System

Abstract

An object of a system according to an embodiment is to reproduce a realistic disaster scenario and provide effective training to a user.SOLUTION: A system according to an embodiment includes a disaster scenario generation unit, a disaster reproduction unit, and a training provision unit. The disaster scenario generation part generates a disaster scenario by using the generation AI. The disaster reproduction unit reproduces a disaster on the Metaverse based on the disaster scenario generated by the disaster scenario generation unit. The training provision unit provides training to the user for the disaster reproduced by the disaster reproduction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to realistically recreate disaster scenarios and provide effective training to users.

[0005] The system according to the embodiment aims to reproduce realistic disaster scenarios and provide effective training to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a disaster scenario generation unit, a disaster reproduction unit, and a training provision unit. The disaster scenario generation unit generates a disaster scenario using a generation AI. The disaster reproduction unit reproduces a disaster on the metaverse based on the disaster scenario generated by the disaster scenario generation unit. The training provision unit provides training to users for the disaster reproduced by the disaster reproduction unit. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce realistic disaster scenarios and provide effective training to users. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A disaster prevention simulation system according to an embodiment of the present invention uses a generation AI to generate realistic disaster scenarios in the metaverse, recreates disasters, and provides training to users. This allows the disaster prevention simulation system to train users to respond appropriately to various disaster scenarios, thereby improving disaster prevention capabilities across the entire region.

[0029] A disaster prevention simulation system according to an embodiment includes a disaster scenario generation unit, a disaster reproduction unit, and a training provision unit. The disaster scenario generation unit generates a disaster scenario using a generation AI. For example, the generation AI analyzes instructions for a disaster scenario input by a user and understands the contents of the instructions. The generation AI receives input in the form of prompts containing instructions for the user's intended actions, and the generation AI generates a disaster scenario based on the prompts. The disaster reproduction unit reproduces a disaster in the metaverse based on the disaster scenario generated by the disaster scenario generation unit. For example, in the event of a magnitude 7 earthquake, the unit realistically reproduces the shaking and collapse of buildings. In addition, in the event of liquefaction, the unit realistically reproduces the sinking of the ground and the tilting of buildings. In the event of a fire, the unit realistically reproduces the spread of flames and smoke. The training provision unit provides training to the user on the disaster reproduced by the disaster reproduction unit. For example, the unit instructs the user on evacuation route selection in the event of an earthquake and firefighting procedures in the event of a fire. The generation AI analyzes the user's behavior in real time and provides appropriate advice and feedback. This allows users to acquire the skills to respond appropriately in the event of a real disaster. As a result, the disaster prevention simulation system according to the embodiment provides training for users to respond appropriately to various disaster scenarios, thereby improving disaster prevention capabilities across the entire region.

[0030] The disaster scenario generation unit can analyze past disaster data and learn patterns of actual disasters to generate realistic scenarios. For example, the generation AI of the disaster scenario generation unit analyzes past earthquake data and learns patterns such as seismic intensity, epicenter, and damage extent. This allows it to recreate actual earthquake scenarios and provide users with a realistic experience. The generation AI also analyzes past flood data and learns patterns such as rainfall, river water levels, and damage extent. This allows it to recreate actual flood scenarios and provide users with a realistic experience. The generation AI also analyzes past fire data and learns patterns such as ignition cause, burning speed, and damage extent. This allows it to recreate actual fire scenarios and provide users with a realistic experience. In this way, by analyzing past disaster data and generating realistic scenarios, it is possible to provide users with more realistic disaster prevention training.

[0031] The disaster scenario generation unit can incorporate structural information about the user's residence or building and generate scenarios that reflect regional disaster risks. For example, the generation AI incorporates geographic information about the user's residence and generates scenarios that reflect regional disaster risks, such as earthquakes and tsunamis. For example, a tsunami scenario is provided for a user living in a coastal area. The generation AI also incorporates structural information about the user's building and generates scenarios that reflect regional disaster risks, such as earthquakes and fires. For example, a fire scenario is provided for a user living in a wooden building. The generation AI also incorporates meteorological data about the user's residence and generates scenarios that reflect regional disaster risks, such as typhoons and heavy rains. For example, a typhoon scenario is provided for a user living in an area prone to typhoons. In this way, by generating scenarios that reflect the user's residence and structural information about the building, training for regional disaster risks can be provided.

[0032] The disaster scenario generation unit can generate complex disaster scenarios that combine different disasters. In the disaster scenario generation unit, for example, the generation AI generates a complex disaster scenario that combines a flood and an earthquake. For example, it provides a scenario in which an earthquake causes a levee to collapse, followed by a flood. The generation AI also generates a complex disaster scenario that combines a tsunami and a fire. For example, it provides a scenario in which a tsunami causes a gas pipe to burst, followed by a fire. The generation AI also generates a complex disaster scenario that combines a typhoon and a landslide. For example, it provides a scenario in which a typhoon causes continuous heavy rain, followed by a landslide. In this way, by generating complex disaster scenarios that combine different disasters, it is possible to provide more complex disaster response training to users.

[0033] The disaster scenario generation unit can learn user feedback on disaster scenarios and improve the accuracy of the scenarios. In the disaster scenario generation unit, for example, the generation AI collects user feedback and improves the accuracy of the scenario. For example, the user evaluates the realism of the scenario and improves the scenario based on that evaluation. The generation AI also analyzes user behavior data and improves the accuracy of the scenario. For example, it learns the behavior of users when selecting an evacuation route and generates a more realistic scenario. The generation AI also adjusts the difficulty of the scenario based on user feedback. For example, if the user is unable to complete the scenario, it provides a scenario with a lower difficulty level. In this way, the system can learn user feedback and improve the accuracy of the scenario, thereby providing more realistic disaster prevention training.

[0034] The disaster reproduction unit can reproduce in detail the internal structure of a building and the arrangement of furniture when recreating a disaster on the metaverse. For example, when recreating an earthquake on the metaverse, the disaster reproduction unit reproduces in detail the internal structure of a building and the arrangement of furniture. For example, it realistically reproduces how furniture falls and how building walls crack. Furthermore, when recreating a fire on the metaverse, the disaster reproduction unit reproduces in detail the internal structure of a building and the arrangement of furniture. For example, it realistically reproduces how furniture burns and how smoke fills the building. Furthermore, when recreating a flood on the metaverse, the disaster reproduction unit reproduces in detail the internal structure of a building and the arrangement of furniture. For example, it realistically reproduces how water enters a building and how furniture floats. In this way, by recreating even the internal structure of a building and the arrangement of furniture in detail, a more realistic disaster experience can be provided to the user.

[0035] The disaster reenactment unit can add sensory information such as sound and vibration when reenacting a disaster, thereby providing a realistic experience. For example, when reenacting an earthquake on the metaverse, the disaster reenactment unit realistically reproduces the shaking and vibration of a building, allowing the user to, for example, feel the shaking of an earthquake. Furthermore, when reenacting a fire on the metaverse, the unit realistically reproduces the sound of flames and the smell of smoke, allowing the user to, for example, feel the presence of a fire. Furthermore, when reenacting a flood on the metaverse, the unit realistically reproduces the sound and coldness of flowing water, allowing the user to, for example, feel the fear of a flood. In this way, by adding sensory information such as sound and vibration, a more realistic disaster experience can be provided to the user.

[0036] The disaster reenactment unit can combine different weather conditions to reenact a disaster on the metaverse. For example, when reenacting an earthquake on the metaverse, the disaster reenactment unit combines weather conditions such as rain and snow to reenact the disaster. For example, it provides a scenario in which heavy rain falls when an earthquake occurs. When reenacting a fire on the metaverse, it combines weather conditions such as wind and fog to reenact the disaster. For example, it provides a scenario in which strong winds blow when a fire occurs. When reenacting a flood on the metaverse, it combines weather conditions such as lightning and storms to reenact the disaster. For example, it provides a scenario in which thunder roars when a flood occurs. In this way, by reenacting different weather conditions in combination, it is possible to provide users with a more diverse disaster experience.

[0037] The disaster reenactment unit can introduce a mode in which multiple users can participate simultaneously when reenacting a disaster. For example, the disaster reenactment unit introduces a multi-user mode in which multiple users can participate simultaneously in an earthquake reenactment on the metaverse. For example, an evacuation drill can be conducted with family and friends. Also, a multi-user mode in which multiple users can participate simultaneously in a fire reenactment on the metaverse. For example, a firefighting drill can be conducted with colleagues at work. Also, a multi-user mode in which multiple users can participate simultaneously in a flood reenactment on the metaverse. For example, an evacuation drill can be conducted with local residents. By introducing a multi-user mode in which multiple users can participate simultaneously, cooperation and collaboration between users can be strengthened, and more effective disaster prevention training can be provided.

[0038] The training provision unit can use the generation AI to analyze the user's past training data and provide a program optimized for each individual user. For example, the generation AI analyzes the user's past evacuation drill data and learns patterns such as evacuation route selection and evacuation speed. This provides an evacuation drill program optimized for the user. The generation AI also analyzes the user's past fire extinguishing drill data and learns patterns such as how to use a fire extinguisher and extinguishing speed. This provides an optimized fire extinguishing training program for the user. The generation AI also analyzes the user's past first aid training data and learns patterns such as first aid procedures and speed. This provides an optimized first aid training program for the user. This allows for more effective disaster prevention training by analyzing the user's past training data and providing an optimized training program for each individual user.

[0039] The training providing unit can evaluate the user's behavior in real time during training and provide feedback. In the training providing unit, for example, the generation AI evaluates the user's evacuation behavior in real time and provides immediate feedback on the selection and speed of the evacuation route. For example, the training providing unit points out areas for improvement in the evacuation route. The generation AI also evaluates the user's fire extinguishing behavior in real time and provides immediate feedback on how to use a fire extinguisher and the speed. For example, the generation AI provides instruction on how to use a fire extinguisher correctly. The generation AI also evaluates the user's first aid behavior in real time and provides immediate feedback on the procedure and speed. For example, the generation AI points out areas for improvement in the first aid procedure. In this way, by evaluating the user's behavior in real time and providing immediate feedback, the user's disaster prevention skills can be rapidly improved.

[0040] The training provision unit can use the generation AI to provide comprehensive training that combines different disaster response methods. For example, the generation AI provides a comprehensive training program that combines evacuation, rescue, and first aid. For example, the generation AI continuously trains evacuation after an earthquake and subsequent first aid. The generation AI also provides a training program that combines evacuation after a fire and firefighting activities in the event of a fire. For example, the generation AI continuously trains evacuation after a fire and subsequent firefighting activities. The generation AI also provides a training program that combines evacuation after a fire and rescue activities in the event of a flood. For example, the generation AI continuously trains evacuation after a flood and subsequent rescue activities. In this way, by providing comprehensive training that combines different disaster response methods, the user's disaster prevention skills can be improved comprehensively.

[0041] The training providing unit can record the user's actions during training and provide a report later. In the training providing unit, for example, the generation AI records the user's evacuation actions and provides a detailed report after the training is completed. For example, a report is created including an evaluation of the selection of an evacuation route and the speed. The generation AI also records the user's fire extinguishing actions and provides a detailed report after the training is completed. For example, a report is created including an evaluation of the method of using a fire extinguisher and the speed. The generation AI also records the user's first aid actions and provides a detailed report after the training is completed. For example, a report is created including an evaluation of the first aid procedure and the speed. In this way, by recording the user's actions and providing a detailed report later, it is possible to evaluate the effectiveness of the user's training and identify areas for improvement.

[0042] The training provider can use the generation AI to provide customized scenarios according to the characteristics and needs of each organization. For example, the generation AI provides disaster prevention scenarios according to the characteristics and needs of a local government. For example, it generates scenarios that take into account the geographical information and population density of the area. The generation AI also provides disaster prevention scenarios according to the characteristics and needs of a company. For example, it generates scenarios that take into account the layout of a factory or office. The generation AI also provides disaster prevention scenarios according to the characteristics and needs of a school. For example, it generates scenarios that take into account the structure of a school building and the number of students. In this way, disaster prevention training that is optimal for each organization can be realized by providing disaster prevention scenarios customized according to the characteristics and needs of each organization.

[0043] The training provision department can analyze disaster prevention training data for each organization and identify common issues and areas for improvement. For example, the generation AI analyzes disaster prevention training data from a local government and identifies common issues and areas for improvement. For example, it extracts issues regarding the selection of evacuation routes and evacuation speed. The generation AI also analyzes disaster prevention training data from a company and identifies common issues and areas for improvement. For example, it extracts issues regarding how to use fire extinguishers and the speed at which fires should be extinguished. The generation AI also analyzes disaster prevention training data from a school and identifies common issues and areas for improvement. For example, it extracts issues regarding first aid procedures and speed. In this way, by analyzing disaster prevention training data for each organization and identifying common issues and areas for improvement, the disaster prevention capabilities of the entire organization can be improved.

[0044] The training provision department can use the generation AI to support the coordination of disaster prevention drills between different organizations and conduct joint drills. For example, the generation AI supports the coordination of disaster prevention drills between local governments and companies and conducts joint drills. For example, local residents and company employees conduct evacuation drills together. The generation AI also supports the coordination of disaster prevention drills between companies and schools and conducts joint drills. For example, company employees and students conduct firefighting drills together. The generation AI also supports the coordination of disaster prevention drills between local governments and schools and conducts joint drills. For example, local residents and students conduct first aid drills together. In this way, by supporting the coordination of disaster prevention drills between different organizations and conducting joint drills, it is possible to improve the disaster prevention capabilities of the entire region.

[0045] The training provider department can share the results of disaster prevention drills conducted by each organization and introduce optimal methods. For example, the generation AI analyzes the results of disaster prevention drills conducted by local governments and introduces best practices. For example, it shares excellent methods for selecting evacuation routes and evacuation speed. The generation AI also analyzes the results of disaster prevention drills conducted by companies and introduces best practices. For example, it shares excellent methods for using fire extinguishers and the speed at which fires should be extinguished. The generation AI also analyzes the results of disaster prevention drills conducted by schools and introduces best practices. For example, it shares excellent methods for first aid procedures and speed. In this way, by sharing the results of disaster prevention drills conducted by each organization and introducing best practices, the disaster prevention capabilities of the entire organization can be improved.

[0046] The training provider can use the generation AI to analyze disaster prevention data for the entire region and identify risks specific to the region. For example, the generation AI analyzes earthquake data for the region and identifies earthquake risks specific to the region. For example, the risk is assessed by analyzing patterns of epicenters and seismic intensity. The generation AI also analyzes flood data for the region and identifies flood risks specific to the region. For example, the risk is assessed by analyzing patterns of rainfall and river water levels. The generation AI also analyzes fire data for the region and identifies fire risks specific to the region. For example, the risk is assessed by analyzing patterns of ignition causes and burning speeds. In this way, by analyzing disaster prevention data for the entire region and identifying risks specific to the region, the disaster prevention capabilities of the entire region can be improved.

[0047] The training provision unit can use the generation AI to provide a program to raise the disaster prevention awareness of the local residents. For example, the generation AI provides a disaster prevention education program for local residents. For example, it creates a program to learn evacuation methods and first aid procedures in the event of an earthquake. The generation AI also plans and implements disaster prevention workshops for local residents. For example, it conducts evacuation drills using actual disaster scenarios. The generation AI also provides disaster prevention information for local residents. For example, it provides information on contact points and evacuation locations in the event of a disaster. In this way, by providing an educational program to raise the disaster prevention awareness of local residents, it is possible to improve the disaster prevention capabilities of the entire region.

[0048] The training provision unit can use the generation AI to coordinate disaster prevention drills for the entire region and conduct resident-participation drills. For example, the training provision unit uses the generation AI to coordinate disaster prevention drills for the entire region and conduct resident-participation evacuation drills. For example, it provides a scenario in which local residents evacuate all at once. The generation AI also coordinates disaster prevention drills for the entire region and conducts resident-participation firefighting drills. For example, it provides a scenario in which local residents cooperate to extinguish a fire. The generation AI also coordinates disaster prevention drills for the entire region and conducts resident-participation first aid drills. For example, it provides a scenario in which local residents cooperate to perform first aid. In this way, by coordinating disaster prevention drills for the entire region and conducting resident-participation drills, the disaster prevention capabilities of the entire region can be improved.

[0049] The training provision unit can use the generation AI to plan and carry out a campaign to raise disaster prevention awareness among local residents. For example, the generation AI plans and implements a campaign to raise disaster prevention awareness among local residents. For example, an event is held to coincide with Disaster Prevention Day. The generation AI also plans and implements a campaign to provide disaster prevention information to local residents. For example, information on contact details and evacuation locations is provided in the event of a disaster. The generation AI also plans and implements a disaster prevention education campaign for local residents. For example, posters and videos are created to raise awareness of the importance of disaster prevention drills. In this way, by planning and implementing a campaign to raise disaster prevention awareness among local residents, the disaster prevention capabilities of the entire region can be improved.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The disaster prevention simulation system can further include a health management unit that monitors the user's health condition. For example, the health management unit can monitor the user's heart rate and blood pressure in real time, and if any abnormalities are detected, the health management unit can halt training and encourage appropriate rest. The health management unit can also analyze the user's past health data and adjust the intensity of training. For example, it can provide low-intensity training to users with heart disease. Furthermore, the health management unit can provide a post-training recovery program based on the user's health condition. For example, it can provide guidance on stretching and relaxation. This allows for safe and effective disaster prevention training by providing training that takes the user's health condition into consideration.

[0052] The disaster prevention simulation system can further include an action recording unit that records the user's action history. For example, the action recording unit can record what disaster scenarios the user has experienced in the past and reflect this in the next training. The action recording unit can also record the user's actions during training in detail and analyze them later. For example, it can collect data on the selection of evacuation routes and evacuation speed. Furthermore, the action recording unit can evaluate the effectiveness of the training based on the user's action data and provide feedback. For example, it can point out areas for improvement in evacuation behavior. In this way, the user's action history can be utilized to provide more effective disaster prevention training.

[0053] The disaster prevention simulation system may further include a communication training section for improving the user's communication skills. For example, the communication training section may provide scenarios for the user to communicate effectively with other users during a disaster. The communication training section may also evaluate the user's communication skills and provide feedback. For example, the communication training section may provide guidance on how to transmit evacuation instructions and how to share information. Furthermore, the communication training section may provide training for the user to demonstrate leadership during a disaster. For example, the communication training section may simulate the role of an evacuation group leader. This may improve the user's communication skills and strengthen cooperation during a disaster.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The disaster scenario generation unit uses the generation AI to generate a disaster scenario. For example, the generation AI analyzes the instructions for the disaster scenario input by the user and understands their content. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a disaster scenario based on that prompt. Step 2: The disaster reenactment unit recreates a disaster in the metaverse based on the disaster scenario generated by the disaster scenario generation unit. For example, if a magnitude 7 earthquake occurs, it realistically recreates the shaking and collapse of buildings. It also recreates the sinking of the ground and the tilting of buildings when liquefaction occurs. If a fire breaks out, it realistically recreates the spread of flames and smoke. Step 3: The training provider provides training to users for the disasters recreated by the disaster reconstruction unit. For example, it provides guidance on choosing an evacuation route in the event of an earthquake or on firefighting procedures in the event of a fire. The generation AI analyzes the user's behavior in real time and provides appropriate advice and feedback. This allows users to acquire the skills they need to respond appropriately in the event of a real disaster.

[0056] (Example 2) A disaster prevention simulation system according to an embodiment of the present invention uses a generation AI to generate realistic disaster scenarios in the metaverse, recreates disasters, and provides training to users. This allows the disaster prevention simulation system to train users to respond appropriately to various disaster scenarios, thereby improving disaster prevention capabilities across the entire region.

[0057] A disaster prevention simulation system according to an embodiment includes a disaster scenario generation unit, a disaster reproduction unit, and a training provision unit. The disaster scenario generation unit generates a disaster scenario using a generation AI. For example, the generation AI analyzes instructions for a disaster scenario input by a user and understands the contents of the instructions. The generation AI receives input in the form of prompts containing instructions for the user's intended actions, and the generation AI generates a disaster scenario based on the prompts. The disaster reproduction unit reproduces a disaster in the metaverse based on the disaster scenario generated by the disaster scenario generation unit. For example, in the event of a magnitude 7 earthquake, the unit realistically reproduces the shaking and collapse of buildings. In addition, in the event of liquefaction, the unit realistically reproduces the sinking of the ground and the tilting of buildings. In the event of a fire, the unit realistically reproduces the spread of flames and smoke. The training provision unit provides training to the user on the disaster reproduced by the disaster reproduction unit. For example, the unit instructs the user on evacuation route selection in the event of an earthquake and firefighting procedures in the event of a fire. The generation AI analyzes the user's behavior in real time and provides appropriate advice and feedback. This allows users to acquire the skills to respond appropriately in the event of a real disaster. As a result, the disaster prevention simulation system according to the embodiment provides training for users to respond appropriately to various disaster scenarios, thereby improving disaster prevention capabilities across the entire region.

[0058] The disaster scenario generation unit can analyze past disaster data and learn patterns of actual disasters to generate realistic scenarios. For example, the generation AI of the disaster scenario generation unit analyzes past earthquake data and learns patterns such as seismic intensity, epicenter, and damage extent. This allows it to recreate actual earthquake scenarios and provide users with a realistic experience. The generation AI also analyzes past flood data and learns patterns such as rainfall, river water levels, and damage extent. This allows it to recreate actual flood scenarios and provide users with a realistic experience. The generation AI also analyzes past fire data and learns patterns such as ignition cause, burning speed, and damage extent. This allows it to recreate actual fire scenarios and provide users with a realistic experience. In this way, by analyzing past disaster data and generating realistic scenarios, it is possible to provide users with more realistic disaster prevention training.

[0059] The disaster scenario generation unit can incorporate structural information about the user's residence or building and generate scenarios that reflect regional disaster risks. For example, the generation AI incorporates geographic information about the user's residence and generates scenarios that reflect regional disaster risks, such as earthquakes and tsunamis. For example, a tsunami scenario is provided for a user living in a coastal area. The generation AI also incorporates structural information about the user's building and generates scenarios that reflect regional disaster risks, such as earthquakes and fires. For example, a fire scenario is provided for a user living in a wooden building. The generation AI also incorporates meteorological data about the user's residence and generates scenarios that reflect regional disaster risks, such as typhoons and heavy rains. For example, a typhoon scenario is provided for a user living in an area prone to typhoons. In this way, by generating scenarios that reflect the user's residence and structural information about the building, training for regional disaster risks can be provided.

[0060] The disaster scenario generation unit can use the emotion estimation function to generate scenarios with difficulty levels according to the user's emotional state and stress level. For example, the disaster scenario generation unit monitors the user's emotional state in real time using the generation AI, and generates a scenario with a low level of difficulty if the stress level is high. For example, it provides a simple evacuation scenario to a first-time user. The generation AI also analyzes the user's emotional state and generates a scenario with a high level of difficulty if the stress level is low. For example, it provides a complex disaster scenario to an experienced user. The generation AI also takes the user's emotional state into consideration and provides scenarios in stages according to the stress level. For example, it generates scenarios with gradually increasing difficulty. In this way, it is possible to provide training that is appropriate for the user by taking the user's emotional state into consideration and generating scenarios with a level of difficulty according to the stress level.

[0061] The disaster scenario generation unit can generate complex disaster scenarios that combine different disasters. In the disaster scenario generation unit, for example, the generation AI generates a complex disaster scenario that combines a flood and an earthquake. For example, it provides a scenario in which an earthquake causes a levee to collapse, followed by a flood. The generation AI also generates a complex disaster scenario that combines a tsunami and a fire. For example, it provides a scenario in which a tsunami causes a gas pipe to burst, followed by a fire. The generation AI also generates a complex disaster scenario that combines a typhoon and a landslide. For example, it provides a scenario in which a typhoon causes continuous heavy rain, followed by a landslide. In this way, by generating complex disaster scenarios that combine different disasters, it is possible to provide more complex disaster response training to users.

[0062] The disaster scenario generation unit can learn user feedback on disaster scenarios and improve the accuracy of the scenarios. In the disaster scenario generation unit, for example, the generation AI collects user feedback and improves the accuracy of the scenario. For example, the user evaluates the realism of the scenario and improves the scenario based on that evaluation. The generation AI also analyzes user behavior data and improves the accuracy of the scenario. For example, it learns the behavior of users when selecting an evacuation route and generates a more realistic scenario. The generation AI also adjusts the difficulty of the scenario based on user feedback. For example, if the user is unable to complete the scenario, it provides a scenario with a lower difficulty level. In this way, the system can learn user feedback and improve the accuracy of the scenario, thereby providing more realistic disaster prevention training.

[0063] The disaster scenario generation unit uses the emotion estimation function to generate the disaster scenario that the user finds most frightening and allows the user to learn how to deal with it. For example, the generation AI in the disaster scenario generation unit analyzes the user's emotional state and generates the most frightening disaster scenario. For example, a user who is afraid of heights may be provided with an earthquake scenario in a high-rise building. The generation AI also provides frightening disaster scenarios in stages based on the user's emotional response. For example, it gradually increases the number of frightening scenarios. The generation AI also analyzes the user's emotional data and allows the user to learn how to deal with frightening disaster scenarios. For example, it may instruct the user on how to evacuate in the event of a fire. In this way, the generation AI generates the most frightening disaster scenario for the user and allows the user to learn how to deal with it, thereby improving the user's disaster prevention skills.

[0064] The disaster reproduction unit can reproduce in detail the internal structure of a building and the arrangement of furniture when recreating a disaster on the metaverse. For example, when recreating an earthquake on the metaverse, the disaster reproduction unit reproduces in detail the internal structure of a building and the arrangement of furniture. For example, it realistically reproduces how furniture falls and how building walls crack. Furthermore, when recreating a fire on the metaverse, the disaster reproduction unit reproduces in detail the internal structure of a building and the arrangement of furniture. For example, it realistically reproduces how furniture burns and how smoke fills the building. Furthermore, when recreating a flood on the metaverse, the disaster reproduction unit reproduces in detail the internal structure of a building and the arrangement of furniture. For example, it realistically reproduces how water enters a building and how furniture floats. In this way, by recreating even the internal structure of a building and the arrangement of furniture in detail, a more realistic disaster experience can be provided to the user.

[0065] The disaster reenactment unit can add sensory information such as sound and vibration when reenacting a disaster, thereby providing a realistic experience. For example, when reenacting an earthquake on the metaverse, the disaster reenactment unit realistically reproduces the shaking and vibration of a building, allowing the user to, for example, feel the shaking of an earthquake. Furthermore, when reenacting a fire on the metaverse, the unit realistically reproduces the sound of flames and the smell of smoke, allowing the user to, for example, feel the presence of a fire. Furthermore, when reenacting a flood on the metaverse, the unit realistically reproduces the sound and coldness of flowing water, allowing the user to, for example, feel the fear of a flood. In this way, by adding sensory information such as sound and vibration, a more realistic disaster experience can be provided to the user.

[0066] The disaster reenactment unit can use the emotion estimation function to monitor the user's fear and anxiety in real time and provide advice at the appropriate time. For example, while a disaster is being reenacted on the metaverse, the disaster reenactment unit uses the emotion estimation function to monitor the user's fear and anxiety in real time. For example, it analyzes the user's heart rate and facial expressions. Furthermore, when the user's fear or anxiety increases, it uses the emotion estimation function to provide advice at the appropriate time. For example, it displays a message encouraging deep breathing. Furthermore, it uses the emotion estimation function to provide advice in real time to reduce the user's fear and anxiety. For example, it provides evacuation route guidance and shows safe places. In this way, by monitoring the user's fear and anxiety in real time and providing advice at the appropriate time, it is possible to reduce the user's stress and provide effective training.

[0067] The disaster reenactment unit can combine different weather conditions to reenact a disaster on the metaverse. For example, when reenacting an earthquake on the metaverse, the disaster reenactment unit combines weather conditions such as rain and snow to reenact the disaster. For example, it provides a scenario in which heavy rain falls when an earthquake occurs. When reenacting a fire on the metaverse, it combines weather conditions such as wind and fog to reenact the disaster. For example, it provides a scenario in which strong winds blow when a fire occurs. When reenacting a flood on the metaverse, it combines weather conditions such as lightning and storms to reenact the disaster. For example, it provides a scenario in which thunder roars when a flood occurs. In this way, by reenacting different weather conditions in combination, it is possible to provide users with a more diverse disaster experience.

[0068] The disaster reenactment unit can introduce a mode in which multiple users can participate simultaneously when reenacting a disaster. For example, the disaster reenactment unit introduces a multi-user mode in which multiple users can participate simultaneously in an earthquake reenactment on the metaverse. For example, an evacuation drill can be conducted with family and friends. Also, a multi-user mode in which multiple users can participate simultaneously in a fire reenactment on the metaverse. For example, a firefighting drill can be conducted with colleagues at work. Also, a multi-user mode in which multiple users can participate simultaneously in a flood reenactment on the metaverse. For example, an evacuation drill can be conducted with local residents. By introducing a multi-user mode in which multiple users can participate simultaneously, cooperation and collaboration between users can be strengthened, and more effective disaster prevention training can be provided.

[0069] The disaster re-enactment unit can use the emotion estimation function to identify a disaster scenario that the user feels most real and re-enact that scenario. For example, the disaster re-enactment unit can use the emotion estimation function to identify an earthquake scenario that the user feels most real and focus on re-enacting that scenario. For example, the scenario can be adjusted based on the user's fear response. Also, the emotion estimation function can be used to identify a fire scenario that the user feels most real and focus on re-enacting that scenario. For example, the scenario can be adjusted based on the user's anxiety response. Also, the emotion estimation function can be used to identify a flood scenario that the user feels most real and focus on re-enacting that scenario. For example, the scenario can be adjusted based on the user's stress response. In this way, by identifying a disaster scenario that the user feels most real and focus on re-enacting that scenario, more effective disaster prevention training can be provided to the user.

[0070] The training provision unit can use the generation AI to analyze the user's past training data and provide a program optimized for each individual user. For example, the generation AI analyzes the user's past evacuation drill data and learns patterns such as evacuation route selection and evacuation speed. This provides an evacuation drill program optimized for the user. The generation AI also analyzes the user's past fire extinguishing drill data and learns patterns such as how to use a fire extinguisher and extinguishing speed. This provides an optimized fire extinguishing training program for the user. The generation AI also analyzes the user's past first aid training data and learns patterns such as first aid procedures and speed. This provides an optimized first aid training program for the user. This allows for more effective disaster prevention training by analyzing the user's past training data and providing an optimized training program for each individual user.

[0071] The training providing unit can evaluate the user's behavior in real time during training and provide feedback. In the training providing unit, for example, the generation AI evaluates the user's evacuation behavior in real time and provides immediate feedback on the selection and speed of the evacuation route. For example, the training providing unit points out areas for improvement in the evacuation route. The generation AI also evaluates the user's fire extinguishing behavior in real time and provides immediate feedback on how to use a fire extinguisher and the speed. For example, the generation AI provides instruction on how to use a fire extinguisher correctly. The generation AI also evaluates the user's first aid behavior in real time and provides immediate feedback on the procedure and speed. For example, the generation AI points out areas for improvement in the first aid procedure. In this way, by evaluating the user's behavior in real time and providing immediate feedback, the user's disaster prevention skills can be rapidly improved.

[0072] The training provision unit can use the emotion estimation function to monitor the user's stress level and recommend breaks at appropriate times. In the training provision unit, for example, the generation AI monitors the user's stress level in real time and suggests breaks when stress increases. For example, the stress level is determined by analyzing heart rate and facial expressions. The emotion estimation function also analyzes the user's stress level and suggests relaxation methods at appropriate times. For example, messages encouraging deep breathing and stretching are displayed. The generation AI also monitors the user's stress level and suggests breaks based on the progress of training. For example, it instructs the user to take breaks at regular intervals. In this way, by monitoring the user's stress level and suggesting breaks at appropriate times, it is possible to provide effective training while maintaining the user's health.

[0073] The training provision unit can use the generation AI to provide comprehensive training that combines different disaster response methods. For example, the generation AI provides a comprehensive training program that combines evacuation, rescue, and first aid. For example, the generation AI continuously trains evacuation after an earthquake and subsequent first aid. The generation AI also provides a training program that combines evacuation after a fire and firefighting activities in the event of a fire. For example, the generation AI continuously trains evacuation after a fire and subsequent firefighting activities. The generation AI also provides a training program that combines evacuation after a fire and rescue activities in the event of a flood. For example, the generation AI continuously trains evacuation after a flood and subsequent rescue activities. In this way, by providing comprehensive training that combines different disaster response methods, the user's disaster prevention skills can be improved comprehensively.

[0074] The training providing unit can record the user's actions during training and provide a report later. In the training providing unit, for example, the generation AI records the user's evacuation actions and provides a detailed report after the training is completed. For example, a report is created including an evaluation of the selection of an evacuation route and the speed. The generation AI also records the user's fire extinguishing actions and provides a detailed report after the training is completed. For example, a report is created including an evaluation of the method of using a fire extinguisher and the speed. The generation AI also records the user's first aid actions and provides a detailed report after the training is completed. For example, a report is created including an evaluation of the first aid procedure and the speed. In this way, by recording the user's actions and providing a detailed report later, it is possible to evaluate the effectiveness of the user's training and identify areas for improvement.

[0075] The training providing unit can use the emotion estimation function to identify a situation in which the user feels the most anxiety and provide training on how to deal with that situation. For example, the training providing unit can use the emotion estimation function to identify an evacuation situation in which the user feels the most anxiety and provide focused training on how to deal with that situation. For example, the training providing unit can provide instruction on how to evacuate in a narrow passage. Also, the emotion estimation function can be used to identify a fire-fighting situation in which the user feels the most anxiety and provide focused training on how to deal with that situation. For example, the training can provide instruction on how to extinguish a fire in a situation where the fire is intense. Also, the emotion estimation function can be used to identify a first aid situation in which the user feels the most anxiety and provide focused training on how to deal with that situation. For example, the training can provide instruction on how to provide first aid to a seriously injured person. In this way, the training unit can identify a situation in which the user feels the most anxiety and provide focused training on how to deal with that situation, thereby effectively improving the user's disaster prevention skills.

[0076] The training provider can use the generation AI to provide customized scenarios according to the characteristics and needs of each organization. For example, the generation AI provides disaster prevention scenarios according to the characteristics and needs of a local government. For example, it generates scenarios that take into account the geographical information and population density of the area. The generation AI also provides disaster prevention scenarios according to the characteristics and needs of a company. For example, it generates scenarios that take into account the layout of a factory or office. The generation AI also provides disaster prevention scenarios according to the characteristics and needs of a school. For example, it generates scenarios that take into account the structure of a school building and the number of students. In this way, disaster prevention training that is optimal for each organization can be realized by providing disaster prevention scenarios customized according to the characteristics and needs of each organization.

[0077] The training provision department can analyze disaster prevention training data for each organization and identify common issues and areas for improvement. For example, the generation AI analyzes disaster prevention training data from a local government and identifies common issues and areas for improvement. For example, it extracts issues regarding the selection of evacuation routes and evacuation speed. The generation AI also analyzes disaster prevention training data from a company and identifies common issues and areas for improvement. For example, it extracts issues regarding how to use fire extinguishers and the speed at which fires should be extinguished. The generation AI also analyzes disaster prevention training data from a school and identifies common issues and areas for improvement. For example, it extracts issues regarding first aid procedures and speed. In this way, by analyzing disaster prevention training data for each organization and identifying common issues and areas for improvement, the disaster prevention capabilities of the entire organization can be improved.

[0078] The training providing unit can use the emotion estimation function to monitor the emotional reactions of the participants and measure the effectiveness of the training. For example, the training providing unit uses the emotion estimation function to monitor the emotional reactions of participants in a local government disaster prevention training and evaluate the effectiveness of the training. For example, the stress level during the training is analyzed. The emotion estimation function can also be used to monitor the emotional reactions of participants in a corporate disaster prevention training and evaluate the effectiveness of the training. For example, the sense of relief after the training is analyzed. The emotion estimation function can also be used to monitor the emotional reactions of participants in a school disaster prevention training and evaluate the effectiveness of the training. For example, the sense of anxiety during the training is analyzed. In this way, by monitoring the emotional reactions of the participants and evaluating the effectiveness of the training, more effective disaster prevention training can be provided.

[0079] The training provision department can use the generation AI to support the coordination of disaster prevention drills between different organizations and conduct joint drills. For example, the generation AI supports the coordination of disaster prevention drills between local governments and companies and conducts joint drills. For example, local residents and company employees conduct evacuation drills together. The generation AI also supports the coordination of disaster prevention drills between companies and schools and conducts joint drills. For example, company employees and students conduct firefighting drills together. The generation AI also supports the coordination of disaster prevention drills between local governments and schools and conducts joint drills. For example, local residents and students conduct first aid drills together. In this way, by supporting the coordination of disaster prevention drills between different organizations and conducting joint drills, it is possible to improve the disaster prevention capabilities of the entire region.

[0080] The training provider department can share the results of disaster prevention drills conducted by each organization and introduce optimal methods. For example, the generation AI analyzes the results of disaster prevention drills conducted by local governments and introduces best practices. For example, it shares excellent methods for selecting evacuation routes and evacuation speed. The generation AI also analyzes the results of disaster prevention drills conducted by companies and introduces best practices. For example, it shares excellent methods for using fire extinguishers and the speed at which fires should be extinguished. The generation AI also analyzes the results of disaster prevention drills conducted by schools and introduces best practices. For example, it shares excellent methods for first aid procedures and speed. In this way, by sharing the results of disaster prevention drills conducted by each organization and introducing best practices, the disaster prevention capabilities of the entire organization can be improved.

[0081] The training providing unit can use the emotion estimation function to analyze the emotion data of the training participants and use it to improve the training program. The training providing unit, for example, uses the emotion estimation function to analyze the emotion data of participants in a local government disaster prevention training and use it to improve the training program. For example, the program is adjusted based on stress levels during the training. The emotion estimation function can also be used to analyze the emotion data of participants in a corporate disaster prevention training and use it to improve the training program. For example, the program is adjusted based on a sense of relief after the training. The emotion estimation function can also be used to analyze the emotion data of participants in a school disaster prevention training and use it to improve the training program. For example, the program is adjusted based on feelings of anxiety during the training. In this way, by analyzing the emotion data of the training participants and using it to improve the training program, more effective disaster prevention training can be provided.

[0082] The training provider can use the generation AI to analyze disaster prevention data for the entire region and identify risks specific to the region. For example, the generation AI analyzes earthquake data for the region and identifies earthquake risks specific to the region. For example, the risk is assessed by analyzing patterns of epicenters and seismic intensity. The generation AI also analyzes flood data for the region and identifies flood risks specific to the region. For example, the risk is assessed by analyzing patterns of rainfall and river water levels. The generation AI also analyzes fire data for the region and identifies fire risks specific to the region. For example, the risk is assessed by analyzing patterns of ignition causes and burning speeds. In this way, by analyzing disaster prevention data for the entire region and identifying risks specific to the region, the disaster prevention capabilities of the entire region can be improved.

[0083] The training provision unit can use the generation AI to provide a program to raise the disaster prevention awareness of the local residents. For example, the generation AI provides a disaster prevention education program for local residents. For example, it creates a program to learn evacuation methods and first aid procedures in the event of an earthquake. The generation AI also plans and implements disaster prevention workshops for local residents. For example, it conducts evacuation drills using actual disaster scenarios. The generation AI also provides disaster prevention information for local residents. For example, it provides information on contact points and evacuation locations in the event of a disaster. In this way, by providing an educational program to raise the disaster prevention awareness of local residents, it is possible to improve the disaster prevention capabilities of the entire region.

[0084] The training provider can use the emotion estimation function to monitor changes in the disaster prevention awareness of the local residents and introduce effective methods. The training provider, for example, uses the emotion estimation function to monitor changes in the disaster prevention awareness of the local residents. For example, the emotion estimation function is used to analyze the emotional state before and after a disaster prevention drill and evaluate changes in awareness. The emotion estimation function is also used to evaluate the effectiveness of disaster prevention education for the local residents. For example, the emotion estimation function is used to analyze emotional reactions after an educational program is implemented and identify effective methods. The emotion estimation function is also used to introduce effective educational methods to raise the disaster prevention awareness of the local residents. For example, the educational program is adjusted based on the emotion data. In this way, by monitoring changes in the disaster prevention awareness of the local residents and introducing effective educational methods, the disaster prevention capabilities of the entire region can be improved.

[0085] The training provision unit can use the generation AI to coordinate disaster prevention drills for the entire region and conduct resident-participation drills. For example, the training provision unit uses the generation AI to coordinate disaster prevention drills for the entire region and conduct resident-participation evacuation drills. For example, it provides a scenario in which local residents evacuate all at once. The generation AI also coordinates disaster prevention drills for the entire region and conducts resident-participation firefighting drills. For example, it provides a scenario in which local residents cooperate to extinguish a fire. The generation AI also coordinates disaster prevention drills for the entire region and conducts resident-participation first aid drills. For example, it provides a scenario in which local residents cooperate to perform first aid. In this way, by coordinating disaster prevention drills for the entire region and conducting resident-participation drills, the disaster prevention capabilities of the entire region can be improved.

[0086] The training provision unit can use the generation AI to plan and carry out a campaign to raise disaster prevention awareness among local residents. For example, the generation AI plans and implements a campaign to raise disaster prevention awareness among local residents. For example, an event is held to coincide with Disaster Prevention Day. The generation AI also plans and implements a campaign to provide disaster prevention information to local residents. For example, information on contact details and evacuation locations is provided in the event of a disaster. The generation AI also plans and implements a disaster prevention education campaign for local residents. For example, posters and videos are created to raise awareness of the importance of disaster prevention drills. In this way, by planning and implementing a campaign to raise disaster prevention awareness among local residents, the disaster prevention capabilities of the entire region can be improved.

[0087] The training provider can use the emotion estimation function to evaluate the improvement in disaster prevention awareness of the local residents in real time and recommend effective measures. The training provider, for example, uses the emotion estimation function to evaluate the improvement in disaster prevention awareness of the local residents in real time. For example, the emotion estimation function analyzes the emotional state during disaster prevention training and evaluates changes in awareness. The emotion estimation function also evaluates the effectiveness of improving the disaster prevention awareness of the local residents. For example, the emotion estimation function analyzes emotional reactions after implementing an educational program and identifies effective measures. The emotion estimation function also uses the function to propose effective measures to raise the disaster prevention awareness of the local residents. For example, the emotion data can be used to adjust the educational program and training content. This allows the improvement in disaster prevention awareness of the local residents to be evaluated in real time and effective measures can be proposed, thereby improving the disaster prevention capabilities of the entire region.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The disaster prevention simulation system can further include a health management unit that monitors the user's health condition. For example, the health management unit can monitor the user's heart rate and blood pressure in real time, and if any abnormalities are detected, the health management unit can halt training and encourage appropriate rest. The health management unit can also analyze the user's past health data and adjust the intensity of training. For example, it can provide low-intensity training to users with heart disease. Furthermore, the health management unit can provide a post-training recovery program based on the user's health condition. For example, it can provide guidance on stretching and relaxation. This allows for safe and effective disaster prevention training by providing training that takes the user's health condition into consideration.

[0090] The disaster prevention simulation system can further include an action recording unit that records the user's action history. For example, the action recording unit can record what disaster scenarios the user has experienced in the past and reflect this in the next training. The action recording unit can also record the user's actions during training in detail and analyze them later. For example, it can collect data on the selection of evacuation routes and evacuation speed. Furthermore, the action recording unit can evaluate the effectiveness of the training based on the user's action data and provide feedback. For example, it can point out areas for improvement in evacuation behavior. In this way, the user's action history can be utilized to provide more effective disaster prevention training.

[0091] The disaster prevention simulation system may further include a communication training section for improving the user's communication skills. For example, the communication training section may provide scenarios for the user to communicate effectively with other users during a disaster. The communication training section may also evaluate the user's communication skills and provide feedback. For example, the communication training section may provide guidance on how to transmit evacuation instructions and how to share information. Furthermore, the communication training section may provide training for the user to demonstrate leadership during a disaster. For example, the communication training section may simulate the role of an evacuation group leader. This may improve the user's communication skills and strengthen cooperation during a disaster.

[0092] The disaster prevention simulation system can further include an emotion adjustment unit that adjusts the progress of the training based on the user's emotional state. For example, the emotion adjustment unit monitors the user's emotional state in real time and slows down the progress of the training if the stress level is high. The emotion adjustment unit can also analyze the user's emotional state and speed up the progress of the training if the stress level is low. Furthermore, the emotion adjustment unit can change the training content according to the user's emotional state. For example, it can provide a scenario for relaxation when stress levels are high. This allows for effective disaster prevention training by providing training that takes the user's emotional state into consideration.

[0093] The disaster prevention simulation system may further include an emotional feedback unit that provides training feedback based on the user's emotional state. For example, the emotional feedback unit may monitor the user's emotional state in real time and provide positive feedback when the user's stress level is high. The emotional feedback unit may also analyze the user's emotional state and provide detailed feedback when the user's stress level is low. Furthermore, the emotional feedback unit may adjust the content of the feedback according to the user's emotional state. For example, an encouraging message may be displayed when stress levels increase. This allows for effective disaster prevention training by providing feedback that takes the user's emotional state into consideration.

[0094] The disaster prevention simulation system may further include an emotional difficulty adjustment unit that adjusts the difficulty of the training based on the user's emotional state. For example, the emotional difficulty adjustment unit may monitor the user's emotional state in real time and provide a less difficult scenario when the user's stress level is high. The emotional difficulty adjustment unit may also analyze the user's emotional state and provide a more difficult scenario when the user's stress level is low. Furthermore, the emotional difficulty adjustment unit may adjust the progress of the training according to the user's emotional state. For example, the progress of the training may be slowed down when stress increases. This allows for effective disaster prevention training by providing training that takes the user's emotional state into consideration.

[0095] The disaster prevention simulation system can further include an emotion customization unit that customizes the content of the training based on the user's emotional state. For example, the emotion customization unit can monitor the user's emotional state in real time and provide a relaxing scenario when the user's stress level is high. The emotion customization unit can also analyze the user's emotional state and provide a challenging scenario when the user's stress level is low. Furthermore, the emotion customization unit can adjust the progress of the training according to the user's emotional state. For example, it can slow down the progress of the training when stress levels increase. This allows for effective disaster prevention training by providing training that takes the user's emotional state into consideration.

[0096] The disaster prevention simulation system may further include an emotional feedback unit that provides training feedback based on the user's emotional state. For example, the emotional feedback unit may monitor the user's emotional state in real time and provide positive feedback when the user's stress level is high. The emotional feedback unit may also analyze the user's emotional state and provide detailed feedback when the user's stress level is low. Furthermore, the emotional feedback unit may adjust the content of the feedback according to the user's emotional state. For example, an encouraging message may be displayed when stress levels increase. This allows for effective disaster prevention training by providing feedback that takes the user's emotional state into consideration.

[0097] The disaster prevention simulation system can further include an emotion adjustment unit that adjusts the progress of the training based on the user's emotional state. For example, the emotion adjustment unit monitors the user's emotional state in real time and slows down the progress of the training if the stress level is high. The emotion adjustment unit can also analyze the user's emotional state and speed up the progress of the training if the stress level is low. Furthermore, the emotion adjustment unit can change the training content according to the user's emotional state. For example, it can provide a scenario for relaxation when stress levels are high. This allows for effective disaster prevention training by providing training that takes the user's emotional state into consideration.

[0098] The disaster prevention simulation system may further include an emotional difficulty adjustment unit that adjusts the difficulty of the training based on the user's emotional state. For example, the emotional difficulty adjustment unit may monitor the user's emotional state in real time and provide a less difficult scenario when the user's stress level is high. The emotional difficulty adjustment unit may also analyze the user's emotional state and provide a more difficult scenario when the user's stress level is low. Furthermore, the emotional difficulty adjustment unit may adjust the progress of the training according to the user's emotional state. For example, the progress of the training may be slowed down when stress increases. This allows for effective disaster prevention training by providing training that takes the user's emotional state into consideration.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The disaster scenario generation unit uses the generation AI to generate a disaster scenario. For example, the generation AI analyzes the instructions for the disaster scenario input by the user and understands their content. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a disaster scenario based on that prompt. Step 2: The disaster reenactment unit recreates a disaster in the metaverse based on the disaster scenario generated by the disaster scenario generation unit. For example, if a magnitude 7 earthquake occurs, it realistically recreates the shaking and collapse of buildings. It also recreates the sinking of the ground and the tilting of buildings when liquefaction occurs. If a fire breaks out, it realistically recreates the spread of flames and smoke. Step 3: The training provider provides training to users for the disasters recreated by the disaster reconstruction unit. For example, it provides guidance on choosing an evacuation route in the event of an earthquake or on firefighting procedures in the event of a fire. The generation AI analyzes the user's behavior in real time and provides appropriate advice and feedback. This allows users to acquire the skills they need to respond appropriately in the event of a real disaster.

[0101] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a 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.

[0136] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a disaster scenario generation unit that generates a disaster scenario using a generation AI; a disaster reenactment unit that reenacts a disaster on a metaverse based on the disaster scenario generated by the disaster scenario generation unit; a training providing unit that provides training to a user for the disaster reproduced by the disaster reproduction unit. A system characterized by:

2. The disaster scenario generation unit Analyze past disaster data, learn patterns of actual disasters, and generate realistic scenarios 2. The system of claim 1.

3. The disaster scenario generation unit Generate scenarios that reflect the disaster risks specific to the region by incorporating information about the user's residence or building structure.

2. The system of claim 1.

4. The disaster scenario generation unit Generates scenarios of varying difficulty according to the user's stress level based on their emotional state 2. The system of claim 1.

5. The disaster scenario generation unit Generate complex disaster scenarios that combine different disasters 2. The system of claim 1.

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