System

Abstract

An object of a system according to an embodiment is to quickly present an appropriate evacuation route when a disaster occurs.SOLUTION: A system includes a disaster information collection part, a topographic information analysis part, and an evacuation route presentation part. The disaster information collection part collects the latest disaster occurrence information. The topographic information analysis part analyzes the information collected by the disaster information collection part. The evacuation route presentation unit presents an appropriate evacuation route based on the information analyzed by the terrain information analysis 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 technologies have had the problem of making it difficult to quickly present appropriate evacuation routes when a disaster occurs.

[0005] The system according to the embodiment aims to quickly present appropriate evacuation routes when a disaster occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes a disaster information collection unit, a topographical information analysis unit, and an evacuation route presentation unit. The disaster information collection unit collects the latest disaster occurrence information. The topographical information analysis unit analyzes the information collected by the disaster information collection unit. The evacuation route presentation unit presents an appropriate evacuation route based on the information analyzed by the topographical information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly present appropriate evacuation routes when a disaster occurs. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 response app according to an embodiment of the present invention analyzes the latest disaster occurrence information, topography, and hazard map information, and instantly shows appropriate evacuation routes and response methods, even to people who are unfamiliar with the area. This app uses generative AI to analyze information and provides users with optimal evacuation routes and response methods in real time. As a result, the disaster response app allows users to instantly find appropriate evacuation routes and response methods, even if they are unfamiliar with the area.

[0029] A disaster response app according to an embodiment includes a disaster information collection unit, a terrain information analysis unit, and an evacuation route presentation unit. The disaster information collection unit collects the latest disaster occurrence information. For example, the generation AI collects data on natural disasters such as earthquakes, floods, typhoons, and volcanic eruptions and analyzes it in real time. The generation AI analyzes information such as the epicenter, seismic intensity, and damage status, and provides the information to the user. The terrain information analysis unit analyzes the collected information. For example, the generation AI analyzes terrain data and hazard map information to assess disaster risk. The generation AI identifies areas at high risk of flooding and areas at risk of landslides and issues warnings to the user. The evacuation route presentation unit presents an appropriate evacuation route based on the analyzed information. For example, the generation AI calculates the safest evacuation route based on the user's current location and displays it on a map. The generation AI also simultaneously displays the locations of evacuation shelters and dangerous areas along the evacuation route. This allows the disaster response app to instantly learn appropriate evacuation routes and response methods, even if the user is not familiar with the local area. For example, if a disaster occurs while you are traveling or on a business trip, you can use this app to evacuate quickly and safely.It will also be a useful tool for people living in areas at high risk of disasters, allowing them to keep up with disaster information on a daily basis and prepare appropriate responses.

[0030] The disaster information collection unit can analyze real-time information from social media and news sites and extract highly reliable information. For example, the generation AI in the disaster information collection unit collects disaster-related posts from social media such as Twitter and Facebook, and extracts highly reliable information using natural language processing technology. The generation AI filters posts based on specific keywords and hashtags and selects highly reliable information. This allows for the provision of highly reliable disaster information in real time.

[0031] The disaster information collection unit can issue early warnings by studying past disaster data and predicting similar disaster patterns. For example, the generation AI in the disaster information collection unit studies past earthquake data and analyzes patterns of epicenters and seismic intensity. The generation AI then builds a system that issues early warnings when similar earthquakes occur. This enables rapid response by issuing early warnings based on past data.

[0032] The terrain information analysis unit can analyze the damage situation in real time using drone and satellite images and integrate that information. For example, the terrain information analysis unit uses drones to collect aerial images of the disaster-stricken area, and the generation AI analyzes those images to assess the damage situation. The generation AI grasps the extent of collapsed buildings and disrupted roads in real time. This allows for real-time analysis and integration of the damage situation, enabling a rapid response.

[0033] The terrain information analysis unit analyzes the data in a way that is compatible with different languages ​​and cultural spheres, making it possible to provide appropriate information to users around the world. For example, the terrain information analysis unit analyzes disaster information provided by the generation AI in different languages, and translates it into the user's language before providing it. The generation AI builds a system that supports multiple languages, including English, Spanish, and Chinese. This makes it possible to provide information that is compatible with different languages ​​and cultural spheres.

[0034] The terrain information analysis unit uses augmented reality (AR) technology to provide the user with terrain data and hazard map information, for example, using augmented reality (AR) technology. The generation AI intuitively displays local risks through the smartphone camera, allowing the user to intuitively understand local risks.

[0035] The terrain information analysis unit performs an integrated analysis of different disaster risks, identifies areas where multiple risks overlap, and can issue a warning to the user. For example, the generation AI performs an integrated analysis of terrain data and hazard map information to identify areas where multiple disaster risks overlap. The generation AI identifies areas where earthquake risk and flood risk overlap. This makes it possible to identify areas where multiple risks overlap and issue a warning to the user.

[0036] The evacuation route presentation unit can customize the optimal evacuation route by taking into account the user's movement speed and physical strength. For example, the generation AI in the evacuation route presentation unit customizes the optimal evacuation route by taking into account the user's movement speed and physical strength. The generation AI proposes safer and more natural evacuation routes for elderly people and people with disabilities. This makes it possible to customize the optimal evacuation route by taking into account the user's movement speed and physical strength.

[0037] The evacuation route presentation unit can learn from past evacuation data and propose the most effective evacuation route. For example, the generation AI in the evacuation route presentation unit learns from past evacuation data and proposes the most effective evacuation route. The generation AI selects the optimal route based on past successful evacuation cases. This makes it possible to propose the most effective evacuation route based on past data.

[0038] The evacuation route presentation unit can add audio guidance to accommodate the visually impaired and elderly. For example, when the generation AI presents an evacuation route, the evacuation route presentation unit adds audio guidance. The generation AI provides audio guidance to the visually impaired and elderly. This makes it possible to present evacuation routes that are also suitable for the visually impaired and elderly.

[0039] The evacuation route presentation unit generates multiple evacuation routes corresponding to different disaster scenarios and can provide the user with options. For example, the generation AI of the evacuation route presentation unit generates multiple evacuation routes corresponding to different disaster scenarios. The generation AI proposes evacuation routes according to disasters such as earthquakes, floods, and volcanic eruptions. This makes it possible to provide multiple evacuation routes corresponding to different disaster scenarios.

[0040] The evacuation route presentation unit can use the generation AI to present a response method that takes into account the user's individual circumstances depending on the type and situation of the disaster. For example, the generation AI considers the user's family composition and health condition and presents a response method that takes into account the type and situation of the disaster. The generation AI provides special precautions for households with children or elderly people. This makes it possible to present a response method that takes into account the user's individual circumstances.

[0041] The evacuation route presentation unit can add video tutorials to make the system visually easier to understand. For example, the generation AI of the evacuation route presentation unit provides disaster response methods as video tutorials. The generation AI explains evacuation procedures and first aid methods through video. This makes it possible to provide video tutorials that are visually easy to understand.

[0042] The evacuation route presentation unit can generate multiple response methods corresponding to different disaster scenarios and provide the user with options. For example, the generation AI of the evacuation route presentation unit generates multiple response methods corresponding to different disaster scenarios. The generation AI proposes response methods according to disasters such as earthquakes, floods, and volcanic eruptions. This makes it possible to provide multiple response methods corresponding to different disaster scenarios.

[0043] When updating real-time information, the disaster information collection unit can take into account the user's location information and prioritize notifying the most relevant information. For example, the disaster information collection unit uses the generation AI to prioritize notifying the most relevant disaster information based on the user's location information. The generation AI provides disaster information for the area where the user is located in real time. This allows the most relevant information to be prioritized and notified, taking into account the user's location information.

[0044] When updating information, the disaster information collection unit can learn from past data and evaluate the reliability of the information. In the disaster information collection unit, for example, the generation AI learns from past disaster data and evaluates the reliability of the information. The generation AI checks the accuracy of the information by comparing it with past data. This makes it possible to evaluate the reliability of the information based on past data.

[0045] The disaster information collection unit reflects the user's customization settings and can notify only the necessary information. For example, the disaster information collection unit builds a system in which the generation AI notifies only the necessary information based on the user's customization settings. The generation AI provides information according to the type of disaster information and notification frequency set by the user. This makes it possible to notify only the necessary information based on the user's customization settings.

[0046] The disaster information collection unit can provide notification methods compatible with different devices. For example, the disaster information collection unit builds a system in which the generation AI provides notification methods compatible with different devices. The generation AI notifies smartwatches and smart speakers of disaster information. This makes it possible to provide notification methods compatible with different devices.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] Disaster response apps can also be equipped with a health management module that monitors the user's health status. For example, the generative AI can monitor the user's heart rate and blood pressure in real time, and if abnormalities are detected, provide appropriate evacuation routes and directions to medical institutions. This allows for evacuation that takes health status into consideration. The health management module can also provide advice on medications and dietary requirements in the event of a disaster by registering the user's chronic illnesses and allergies in advance. Furthermore, the health management module can also provide information on relaxation methods and mental health care to reduce stress and anxiety during a disaster.

[0049] Disaster response apps can also be equipped with a location analysis unit that prioritizes the most relevant disaster information based on the user's location information. For example, the generation AI can track the user's current location in real time and provide the latest disaster information for that area. This allows the user to quickly obtain the information most relevant to their location. The location analysis unit can also notify the user of disaster risks in advance as they move. Furthermore, the location analysis unit can update dangerous areas on evacuation routes in real time based on the user's location information and provide the safest route.

[0050] Disaster response apps can also be equipped with a health management module that monitors the user's health status. For example, the generative AI can monitor the user's heart rate and blood pressure in real time, and if abnormalities are detected, provide appropriate evacuation routes and directions to medical institutions. This allows for evacuation that takes health status into consideration. The health management module can also provide advice on medications and dietary requirements in the event of a disaster by registering the user's chronic illnesses and allergies in advance. Furthermore, the health management module can also provide information on relaxation methods and mental health care to reduce stress and anxiety during a disaster.

[0051] Disaster response apps can also be equipped with a location analysis unit that prioritizes the most relevant disaster information based on the user's location information. For example, the generation AI can track the user's current location in real time and provide the latest disaster information for that area. This allows the user to quickly obtain the information most relevant to their location. The location analysis unit can also notify the user of disaster risks in advance as they move. Furthermore, the location analysis unit can update dangerous areas on evacuation routes in real time based on the user's location information and provide the safest route.

[0052] Disaster response apps can also be equipped with a health management module that monitors the user's health status. For example, the generative AI can monitor the user's heart rate and blood pressure in real time, and if abnormalities are detected, provide appropriate evacuation routes and directions to medical institutions. This allows for evacuation that takes health status into consideration. The health management module can also provide advice on medications and dietary requirements in the event of a disaster by registering the user's chronic illnesses and allergies in advance. Furthermore, the health management module can also provide information on relaxation methods and mental health care to reduce stress and anxiety during a disaster.

[0053] Disaster response apps can also be equipped with a location analysis unit that prioritizes the most relevant disaster information based on the user's location information. For example, the generation AI can track the user's current location in real time and provide the latest disaster information for that area. This allows the user to quickly obtain the information most relevant to their location. The location analysis unit can also notify the user of disaster risks in advance as they move. Furthermore, the location analysis unit can update dangerous areas on evacuation routes in real time based on the user's location information and provide the safest route.

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

[0055] Step 1: The disaster information collection unit collects the latest disaster occurrence information. For example, the generation AI collects data on natural disasters such as earthquakes, floods, typhoons, and volcanic eruptions, and analyzes it in real time. The generation AI analyzes information such as the epicenter, seismic intensity, and damage status, and provides it to the user. Step 2: The terrain information analysis unit analyzes the collected information. For example, the generation AI analyzes terrain data and hazard map information to assess disaster risk. The generation AI identifies areas at high risk of flooding or landslides and issues warnings to the user. Step 3: The evacuation route presentation unit presents an appropriate evacuation route based on the analyzed information. For example, the generation AI calculates the safest evacuation route based on the user's current location and displays it on a map. The generation AI also simultaneously displays the location of evacuation shelters and dangerous areas along the evacuation route. This allows the disaster response app to provide users with information on appropriate evacuation routes and how to respond on the spot, even if they are not familiar with the local area.

[0056] (Example 2) A disaster response app according to an embodiment of the present invention analyzes the latest disaster occurrence information, topography, and hazard map information, and instantly shows appropriate evacuation routes and response methods, even to people who are unfamiliar with the area. This app uses generative AI to analyze information and provides users with optimal evacuation routes and response methods in real time. As a result, the disaster response app allows users to instantly find appropriate evacuation routes and response methods, even if they are unfamiliar with the area.

[0057] A disaster response app according to an embodiment includes a disaster information collection unit, a terrain information analysis unit, and an evacuation route presentation unit. The disaster information collection unit collects the latest disaster occurrence information. For example, the generation AI collects data on natural disasters such as earthquakes, floods, typhoons, and volcanic eruptions and analyzes it in real time. The generation AI analyzes information such as the epicenter, seismic intensity, and damage status, and provides the information to the user. The terrain information analysis unit analyzes the collected information. For example, the generation AI analyzes terrain data and hazard map information to assess disaster risk. The generation AI identifies areas at high risk of flooding and areas at risk of landslides and issues warnings to the user. The evacuation route presentation unit presents an appropriate evacuation route based on the analyzed information. For example, the generation AI calculates the safest evacuation route based on the user's current location and displays it on a map. The generation AI also simultaneously displays the locations of evacuation shelters and dangerous areas along the evacuation route. This allows the disaster response app to instantly learn appropriate evacuation routes and response methods, even if the user is not familiar with the local area. For example, if a disaster occurs while you are traveling or on a business trip, you can use this app to evacuate quickly and safely.It will also be a useful tool for people living in areas at high risk of disasters, allowing them to keep up with disaster information on a daily basis and prepare appropriate responses.

[0058] The disaster information collection unit can analyze real-time information from social media and news sites and extract highly reliable information. For example, the generation AI in the disaster information collection unit collects disaster-related posts from social media such as Twitter and Facebook, and extracts highly reliable information using natural language processing technology. The generation AI filters posts based on specific keywords and hashtags and selects highly reliable information. This allows for the provision of highly reliable disaster information in real time.

[0059] The disaster information collection unit can issue early warnings by studying past disaster data and predicting similar disaster patterns. For example, the generation AI in the disaster information collection unit studies past earthquake data and analyzes patterns of epicenters and seismic intensity. The generation AI then builds a system that issues early warnings when similar earthquakes occur. This enables rapid response by issuing early warnings based on past data.

[0060] The disaster information collection unit uses the emotion estimation function to analyze the user's emotions when a disaster occurs and can provide information to reduce stress and anxiety. For example, the disaster information collection unit uses the emotion estimation function to analyze the user's emotions in real time when a disaster occurs. The generation AI analyzes the user's facial expressions and voice to measure the level of stress and anxiety. This makes it possible to provide information to reduce the user's stress and anxiety.

[0061] The terrain information analysis unit can analyze the damage situation in real time using drone and satellite images and integrate that information. For example, the terrain information analysis unit uses drones to collect aerial images of the disaster-stricken area, and the generation AI analyzes those images to assess the damage situation. The generation AI grasps the extent of collapsed buildings and disrupted roads in real time. This allows for real-time analysis and integration of the damage situation, enabling a rapid response.

[0062] The terrain information analysis unit analyzes the data in a way that is compatible with different languages ​​and cultural spheres, making it possible to provide appropriate information to users around the world. For example, the terrain information analysis unit analyzes disaster information provided by the generation AI in different languages, and translates it into the user's language before providing it. The generation AI builds a system that supports multiple languages, including English, Spanish, and Chinese. This makes it possible to provide information that is compatible with different languages ​​and cultural spheres.

[0063] The terrain information analysis unit uses the emotion estimation function to analyze the emotions of users when they understand the terrain and hazard map information, and can provide interactive guidance to help with their understanding. For example, the terrain information analysis unit uses the emotion estimation function to analyze the emotions of users when they understand the terrain and hazard map information in real time. The generation AI analyzes the user's facial expressions and voice to measure their level of understanding. This makes it possible to provide interactive guidance to help the user understand.

[0064] The terrain information analysis unit uses augmented reality (AR) technology to provide the user with terrain data and hazard map information, for example, using augmented reality (AR) technology. The generation AI intuitively displays local risks through the smartphone camera, allowing the user to intuitively understand local risks.

[0065] The terrain information analysis unit performs an integrated analysis of different disaster risks, identifies areas where multiple risks overlap, and can issue a warning to the user. For example, the generation AI performs an integrated analysis of terrain data and hazard map information to identify areas where multiple disaster risks overlap. The generation AI identifies areas where earthquake risk and flood risk overlap. This makes it possible to identify areas where multiple risks overlap and issue a warning to the user.

[0066] The evacuation route presentation unit can customize the optimal evacuation route by taking into account the user's movement speed and physical strength. For example, the generation AI in the evacuation route presentation unit customizes the optimal evacuation route by taking into account the user's movement speed and physical strength. The generation AI proposes safer and more natural evacuation routes for elderly people and people with disabilities. This makes it possible to customize the optimal evacuation route by taking into account the user's movement speed and physical strength.

[0067] The evacuation route presentation unit can learn from past evacuation data and propose the most effective evacuation route. For example, the generation AI in the evacuation route presentation unit learns from past evacuation data and proposes the most effective evacuation route. The generation AI selects the optimal route based on past successful evacuation cases. This makes it possible to propose the most effective evacuation route based on past data.

[0068] The evacuation route presentation unit uses the emotion estimation function to analyze the user's emotions when checking evacuation routes and can provide information that gives a sense of security. For example, the evacuation route presentation unit uses the emotion estimation function to analyze the user's emotions in real time when checking evacuation routes. The generation AI analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to provide information that gives the user a sense of security.

[0069] The evacuation route presentation unit can add audio guidance to accommodate the visually impaired and elderly. For example, when the generation AI presents an evacuation route, the evacuation route presentation unit adds audio guidance. The generation AI provides audio guidance to the visually impaired and elderly. This makes it possible to present evacuation routes that are also suitable for the visually impaired and elderly.

[0070] The evacuation route presentation unit generates multiple evacuation routes corresponding to different disaster scenarios and can provide the user with options. For example, the generation AI of the evacuation route presentation unit generates multiple evacuation routes corresponding to different disaster scenarios. The generation AI proposes evacuation routes according to disasters such as earthquakes, floods, and volcanic eruptions. This makes it possible to provide multiple evacuation routes corresponding to different disaster scenarios.

[0071] The evacuation route presentation unit uses the emotion estimation function to analyze the user's emotions in real time when selecting an evacuation route, and can suggest the safest route. The evacuation route presentation unit, for example, uses the emotion estimation function to analyze the user's emotions in real time when selecting an evacuation route. The generation AI analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to suggest the safest evacuation route to the user.

[0072] The evacuation route presentation unit can use the generation AI to present a response method that takes into account the user's individual circumstances depending on the type and situation of the disaster. For example, the generation AI considers the user's family composition and health condition and presents a response method that takes into account the type and situation of the disaster. The generation AI provides special precautions for households with children or elderly people. This makes it possible to present a response method that takes into account the user's individual circumstances.

[0073] The evacuation route presentation unit uses the emotion estimation function to analyze the user's emotions when confirming how to respond, and can provide information that gives a sense of security. For example, the evacuation route presentation unit uses the emotion estimation function to analyze the user's emotions in real time when confirming how to respond. The generation AI analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to provide information that gives the user a sense of security.

[0074] The evacuation route presentation unit can add video tutorials to make the system visually easier to understand. For example, the generation AI of the evacuation route presentation unit provides disaster response methods as video tutorials. The generation AI explains evacuation procedures and first aid methods through video. This makes it possible to provide video tutorials that are visually easy to understand.

[0075] The evacuation route presentation unit can generate multiple response methods corresponding to different disaster scenarios and provide the user with options. For example, the generation AI of the evacuation route presentation unit generates multiple response methods corresponding to different disaster scenarios. The generation AI proposes response methods according to disasters such as earthquakes, floods, and volcanic eruptions. This makes it possible to provide multiple response methods corresponding to different disaster scenarios.

[0076] The evacuation route presentation unit uses the emotion estimation function to analyze in real time the emotions of the user when selecting a response method, and can suggest the most reassuring method. The evacuation route presentation unit, for example, uses the emotion estimation function to analyze in real time the emotions of the user when selecting a response method. The generation AI analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to suggest the most reassuring response method to the user.

[0077] When updating real-time information, the disaster information collection unit can take into account the user's location information and prioritize notifying the most relevant information. For example, the disaster information collection unit uses the generation AI to prioritize notifying the most relevant disaster information based on the user's location information. The generation AI provides disaster information for the area where the user is located in real time. This allows the most relevant information to be prioritized and notified, taking into account the user's location information.

[0078] When updating information, the disaster information collection unit can learn from past data and evaluate the reliability of the information. In the disaster information collection unit, for example, the generation AI learns from past disaster data and evaluates the reliability of the information. The generation AI checks the accuracy of the information by comparing it with past data. This makes it possible to evaluate the reliability of the information based on past data.

[0079] The disaster information collection unit can use the emotion estimation function to analyze the emotions of users when receiving information and provide a notification method to reduce stress. For example, the disaster information collection unit uses the emotion estimation function to analyze the emotions of users when receiving information in real time. The generation AI analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to provide a notification method to reduce stress for users.

[0080] The disaster information collection unit reflects the user's customization settings and can notify only the necessary information. For example, the disaster information collection unit builds a system in which the generation AI notifies only the necessary information based on the user's customization settings. The generation AI provides information according to the type of disaster information and notification frequency set by the user. This makes it possible to notify only the necessary information based on the user's customization settings.

[0081] The disaster information collection unit can provide notification methods compatible with different devices. For example, the disaster information collection unit builds a system in which the generation AI provides notification methods compatible with different devices. The generation AI notifies smartwatches and smart speakers of disaster information. This makes it possible to provide notification methods compatible with different devices.

[0082] The disaster information collection unit uses the emotion estimation function to analyze the emotions of users when receiving information in real time, and can propose the most reassuring notification method. The disaster information collection unit, for example, uses the emotion estimation function to analyze the emotions of users when receiving information in real time. The generation AI analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to propose the most reassuring notification method for the user.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] Disaster response apps can also be equipped with a health management module that monitors the user's health status. For example, the generative AI can monitor the user's heart rate and blood pressure in real time, and if abnormalities are detected, provide appropriate evacuation routes and directions to medical institutions. This allows for evacuation that takes health status into consideration. The health management module can also provide advice on medications and dietary requirements in the event of a disaster by registering the user's chronic illnesses and allergies in advance. Furthermore, the health management module can also provide information on relaxation methods and mental health care to reduce stress and anxiety during a disaster.

[0085] Disaster response apps can also be equipped with an emotion analysis unit that estimates a user's emotions and provides appropriate evacuation routes and response methods based on the estimated emotions. For example, the generative AI can analyze a user's facial expressions and voice to measure their level of stress and anxiety. This makes it possible to provide evacuation routes and response methods that match the user's emotional state. For example, for a user with high stress levels, it can suggest easier and safer evacuation routes and provide information to give them a sense of security. The emotion analysis unit can also analyze a user's emotions in real time when selecting an evacuation route and suggest the safest route. Furthermore, the emotion analysis unit can analyze the user's emotions when receiving disaster information and provide notification methods to reduce stress.

[0086] Disaster response apps can also be equipped with a location analysis unit that prioritizes the most relevant disaster information based on the user's location information. For example, the generation AI can track the user's current location in real time and provide the latest disaster information for that area. This allows the user to quickly obtain the information most relevant to their location. The location analysis unit can also notify the user of disaster risks in advance as they move. Furthermore, the location analysis unit can update dangerous areas on evacuation routes in real time based on the user's location information and provide the safest route.

[0087] Disaster response apps can also be equipped with an emotion analysis unit that estimates a user's emotions and provides appropriate evacuation routes and response methods based on the estimated emotions. For example, the generative AI can analyze a user's facial expressions and voice to measure their level of stress and anxiety. This makes it possible to provide evacuation routes and response methods that match the user's emotional state. For example, for a user with high stress levels, it can suggest easier and safer evacuation routes and provide information to give them a sense of security. The emotion analysis unit can also analyze a user's emotions in real time when selecting an evacuation route and suggest the safest route. Furthermore, the emotion analysis unit can analyze the user's emotions when receiving disaster information and provide notification methods to reduce stress.

[0088] Disaster response apps can also be equipped with a health management module that monitors the user's health status. For example, the generative AI can monitor the user's heart rate and blood pressure in real time, and if abnormalities are detected, provide appropriate evacuation routes and directions to medical institutions. This allows for evacuation that takes health status into consideration. The health management module can also provide advice on medications and dietary requirements in the event of a disaster by registering the user's chronic illnesses and allergies in advance. Furthermore, the health management module can also provide information on relaxation methods and mental health care to reduce stress and anxiety during a disaster.

[0089] Disaster response apps can also be equipped with an emotion analysis unit that estimates a user's emotions and provides appropriate evacuation routes and response methods based on the estimated emotions. For example, the generative AI can analyze a user's facial expressions and voice to measure their level of stress and anxiety. This makes it possible to provide evacuation routes and response methods that match the user's emotional state. For example, for a user with high stress levels, it can suggest easier and safer evacuation routes and provide information to give them a sense of security. The emotion analysis unit can also analyze a user's emotions in real time when selecting an evacuation route and suggest the safest route. Furthermore, the emotion analysis unit can analyze the user's emotions when receiving disaster information and provide notification methods to reduce stress.

[0090] Disaster response apps can also be equipped with a location analysis unit that prioritizes the most relevant disaster information based on the user's location information. For example, the generation AI can track the user's current location in real time and provide the latest disaster information for that area. This allows the user to quickly obtain the information most relevant to their location. The location analysis unit can also notify the user of disaster risks in advance as they move. Furthermore, the location analysis unit can update dangerous areas on evacuation routes in real time based on the user's location information and provide the safest route.

[0091] Disaster response apps can also be equipped with an emotion analysis unit that estimates a user's emotions and provides appropriate evacuation routes and response methods based on the estimated emotions. For example, the generative AI can analyze a user's facial expressions and voice to measure their level of stress and anxiety. This makes it possible to provide evacuation routes and response methods that match the user's emotional state. For example, for a user with high stress levels, it can suggest easier and safer evacuation routes and provide information to give them a sense of security. The emotion analysis unit can also analyze a user's emotions in real time when selecting an evacuation route and suggest the safest route. Furthermore, the emotion analysis unit can analyze the user's emotions when receiving disaster information and provide notification methods to reduce stress.

[0092] Disaster response apps can also be equipped with a health management module that monitors the user's health status. For example, the generative AI can monitor the user's heart rate and blood pressure in real time, and if abnormalities are detected, provide appropriate evacuation routes and directions to medical institutions. This allows for evacuation that takes health status into consideration. The health management module can also provide advice on medications and dietary requirements in the event of a disaster by registering the user's chronic illnesses and allergies in advance. Furthermore, the health management module can also provide information on relaxation methods and mental health care to reduce stress and anxiety during a disaster.

[0093] Disaster response apps can also be equipped with a location analysis unit that prioritizes the most relevant disaster information based on the user's location information. For example, the generation AI can track the user's current location in real time and provide the latest disaster information for that area. This allows the user to quickly obtain the information most relevant to their location. The location analysis unit can also notify the user of disaster risks in advance as they move. Furthermore, the location analysis unit can update dangerous areas on evacuation routes in real time based on the user's location information and provide the safest route.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The disaster information collection unit collects the latest disaster occurrence information. For example, the generation AI collects data on natural disasters such as earthquakes, floods, typhoons, and volcanic eruptions, and analyzes it in real time. The generation AI analyzes information such as the epicenter, seismic intensity, and damage status, and provides it to the user. Step 2: The terrain information analysis unit analyzes the collected information. For example, the generation AI analyzes terrain data and hazard map information to assess disaster risk. The generation AI identifies areas at high risk of flooding or landslides and issues warnings to the user. Step 3: The evacuation route presentation unit presents an appropriate evacuation route based on the analyzed information. For example, the generation AI calculates the safest evacuation route based on the user's current location and displays it on a map. The generation AI also simultaneously displays the location of evacuation shelters and dangerous areas along the evacuation route. This allows the disaster response app to provide users with information on appropriate evacuation routes and how to respond on the spot, even if they are not familiar with the local area.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] 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 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] 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.

[0142] 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.

[0143] 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 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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]

[0163] 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. Disaster Information Collection Department, which collects the latest disaster occurrence information; a topographical information analysis unit that analyzes the information collected by the disaster information collection unit; an evacuation route presentation unit that presents an appropriate evacuation route based on the information analyzed by the topographical information analysis unit; A system characterized by:

2. The disaster information collection unit Analyze real-time information from social media and news sites to extract reliable information 2. The system of claim 1.

3. The topographical information analysis unit Analyzing damage situations in real time using drone and satellite images and integrating that information 2. The system of claim 1.

4. The evacuation route presentation unit Customize the optimal evacuation route based on the user's movement speed and physical strength 2. The system of claim 1.

5. The disaster information collection unit Using emotion estimation functionality, we analyze users' emotions during disasters and provide information to reduce stress and anxiety.

2. The system of claim 1.

6. The topographical information analysis unit Using emotion estimation, we analyze the emotions users feel when interpreting information on terrain and hazard maps, and provide an interactive guide to help them understand.

2. The system of claim 1.

7. The evacuation route presentation unit Using the emotion estimation function, the emotion of the user when checking the evacuation route is analyzed, and information is provided to give a sense of security.

2. The system of claim 1.

8. The disaster information collection unit Using the emotion estimation function, the emotion of the user when receiving the information is analyzed, and a notification method for reducing stress is provided.

2. The system of claim 1.

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