results and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] Figure 9 shows the 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.

[0162] 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.

[0163] 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.

[0164] 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, and motorcycles, 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 based, for example, 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.

[0165] 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."

[0166] 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.

[0167] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] 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 other things 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.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A reception area for entering information to start a disaster prevention simulation for a designated area, A generation unit analyzes the information input by the reception unit and generates a realistic disaster prevention simulation on the metaverse, A training unit that performs evacuation training based on the simulation generated by the generation unit, The system includes an evaluation unit that evaluates the evacuation actions performed by the training unit and provides feedback. A system characterized by the following features. (Note 2) The generating unit is Based on map data and disaster data for a designated area, it reproduces natural disasters such as earthquake intensity, liquefaction, and fires. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned training department The training will cover topics such as how to evacuate in the event of an earthquake and how to deal with a fire. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Evaluate and provide feedback on evacuation procedures performed by the training department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Referencing past disaster data improves the accuracy of input information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts how input information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When acquiring input information, the system prioritizes retrieving highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When acquiring input information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the realism of the simulation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is By referring to past disaster data, we can improve the accuracy of simulations. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During simulation generation, a realistic reproduction is achieved based on detailed topographic data of the specified region. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the difficulty of the simulation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During simulation generation, highly relevant scenarios are generated based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During simulation generation, the system analyzes the user's social media activity and generates relevant scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned training department It estimates the user's emotions and adjusts the training content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned training department Referencing past training data improves training accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned training department During training, the system analyzes the user's past evacuation behavior and suggests the optimal training method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned training department It estimates the user's emotions and adjusts the training progress speed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned training department During training, the system provides highly relevant training content based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned training department During training, we analyze users' social media activity and provide relevant training content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, We improve the accuracy of evaluations by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During evaluation, the system analyzes the user's past training history to provide optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, During evaluation, provide highly relevant feedback based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit, During the evaluation process, we analyze the user's social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area for entering information to start a disaster prevention simulation for a designated area, A generation unit analyzes the information input by the reception unit and generates a realistic disaster prevention simulation on the metaverse, A training unit that performs evacuation training based on the simulation generated by the generation unit, The system includes an evaluation unit that evaluates the evacuation actions performed by the training unit and provides feedback. A system characterized by the following features.

2. The generating unit is Based on map data and disaster data for a designated area, it reproduces natural disasters such as earthquake intensity, liquefaction, and fires. The system according to feature 1.

3. The aforementioned training department, The training will cover topics such as how to evacuate in the event of an earthquake and how to deal with a fire. The system according to feature 1.

4. The evaluation unit, Evaluate and provide feedback on evacuation procedures performed by the training department. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Referencing past disaster data improves the accuracy of input information. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts how input information is displayed based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When acquiring input information, the system prioritizes retrieving highly relevant information based on the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When acquiring input information, the system analyzes the user's social media activity and retrieves relevant information. The system according to feature 1.

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