System

Abstract

An object of a system according to an embodiment is to enable each home to quickly and appropriately grasp an evacuation destination at the time of a disaster.SOLUTION: A system includes a disaster prevention radio information acquisition part, a weather information acquisition part, a position information acquisition part, an evacuation destination proposal part, and an evacuation route presentation part. The wireless disaster prevention information acquisition unit acquires wireless disaster prevention information. The weather information acquisition unit acquires weather information. The position information acquisition unit acquires position information of each home. The evacuation point proposing section analyzes the information acquired by the wireless disaster prevention information acquiring section and the weather information acquiring section using the generated AI, and proposes an optimum evacuation point based on the position information acquired by the position information acquiring section. The evacuation route presentation unit presents an optimal evacuation route to the evacuation destination proposed by the evacuation destination proposal 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 technology has had the problem of making it difficult for each household to quickly and appropriately determine where to evacuate in the event of a disaster.

[0005] The system according to the embodiment aims to enable each household to quickly and appropriately determine an evacuation destination in the event of a disaster. [Means for solving the problem]

[0006] The system according to the embodiment includes a disaster prevention radio information acquisition unit, a weather information acquisition unit, a location information acquisition unit, an evacuation destination suggestion unit, and an evacuation route presentation unit. The disaster prevention radio information acquisition unit acquires disaster prevention radio information. The weather information acquisition unit acquires weather information. The location information acquisition unit acquires location information for each household. The evacuation destination suggestion unit analyzes the information acquired by the disaster prevention radio information acquisition unit and the weather information acquisition unit using a generation AI, and suggests an optimal evacuation destination based on the location information acquired by the location information acquisition unit. The evacuation route presentation unit presents an optimal evacuation route to the evacuation destination suggested by the evacuation destination suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable each household to quickly and appropriately determine an evacuation destination in the event of a disaster. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The disaster prevention support system according to the embodiment of the present invention is a system that uses a generation AI to analyze local disaster prevention radio information and weather information, and suggests optimal evacuation destinations based on the location information of each household. This enables the disaster prevention support system to support each household in evacuating quickly and safely in the event of a disaster.

[0029] A disaster prevention support system according to an embodiment includes a disaster prevention radio information acquisition unit, a weather information acquisition unit, a location information acquisition unit, an evacuation destination suggestion unit, and an evacuation route presentation unit. The disaster prevention radio information acquisition unit acquires disaster prevention radio information. For example, it acquires evacuation instructions from a disaster prevention radio. The disaster prevention radio information acquisition unit can also acquire disaster warnings from local disaster prevention radio. The disaster prevention radio information acquisition unit can also acquire emergency alerts. The weather information acquisition unit acquires weather information. For example, it acquires earthquake alerts from the Japan Meteorological Agency. The weather information acquisition unit can also acquire weather forecasts. The weather information acquisition unit can also acquire weather warnings. The location information acquisition unit acquires location information of each household. For example, it acquires the household location information as GPS data. The location information acquisition unit can also acquire address information. The location information acquisition unit can also acquire device location information. The evacuation destination suggestion unit uses a generation AI to analyze the information acquired by the disaster prevention radio information acquisition unit and the weather information acquisition unit, and suggests an optimal evacuation destination based on the location information acquired by the location information acquisition unit. For example, the generation AI suggests an optimal evacuation destination taking into account the capacity of the evacuation site. The generation AI can also propose an optimal evacuation destination taking into account accessible routes. The generation AI can also propose an optimal evacuation destination taking into account safety. The evacuation route presentation unit presents an optimal evacuation route to the evacuation destination proposed by the evacuation destination proposal unit. For example, the generation AI presents an optimal evacuation route taking into account road congestion. The generation AI can also present an optimal evacuation route taking into account road closures due to a disaster. The generation AI can also display evacuation routes on a map to present them to the user in a visually easy-to-understand manner. This allows the disaster prevention support system according to the embodiment to support each household in swiftly and safely evacuating in the event of a disaster. For example, the generation AI provides detailed information about the evacuation destination. The generation AI provides the user with information such as the address, contact information, capacity, and facility status of the evacuation destination. This allows the user to understand the situation at the evacuation destination in advance and evacuate with peace of mind. The generation AI also updates information in real time according to the progress of the disaster and re-proposes optimal evacuation destinations and evacuation routes.For example, if evacuation sites become full or new evacuation orders are issued, the AI ​​will respond quickly to changing situations. The AI ​​will always make optimal suggestions based on the latest information.

[0030] The disaster prevention radio information acquisition unit can acquire evacuation instructions from disaster prevention radio or earthquake early warning information from the Japan Meteorological Agency, and analyze them to grasp the disaster situation. The disaster prevention radio information acquisition unit, for example, acquires evacuation instructions from disaster prevention radio and analyzes them to grasp the disaster situation. The disaster prevention radio information acquisition unit can also acquire earthquake early warnings from the Japan Meteorological Agency and analyze them to grasp the disaster situation. The disaster prevention radio information acquisition unit can also refer to past disaster data, identify similar disaster patterns, and improve prediction accuracy. For example, the generation AI refers to disaster data from past earthquakes, typhoons, etc., and compares it with current disaster prevention radio information and weather information. This identifies similar disaster patterns and improves prediction accuracy. For example, past data can be used to predict the damage situation in a specific area. This makes it possible to accurately grasp the disaster situation.

[0031] The location information acquisition unit acquires the home's location information as GPS data and can identify the nearest and safest evacuation destination based on that data. For example, the location information acquisition unit acquires the home's location information as GPS data and can identify the nearest and safest evacuation destination based on that data. The location information acquisition unit also visualizes the extent of a disaster's impact on a map in real time based on the home's location information and provides it to the user. For example, the generation AI analyzes disaster prevention radio information and weather information and displays the results on a map in real time. For example, the epicenter of an earthquake or the path of a typhoon is visualized on a map. The generation AI also displays the extent of the impact on the map in different colors based on the disaster information analyzed. For example, areas with heavy damage are displayed in red and areas with less damage in yellow. The generation AI also overlays the information analyzed on the map, allowing the user to intuitively grasp the extent of the disaster's impact. For example, areas where evacuation orders have been issued are displayed on the map. This allows the user to identify the nearest and safest evacuation destination.

[0032] The evacuation route presentation unit can calculate the safest and fastest route based on information about road congestion or road closures due to a disaster. The evacuation route presentation unit, for example, calculates the safest and fastest route by taking road congestion into account. The evacuation route presentation unit can also calculate the safest and fastest route by taking road closures due to a disaster into account. The evacuation route presentation unit also uses an emotion estimation function to consider residents' emotional reactions when analyzing disaster prevention radio information and weather information, optimizing the information provision method to avoid panic. For example, the generation AI analyzes disaster prevention radio information and weather information to estimate residents' emotional reactions. For example, it optimizes the information provision method by taking into account residents' anxiety and fear when an evacuation order is issued. The emotion estimation function can also be used to monitor residents' emotional reactions in real time and adjust the information provision method to avoid panic. For example, it can communicate evacuation orders in a calm tone. The generation AI can also analyze residents' emotional reactions and propose information provision methods that take emotions into account. For example, it can simultaneously provide messages that convey a sense of security. This allows the safest and fastest route to be calculated.

[0033] The evacuation destination suggestion unit can provide the user with information such as the address or contact information, capacity, and facility status of the evacuation destination. The evacuation destination suggestion unit provides the user with information such as the address or contact information, capacity, and facility status of the evacuation destination. For example, the information analyzed by the generation AI utilizes more diverse data sources, including disaster-related information from social media and news sites. For example, the generation AI collects disaster-related information from social media and news sites and analyzes it in combination with disaster prevention radio information and weather information. For example, disaster-related tweets from Twitter (registered trademark) are used for analysis. The generation AI also collects disaster alerts from news sites in real time and analyzes them in combination with disaster prevention radio information and weather information. For example, the content of news articles is reflected in the analysis. The generation AI also filters information from social media and news sites and uses only reliable information for analysis. For example, information from official accounts is preferentially collected. This allows detailed information about evacuation destinations to be provided to the user.

[0034] The generating AI can update information in real time according to the progress of the disaster and re-suggest optimal evacuation destinations or evacuation routes. The generating AI can, for example, update information in real time according to the progress of the disaster and re-suggest optimal evacuation destinations and evacuation routes. For example, disaster prevention radio information and weather information analyzed by the generating AI can be provided through a voice assistant. For example, evacuation instructions can be transmitted by voice using a smart speaker. Information can also be provided through the voice assistant in a format that is easy to understand for visually impaired people and the elderly. For example, concise and clear voice messages can be used. The information analyzed by the generating AI can also be linked to the voice assistant, allowing users to obtain information by voice command. For example, it can respond to voice commands such as "Tell me where to evacuate." This makes it possible to re-suggest optimal evacuation destinations and evacuation routes according to the progress of the disaster.

[0035] The generation AI can identify similar disaster patterns based on past disaster data and improve prediction accuracy. For example, the generation AI refers to past disaster data to identify similar disaster patterns and improve prediction accuracy. For example, the generation AI refers to disaster data such as past earthquakes and typhoons and compares it with current disaster prevention radio information and weather information. This identifies similar disaster patterns and improves prediction accuracy. For example, the generation AI predicts the damage situation in a specific area from past data. The generation AI also analyzes past disaster data and predicts the progression pattern of a disaster based on specific weather conditions and disaster prevention radio information. For example, the generation AI predicts the path of the current typhoon based on past typhoon path data. The generation AI also uses past disaster data to correct the analysis results of current disaster prevention radio information and weather information. For example, the generation AI improves the accuracy of current earthquake early warnings based on past earthquake data. In this way, prediction accuracy can be improved by referring to past disaster data.

[0036] The generation AI can visualize the extent of the disaster's impact on a map in real time based on the analyzed information and provide it to the user. For example, the generation AI visualizes the extent of the disaster's impact on a map in real time based on the analyzed information and provides it to the user. For example, the generation AI analyzes disaster prevention radio information and weather information and displays the results on a map in real time. For example, the generation AI visualizes the epicenter of an earthquake or the path of a typhoon on a map. Furthermore, based on the disaster information analyzed by the generation AI, the impact area is displayed in different colors on the map. For example, areas with heavy damage are displayed in red, and areas with less damage are displayed in yellow. Furthermore, the generation AI overlays the information analyzed on the map, allowing the user to intuitively grasp the extent of the disaster's impact. For example, areas where evacuation orders have been issued are displayed on the map. In this way, the extent of the disaster's impact is visualized on a map in real time, allowing the user to intuitively grasp the situation of the disaster.

[0037] The generation AI can utilize more diverse data sources, including disaster-related information from social media or news sites, for the information it analyzes. For example, the generation AI collects disaster-related information from social media or news sites, and analyzes it by integrating it with disaster prevention radio information and weather information. For example, disaster-related tweets from Twitter (registered trademark) are used for analysis. The generation AI also collects disaster alerts from news sites in real time and analyzes them in combination with disaster prevention radio information and weather information. For example, the content of news articles is reflected in the analysis. The generation AI also filters information from social media and news sites, and uses only highly reliable information for analysis. For example, it prioritizes the collection of information from official accounts. This allows for the utilization of more diverse data sources, thereby improving the accuracy of the analysis.

[0038] The generating AI provides the analyzed information to the user through a voice assistant, making it possible to accommodate the visually impaired or elderly. The generating AI, for example, provides analyzed disaster prevention radio information or weather information through the voice assistant. For example, evacuation instructions can be given aloud using a smart speaker. Information can also be provided through the voice assistant in a format that is easy for the visually impaired and elderly to understand. For example, concise and clear voice messages can be used. The generating AI can also link the analyzed information to the voice assistant, allowing the user to obtain information through voice commands. For example, it can respond to voice commands such as "tell me where to evacuate." This makes it possible to provide information to a wider range of users by accommodating the visually impaired and elderly.

[0039] When acquiring the home's location information, the location information acquisition unit can suggest safer evacuation destinations based on the building's earthquake resistance or surrounding topographical information. The location information acquisition unit, for example, acquires the home's location information and references earthquake resistance data for surrounding buildings. For example, it prioritizes suggesting areas with many highly earthquake-resistant buildings as evacuation destinations. The location information acquisition unit also analyzes surrounding topographical information based on the home's location information to suggest evacuation destinations with a low risk of earthquakes or tsunamis. For example, it prioritizes suggesting evacuation sites on high ground. The location information acquisition unit also considers the building's earthquake resistance and topographical information to build a system that suggests optimal evacuation destinations. For example, it suggests avoiding areas with many buildings with low earthquake resistance. In this way, safer evacuation destinations can be suggested by taking into account the building's earthquake resistance and topographical information.

[0040] The generation AI can reflect the congestion status of evacuation shelters or the availability of facilities in real time when proposing evacuation destinations. For example, the generation AI can reflect the congestion status of evacuation shelters and the availability of facilities in real time when proposing evacuation destinations. For example, the generation AI can collect information on the congestion status of evacuation shelters in real time and reflect this in its evacuation destination suggestions. For example, it can suggest evacuation destinations that avoid crowded evacuation shelters. The generation AI can also collect information on the availability of facilities at evacuation shelters in real time and reflect this in its evacuation destination suggestions. For example, it can make suggestions that take into account the supply of toilets and water. The generation AI can also update the congestion status of evacuation shelters and the availability of facilities in real time, building a system that suggests optimal evacuation destinations. For example, it can make suggestions based on the capacity of the evacuation shelter. This allows the congestion status of evacuation shelters and the availability of facilities to be reflected in real time, making it possible to suggest more appropriate evacuation destinations.

[0041] The generation AI can also include places where pets are allowed or barrier-free shelters in the evacuation destinations it suggests. For example, the generation AI can collect information on pet-friendly shelters and reflect it in evacuation destination suggestions. For example, it can suggest pet-friendly shelters to households with pets. The generation AI can also collect information on barrier-free shelters and reflect it in evacuation destination suggestions. For example, it can suggest barrier-free shelters to households with elderly or disabled people. In addition, a system can be built in which the generation AI prioritizes suggesting places where pets are allowed or barrier-free shelters. For example, it can make suggestions based on information about the shelter's facilities. This makes it possible to meet a wider variety of needs by including places where pets are allowed or barrier-free shelters.

[0042] The generating AI can add a function that allows the user to share the evacuation destination it suggests with their family or friends, ensuring a means of communication. For example, the generating AI can add a function that allows the user to share the evacuation destination suggested by the generating AI with their family and friends. For example, by sending evacuation destination information via SMS or email. In addition, an app can be developed that allows users to share evacuation destination information with their family and friends. For example, a function can be provided that allows the user to share evacuation destination maps and contact information. In addition, a system can be built that allows the evacuation destination suggested by the generating AI to be shared with the user's family and friends in real time. For example, the evacuation destination information can be shared on the cloud. In this way, by sharing the evacuation destination, a means of communication can be secured and cooperation with family and friends can be strengthened.

[0043] When presenting an evacuation route, the generation AI can detect dangerous areas on the evacuation route in real time and propose an avoiding route. For example, when presenting an evacuation route, the generation AI can detect dangerous areas on the evacuation route in real time and propose an avoiding route. For example, the generation AI can detect dangerous areas on the evacuation route in real time and propose an avoiding route. For example, it can calculate a route that avoids buildings at risk of collapse or flooded areas. The generation AI can also display dangerous areas on the evacuation route on a map, presenting them visually to the user in an easy-to-understand manner. For example, it can display dangerous areas in red and safe routes in green. Furthermore, a system can be built in which the generation AI proposes the optimal evacuation route based on information on dangerous areas that is updated in real time. For example, the route can be recalculated every time road conditions change. This allows the system to detect dangerous areas on the evacuation route in real time and propose an avoiding route, thereby supporting safe evacuation.

[0044] When presenting an evacuation route, the generation AI can calculate the optimal route based on the user's means of transportation. For example, when presenting an evacuation route, the generation AI takes the user's means of transportation into consideration and calculates the optimal route. For example, the generation AI calculates the optimal evacuation route by taking the user's means of transportation into consideration. For example, if walking, it will prioritize pedestrian-only roads, and if driving, it will choose wide roads. The generation AI also displays routes based on the user's means of transportation on a map, presenting them visually in an easy-to-understand manner. For example, walking routes and driving routes will be displayed in different colors. We will also build a system in which the generation AI detects the user's means of transportation in real time and proposes the optimal evacuation route accordingly. For example, if the user is riding a bicycle, it will prioritize bicycle-only roads. This allows us to calculate the optimal route based on the user's means of transportation and provide a more appropriate evacuation route.

[0045] When presenting an evacuation route, the generation AI can also display the locations of shelters or medical facilities along the route, thereby supporting emergency response. For example, when presenting an evacuation route, the generation AI can display the locations of shelters and medical facilities along the route on a map, providing a visually easy-to-understand presentation to the user. For example, the generation AI can display shelters and medical facilities as icons. The generation AI can also update information about shelters and medical facilities along the evacuation route in real time and provide it to the user. For example, it can display the capacity of the shelter and the availability of the medical facility. The generation AI can also build a system that takes into account the locations of shelters and medical facilities along the evacuation route and proposes the optimal evacuation route. For example, it can propose a route that includes a stop at the shelter or medical facility. This can support emergency response by displaying the locations of shelters and medical facilities along the evacuation route.

[0046] When presenting an evacuation route, the generation AI can suggest a reasonable route based on the user's physical strength or health condition. The generation AI, for example, takes into account the user's physical strength and health condition to suggest a reasonable evacuation route. For example, it may suggest a short, flat route for the elderly and children. In addition, a system can be built in which the generation AI monitors the user's health condition in real time and suggests the optimal evacuation route accordingly. For example, it adjusts the route based on heart rate and number of steps. The generation AI also displays the evacuation route on a map based on the user's physical strength and health condition, presenting it in an easy-to-understand visual manner. For example, routes are displayed in different colors according to physical strength. This makes it possible to suggest a reasonable route by taking into account the user's physical strength and health condition.

[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] The disaster prevention support system can also include a supply supply unit that monitors the supply status during a disaster in real time and provides necessary supplies. For example, it can monitor the inventory of food, water, and medicines at evacuation centers and arrange for additional supplies as needed. The supply supply unit can also provide supplies according to the specific needs of evacuees. For example, it can provide appropriate supplies to evacuees who require food for allergies or specific medicines. Furthermore, the supply supply unit can reallocate supplies between evacuation centers to ensure efficient supply. This allows evacuees to receive the supplies they need quickly, improving the quality of their lives in evacuation centers.

[0049] The disaster prevention support system can also be equipped with a health management unit that monitors the health status of evacuees. For example, it can measure the evacuees' body temperature and heart rate in real time and notify medical staff if any abnormalities are detected. The health management unit can also manage information on the evacuees' chronic illnesses and allergies, allowing them to provide appropriate medical care. Furthermore, the health management unit can take measures to prevent the spread of infectious diseases within the evacuation shelter. For example, it can isolate evacuees suspected of having an infectious disease to prevent them from infecting other evacuees. This helps protect the health of evacuees and support their safe evacuation.

[0050] The disaster prevention support system can also be equipped with an environmental management unit that monitors the environment of the evacuation shelter. For example, it can monitor the temperature, humidity, and air quality inside the shelter in real time to maintain a comfortable environment. The environmental management unit can also manage the cleaning status inside the shelter and take measures to maintain a hygienic environment. Furthermore, the environmental management unit can monitor the condition of the shelter's facilities and perform repairs and maintenance as necessary. This allows evacuees to live in a comfortable and safe environment.

[0051] The disaster prevention support system can also be equipped with a pet management section to ensure the safety of evacuees' pets. For example, it can manage pets' locations within the evacuation shelter to prevent conflicts with other evacuees. The pet management section can also monitor the health of pets and dispatch veterinarians as needed. The pet management section can also manage food and water supplies for pets, providing a comfortable environment for pets. This allows evacuees with pets to live in safety and peace of mind.

[0052] The disaster prevention support system can further include a mobility support unit that supports the movement of evacuees. For example, it can provide wheelchairs and walking aids to support the mobility of elderly people and people with disabilities. The mobility support unit can also operate shuttle buses to support movement between evacuation centers. Furthermore, the mobility support unit can optimize evacuees' travel routes and take measures to avoid congestion. This can help evacuees move safely and smoothly.

[0053] The disaster prevention support system can further include an information management unit that centrally manages information about evacuees. For example, it can centrally manage personal information, health information, and evacuation destination information about evacuees and quickly provide necessary information. The information management unit can also provide functions to support evacuees in contacting their families and friends. Furthermore, the information management unit can take measures to protect evacuees' information and ensure their privacy. This allows for appropriate management of evacuees' information and supports the rapid and accurate provision of information.

[0054] The disaster prevention support system can also include an education support section that supports the education of evacuees. For example, it can provide teaching materials and learning programs to support children's learning in evacuation shelters. The education support section can also provide disaster prevention education to evacuees and provide opportunities for them to learn how to respond in the event of a disaster. Furthermore, the education support section can provide vocational training programs to help evacuees improve their skills. This allows evacuees to continue learning even while they are in evacuation shelters, and to acquire skills that will be useful in their future lives.

[0055] The disaster prevention support system can also be equipped with a communication support unit that supports communication between evacuees. For example, it can provide bulletin boards and chat functions to make it easier for evacuees to share information with each other. The communication support unit can also provide communication methods to help evacuees contact their families and friends. Furthermore, the communication support unit can also build a system to collect evacuees' opinions and requests and reflect them in the operation of evacuation shelters. This can facilitate communication between evacuees and their families and improve the quality of life in evacuation shelters.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The disaster prevention radio information acquisition unit acquires disaster prevention radio information, such as evacuation instructions, disaster warnings, and emergency alerts, from the disaster prevention radio. Step 2: The weather information acquisition unit acquires weather information, such as earthquake alerts, weather forecasts, and weather warnings from the Japan Meteorological Agency. Step 3: The location information acquisition unit acquires location information of each household. For example, the location information of the household is acquired as GPS data, address information, or device location information. Step 4: The evacuation destination suggestion unit uses the generation AI to analyze the information acquired by the disaster prevention radio information acquisition unit and the weather information acquisition unit, and suggests the optimal evacuation destination based on the location information acquired by the location information acquisition unit. For example, the generation AI suggests the optimal evacuation destination taking into account the evacuation site's capacity, accessible routes, and safety. Step 5: The evacuation route presentation unit presents the optimal evacuation route to the evacuation destination proposed by the evacuation destination proposal unit. For example, the generation AI presents the optimal evacuation route taking into account road congestion and road closures due to the disaster, and displays the evacuation route on a map to make it visually easy for the user to understand.

[0058] (Example 2) The disaster prevention support system according to the embodiment of the present invention is a system that uses a generation AI to analyze local disaster prevention radio information and weather information, and suggests optimal evacuation destinations based on the location information of each household. This enables the disaster prevention support system to support each household in evacuating quickly and safely in the event of a disaster.

[0059] A disaster prevention support system according to an embodiment includes a disaster prevention radio information acquisition unit, a weather information acquisition unit, a location information acquisition unit, an evacuation destination suggestion unit, and an evacuation route presentation unit. The disaster prevention radio information acquisition unit acquires disaster prevention radio information. For example, it acquires evacuation instructions from a disaster prevention radio. The disaster prevention radio information acquisition unit can also acquire disaster warnings from local disaster prevention radio. The disaster prevention radio information acquisition unit can also acquire emergency alerts. The weather information acquisition unit acquires weather information. For example, it acquires earthquake alerts from the Japan Meteorological Agency. The weather information acquisition unit can also acquire weather forecasts. The weather information acquisition unit can also acquire weather warnings. The location information acquisition unit acquires location information of each household. For example, it acquires the household location information as GPS data. The location information acquisition unit can also acquire address information. The location information acquisition unit can also acquire device location information. The evacuation destination suggestion unit uses a generation AI to analyze the information acquired by the disaster prevention radio information acquisition unit and the weather information acquisition unit, and suggests an optimal evacuation destination based on the location information acquired by the location information acquisition unit. For example, the generation AI suggests an optimal evacuation destination taking into account the capacity of the evacuation site. The generation AI can also propose an optimal evacuation destination taking into account accessible routes. The generation AI can also propose an optimal evacuation destination taking into account safety. The evacuation route presentation unit presents an optimal evacuation route to the evacuation destination proposed by the evacuation destination proposal unit. For example, the generation AI presents an optimal evacuation route taking into account road congestion. The generation AI can also present an optimal evacuation route taking into account road closures due to a disaster. The generation AI can also display evacuation routes on a map to present them to the user in a visually easy-to-understand manner. This allows the disaster prevention support system according to the embodiment to support each household in swiftly and safely evacuating in the event of a disaster. For example, the generation AI provides detailed information about the evacuation destination. The generation AI provides the user with information such as the address, contact information, capacity, and facility status of the evacuation destination. This allows the user to understand the situation at the evacuation destination in advance and evacuate with peace of mind. The generation AI also updates information in real time according to the progress of the disaster and re-proposes optimal evacuation destinations and evacuation routes.For example, if evacuation sites become full or new evacuation orders are issued, the AI ​​will respond quickly to changing situations. The AI ​​will always make optimal suggestions based on the latest information.

[0060] The disaster prevention radio information acquisition unit can acquire evacuation instructions from disaster prevention radio or earthquake early warning information from the Japan Meteorological Agency, and analyze them to grasp the disaster situation. The disaster prevention radio information acquisition unit, for example, acquires evacuation instructions from disaster prevention radio and analyzes them to grasp the disaster situation. The disaster prevention radio information acquisition unit can also acquire earthquake early warnings from the Japan Meteorological Agency and analyze them to grasp the disaster situation. The disaster prevention radio information acquisition unit can also refer to past disaster data, identify similar disaster patterns, and improve prediction accuracy. For example, the generation AI refers to disaster data from past earthquakes, typhoons, etc., and compares it with current disaster prevention radio information and weather information. This identifies similar disaster patterns and improves prediction accuracy. For example, past data can be used to predict the damage situation in a specific area. This makes it possible to accurately grasp the disaster situation.

[0061] The location information acquisition unit acquires the home's location information as GPS data and can identify the nearest and safest evacuation destination based on that data. For example, the location information acquisition unit acquires the home's location information as GPS data and can identify the nearest and safest evacuation destination based on that data. The location information acquisition unit also visualizes the extent of a disaster's impact on a map in real time based on the home's location information and provides it to the user. For example, the generation AI analyzes disaster prevention radio information and weather information and displays the results on a map in real time. For example, the epicenter of an earthquake or the path of a typhoon is visualized on a map. The generation AI also displays the extent of the impact on the map in different colors based on the disaster information analyzed. For example, areas with heavy damage are displayed in red and areas with less damage in yellow. The generation AI also overlays the information analyzed on the map, allowing the user to intuitively grasp the extent of the disaster's impact. For example, areas where evacuation orders have been issued are displayed on the map. This allows the user to identify the nearest and safest evacuation destination.

[0062] The evacuation route presentation unit can calculate the safest and fastest route based on information about road congestion or road closures due to a disaster. The evacuation route presentation unit, for example, calculates the safest and fastest route by taking road congestion into account. The evacuation route presentation unit can also calculate the safest and fastest route by taking road closures due to a disaster into account. The evacuation route presentation unit also uses an emotion estimation function to consider residents' emotional reactions when analyzing disaster prevention radio information and weather information, optimizing the information provision method to avoid panic. For example, the generation AI analyzes disaster prevention radio information and weather information to estimate residents' emotional reactions. For example, it optimizes the information provision method by taking into account residents' anxiety and fear when an evacuation order is issued. The emotion estimation function can also be used to monitor residents' emotional reactions in real time and adjust the information provision method to avoid panic. For example, it can communicate evacuation orders in a calm tone. The generation AI can also analyze residents' emotional reactions and propose information provision methods that take emotions into account. For example, it can simultaneously provide messages that convey a sense of security. This allows the safest and fastest route to be calculated.

[0063] The evacuation destination suggestion unit can provide the user with information such as the address or contact information, capacity, and facility status of the evacuation destination. The evacuation destination suggestion unit provides the user with information such as the address or contact information, capacity, and facility status of the evacuation destination. For example, the information analyzed by the generation AI utilizes more diverse data sources, including disaster-related information from social media and news sites. For example, the generation AI collects disaster-related information from social media and news sites and analyzes it in combination with disaster prevention radio information and weather information. For example, disaster-related tweets from Twitter (registered trademark) are used for analysis. The generation AI also collects disaster alerts from news sites in real time and analyzes them in combination with disaster prevention radio information and weather information. For example, the content of news articles is reflected in the analysis. The generation AI also filters information from social media and news sites and uses only reliable information for analysis. For example, information from official accounts is preferentially collected. This allows detailed information about evacuation destinations to be provided to the user.

[0064] The generating AI can update information in real time according to the progress of the disaster and re-suggest optimal evacuation destinations or evacuation routes. The generating AI can, for example, update information in real time according to the progress of the disaster and re-suggest optimal evacuation destinations and evacuation routes. For example, disaster prevention radio information and weather information analyzed by the generating AI can be provided through a voice assistant. For example, evacuation instructions can be transmitted by voice using a smart speaker. Information can also be provided through the voice assistant in a format that is easy to understand for visually impaired people and the elderly. For example, concise and clear voice messages can be used. The information analyzed by the generating AI can also be linked to the voice assistant, allowing users to obtain information by voice command. For example, it can respond to voice commands such as "Tell me where to evacuate." This makes it possible to re-suggest optimal evacuation destinations and evacuation routes according to the progress of the disaster.

[0065] The generation AI can identify similar disaster patterns based on past disaster data and improve prediction accuracy. For example, the generation AI refers to past disaster data to identify similar disaster patterns and improve prediction accuracy. For example, the generation AI refers to disaster data such as past earthquakes and typhoons and compares it with current disaster prevention radio information and weather information. This identifies similar disaster patterns and improves prediction accuracy. For example, the generation AI predicts the damage situation in a specific area from past data. The generation AI also analyzes past disaster data and predicts the progression pattern of a disaster based on specific weather conditions and disaster prevention radio information. For example, the generation AI predicts the path of the current typhoon based on past typhoon path data. The generation AI also uses past disaster data to correct the analysis results of current disaster prevention radio information and weather information. For example, the generation AI improves the accuracy of current earthquake early warnings based on past earthquake data. In this way, prediction accuracy can be improved by referring to past disaster data.

[0066] The generation AI can visualize the extent of the disaster's impact on a map in real time based on the analyzed information and provide it to the user. For example, the generation AI visualizes the extent of the disaster's impact on a map in real time based on the analyzed information and provides it to the user. For example, the generation AI analyzes disaster prevention radio information and weather information and displays the results on a map in real time. For example, the generation AI visualizes the epicenter of an earthquake or the path of a typhoon on a map. Furthermore, based on the disaster information analyzed by the generation AI, the impact area is displayed in different colors on the map. For example, areas with heavy damage are displayed in red, and areas with less damage are displayed in yellow. Furthermore, the generation AI overlays the information analyzed on the map, allowing the user to intuitively grasp the extent of the disaster's impact. For example, areas where evacuation orders have been issued are displayed on the map. In this way, the extent of the disaster's impact is visualized on a map in real time, allowing the user to intuitively grasp the situation of the disaster.

[0067] The generation AI can use its emotion estimation function to take into account residents' emotional reactions when analyzing disaster prevention radio information or weather information, thereby optimizing how information is provided to avoid panic. For example, the generation AI can use its emotion estimation function to take into account residents' emotional reactions when analyzing disaster prevention radio information or weather information, thereby optimizing how information is provided to avoid panic. For example, the generation AI analyzes disaster prevention radio information or weather information and estimates residents' emotional reactions. For example, it optimizes how information is provided, taking into account residents' anxiety and fear when an evacuation order is issued. The emotion estimation function can also be used to monitor residents' emotional reactions in real time and adjust how information is provided to avoid panic. For example, it can communicate evacuation orders in a calm tone. The generation AI can also analyze residents' emotional reactions and propose how information is provided that takes their emotions into consideration. For example, it can simultaneously provide a message that gives a sense of security. In this way, by taking residents' emotional reactions into consideration, it is possible to optimize how information is provided to avoid panic.

[0068] The generation AI can utilize more diverse data sources, including disaster-related information from social media or news sites, for the information it analyzes. For example, the generation AI collects disaster-related information from social media or news sites, and analyzes it by integrating it with disaster prevention radio information and weather information. For example, disaster-related tweets from Twitter (registered trademark) are used for analysis. The generation AI also collects disaster alerts from news sites in real time and analyzes them in combination with disaster prevention radio information and weather information. For example, the content of news articles is reflected in the analysis. The generation AI also filters information from social media and news sites, and uses only highly reliable information for analysis. For example, it prioritizes the collection of information from official accounts. This allows for the utilization of more diverse data sources, thereby improving the accuracy of the analysis.

[0069] The generating AI provides the analyzed information to the user through a voice assistant, making it possible to accommodate the visually impaired or elderly. The generating AI, for example, provides analyzed disaster prevention radio information or weather information through the voice assistant. For example, evacuation instructions can be given aloud using a smart speaker. Information can also be provided through the voice assistant in a format that is easy for the visually impaired and elderly to understand. For example, concise and clear voice messages can be used. The generating AI can also link the analyzed information to the voice assistant, allowing the user to obtain information through voice commands. For example, it can respond to voice commands such as "tell me where to evacuate." This makes it possible to provide information to a wider range of users by accommodating the visually impaired and elderly.

[0070] The generation AI can use its emotion estimation function to monitor the emotions of users who receive disaster information in real time and provide additional information at the appropriate time. The generation AI, for example, uses its emotion estimation function to monitor the emotions of users who receive disaster information in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also builds a system that provides additional information at the appropriate time based on the user's emotional response. For example, it sends a message that gives a sense of security when the user feels anxious. It also adjusts the way information is provided according to the user's emotions based on the emotion estimation data. For example, it provides detailed information if the user is calm, and gives simple instructions if the user is panicking. In this way, by monitoring the user's emotions in real time, it can provide additional information at the appropriate time.

[0071] When acquiring the home's location information, the location information acquisition unit can suggest safer evacuation destinations based on the building's earthquake resistance or surrounding topographical information. The location information acquisition unit, for example, acquires the home's location information and references earthquake resistance data for surrounding buildings. For example, it prioritizes suggesting areas with many highly earthquake-resistant buildings as evacuation destinations. The location information acquisition unit also analyzes surrounding topographical information based on the home's location information to suggest evacuation destinations with a low risk of earthquakes or tsunamis. For example, it prioritizes suggesting evacuation sites on high ground. The location information acquisition unit also considers the building's earthquake resistance and topographical information to build a system that suggests optimal evacuation destinations. For example, it suggests avoiding areas with many buildings with low earthquake resistance. In this way, safer evacuation destinations can be suggested by taking into account the building's earthquake resistance and topographical information.

[0072] The generation AI can reflect the congestion status of evacuation shelters or the availability of facilities in real time when proposing evacuation destinations. For example, the generation AI can reflect the congestion status of evacuation shelters and the availability of facilities in real time when proposing evacuation destinations. For example, the generation AI can collect information on the congestion status of evacuation shelters in real time and reflect this in its evacuation destination suggestions. For example, it can suggest evacuation destinations that avoid crowded evacuation shelters. The generation AI can also collect information on the availability of facilities at evacuation shelters in real time and reflect this in its evacuation destination suggestions. For example, it can make suggestions that take into account the supply of toilets and water. The generation AI can also update the congestion status of evacuation shelters and the availability of facilities in real time, building a system that suggests optimal evacuation destinations. For example, it can make suggestions based on the capacity of the evacuation shelter. This allows the congestion status of evacuation shelters and the availability of facilities to be reflected in real time, making it possible to suggest more appropriate evacuation destinations.

[0073] The generation AI can use the emotion estimation function to evaluate the user's anxiety or stress level and suggest the optimal evacuation destination based on that. The generation AI, for example, uses the emotion estimation function to evaluate the user's anxiety and stress level in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also builds a system that suggests the optimal evacuation destination based on the user's anxiety and stress level. For example, it suggests a nearby, safe evacuation destination for a user with high stress levels. It also suggests evacuation destinations that correspond to the user's emotions based on the emotion estimation data. For example, it prioritizes suggesting evacuation destinations that give a sense of security. This makes it possible to suggest more appropriate evacuation destinations by evaluating the user's anxiety and stress levels.

[0074] The generation AI can also include places where pets are allowed or barrier-free shelters in the evacuation destinations it suggests. For example, the generation AI can collect information on pet-friendly shelters and reflect it in evacuation destination suggestions. For example, it can suggest pet-friendly shelters to households with pets. The generation AI can also collect information on barrier-free shelters and reflect it in evacuation destination suggestions. For example, it can suggest barrier-free shelters to households with elderly or disabled people. In addition, a system can be built in which the generation AI prioritizes suggesting places where pets are allowed or barrier-free shelters. For example, it can make suggestions based on information about the shelter's facilities. This makes it possible to meet a wider variety of needs by including places where pets are allowed or barrier-free shelters.

[0075] The generating AI can add a function that allows the user to share the evacuation destination it suggests with their family or friends, ensuring a means of communication. For example, the generating AI can add a function that allows the user to share the evacuation destination suggested by the generating AI with their family and friends. For example, by sending evacuation destination information via SMS or email. In addition, an app can be developed that allows users to share evacuation destination information with their family and friends. For example, a function can be provided that allows the user to share evacuation destination maps and contact information. In addition, a system can be built that allows the evacuation destination suggested by the generating AI to be shared with the user's family and friends in real time. For example, the evacuation destination information can be shared on the cloud. In this way, by sharing the evacuation destination, a means of communication can be secured and cooperation with family and friends can be strengthened.

[0076] The generation AI can use the emotion estimation function to simultaneously provide a message that gives a sense of security based on the user's emotions when suggesting an evacuation destination. For example, the generation AI uses the emotion estimation function to evaluate the user's emotions in real time when suggesting an evacuation destination. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. In addition, a system is built that simultaneously provides a message that gives a sense of security according to the user's emotions. For example, a message such as "A safe evacuation destination has been found" is displayed. Furthermore, based on the emotion estimation data, a message that takes the user's emotions into consideration is provided. For example, if the user is feeling anxious, an encouraging message is sent. This allows for a reduction in anxiety during evacuation by providing a message that gives a sense of security that takes the user's emotions into consideration.

[0077] When presenting an evacuation route, the generation AI can detect dangerous areas on the evacuation route in real time and propose an avoiding route. For example, when presenting an evacuation route, the generation AI can detect dangerous areas on the evacuation route in real time and propose an avoiding route. For example, the generation AI can detect dangerous areas on the evacuation route in real time and propose an avoiding route. For example, it can calculate a route that avoids buildings at risk of collapse or flooded areas. The generation AI can also display dangerous areas on the evacuation route on a map, presenting them visually to the user in an easy-to-understand manner. For example, it can display dangerous areas in red and safe routes in green. Furthermore, a system can be built in which the generation AI proposes the optimal evacuation route based on information on dangerous areas that is updated in real time. For example, the route can be recalculated every time road conditions change. This allows the system to detect dangerous areas on the evacuation route in real time and propose an avoiding route, thereby supporting safe evacuation.

[0078] When presenting an evacuation route, the generation AI can calculate the optimal route based on the user's means of transportation. For example, when presenting an evacuation route, the generation AI takes the user's means of transportation into consideration and calculates the optimal route. For example, the generation AI calculates the optimal evacuation route by taking the user's means of transportation into consideration. For example, if walking, it will prioritize pedestrian-only roads, and if driving, it will choose wide roads. The generation AI also displays routes based on the user's means of transportation on a map, presenting them visually in an easy-to-understand manner. For example, walking routes and driving routes will be displayed in different colors. We will also build a system in which the generation AI detects the user's means of transportation in real time and proposes the optimal evacuation route accordingly. For example, if the user is riding a bicycle, it will prioritize bicycle-only roads. This allows us to calculate the optimal route based on the user's means of transportation and provide a more appropriate evacuation route.

[0079] The generation AI can use the emotion estimation function to evaluate the user's stress level and suggest a route to reduce stress. The generation AI, for example, uses the emotion estimation function to evaluate the user's stress level in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also builds a system that suggests routes to reduce stress based on the user's stress level. For example, it suggests routes that go along quiet roads or through parks. It also suggests evacuation routes based on the emotion estimation data according to the user's emotions. For example, it suggests a short and safe route for a user with high stress levels. This makes it possible to support a more comfortable evacuation by evaluating the user's stress level and suggesting routes to reduce stress.

[0080] When presenting an evacuation route, the generation AI can also display the locations of shelters or medical facilities along the route, thereby supporting emergency response. For example, when presenting an evacuation route, the generation AI can display the locations of shelters and medical facilities along the route on a map, providing a visually easy-to-understand presentation to the user. For example, the generation AI can display shelters and medical facilities as icons. The generation AI can also update information about shelters and medical facilities along the evacuation route in real time and provide it to the user. For example, it can display the capacity of the shelter and the availability of the medical facility. The generation AI can also build a system that takes into account the locations of shelters and medical facilities along the evacuation route and proposes the optimal evacuation route. For example, it can propose a route that includes a stop at the shelter or medical facility. This can support emergency response by displaying the locations of shelters and medical facilities along the evacuation route.

[0081] When presenting an evacuation route, the generation AI can suggest a reasonable route based on the user's physical strength or health condition. The generation AI, for example, takes into account the user's physical strength and health condition to suggest a reasonable evacuation route. For example, it may suggest a short, flat route for the elderly and children. In addition, a system can be built in which the generation AI monitors the user's health condition in real time and suggests the optimal evacuation route accordingly. For example, it adjusts the route based on heart rate and number of steps. The generation AI also displays the evacuation route on a map based on the user's physical strength and health condition, presenting it in an easy-to-understand visual manner. For example, routes are displayed in different colors according to physical strength. This makes it possible to suggest a reasonable route by taking into account the user's physical strength and health condition.

[0082] The generation AI can use the emotion estimation function to simultaneously provide a message that gives a sense of security based on the user's emotions when presenting an evacuation route. For example, the generation AI uses the emotion estimation function to evaluate the user's emotions in real time when presenting an evacuation route. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. In addition, a system is built that simultaneously provides a message that gives a sense of security according to the user's emotions. For example, a message such as "This route is safe" is displayed. In addition, a message that takes the user's emotions into consideration is provided based on the emotion estimation data. For example, if the user is feeling anxious, an encouraging message is sent. This allows for the provision of a message that takes the user's emotions into consideration and reduces anxiety during evacuation.

[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] The disaster prevention support system can also include a supply supply unit that monitors the supply status during a disaster in real time and provides necessary supplies. For example, it can monitor the inventory of food, water, and medicines at evacuation centers and arrange for additional supplies as needed. The supply supply unit can also provide supplies according to the specific needs of evacuees. For example, it can provide appropriate supplies to evacuees who require food for allergies or specific medicines. Furthermore, the supply supply unit can reallocate supplies between evacuation centers to ensure efficient supply. This allows evacuees to receive the supplies they need quickly, improving the quality of their lives in evacuation centers.

[0085] The disaster prevention support system can also be equipped with a health management unit that monitors the health status of evacuees. For example, it can measure the evacuees' body temperature and heart rate in real time and notify medical staff if any abnormalities are detected. The health management unit can also manage information on the evacuees' chronic illnesses and allergies, allowing them to provide appropriate medical care. Furthermore, the health management unit can take measures to prevent the spread of infectious diseases within the evacuation shelter. For example, it can isolate evacuees suspected of having an infectious disease to prevent them from infecting other evacuees. This helps protect the health of evacuees and support their safe evacuation.

[0086] The disaster prevention support system can also be equipped with a psychological support unit that provides psychological support to evacuees. For example, it can provide counseling services to reduce stress and anxiety among evacuees. The psychological support unit can also promote communication among evacuees and carry out activities to prevent feelings of isolation. Furthermore, the psychological support unit can monitor the emotional state of evacuees and dispatch professional psychological counselors as needed. This will support the mental health of evacuees and allow them to live in evacuation shelters with peace of mind.

[0087] The disaster prevention support system can also be equipped with an environmental management unit that monitors the environment of the evacuation shelter. For example, it can monitor the temperature, humidity, and air quality inside the shelter in real time to maintain a comfortable environment. The environmental management unit can also manage the cleaning status inside the shelter and take measures to maintain a hygienic environment. Furthermore, the environmental management unit can monitor the condition of the shelter's facilities and perform repairs and maintenance as necessary. This allows evacuees to live in a comfortable and safe environment.

[0088] The disaster prevention support system can also be equipped with a pet management section to ensure the safety of evacuees' pets. For example, it can manage pets' locations within the evacuation shelter to prevent conflicts with other evacuees. The pet management section can also monitor the health of pets and dispatch veterinarians as needed. The pet management section can also manage food and water supplies for pets, providing a comfortable environment for pets. This allows evacuees with pets to live in safety and peace of mind.

[0089] The disaster prevention support system can further include a mobility support unit that supports the movement of evacuees. For example, it can provide wheelchairs and walking aids to support the mobility of elderly people and people with disabilities. The mobility support unit can also operate shuttle buses to support movement between evacuation centers. Furthermore, the mobility support unit can optimize evacuees' travel routes and take measures to avoid congestion. This can help evacuees move safely and smoothly.

[0090] The disaster prevention support system can further include an information management unit that centrally manages information about evacuees. For example, it can centrally manage personal information, health information, and evacuation destination information about evacuees and quickly provide necessary information. The information management unit can also provide functions to support evacuees in contacting their families and friends. Furthermore, the information management unit can take measures to protect evacuees' information and ensure their privacy. This allows for appropriate management of evacuees' information and supports the rapid and accurate provision of information.

[0091] The disaster prevention support system can also include an education support section that supports the education of evacuees. For example, it can provide teaching materials and learning programs to support children's learning in evacuation shelters. The education support section can also provide disaster prevention education to evacuees and provide opportunities for them to learn how to respond in the event of a disaster. Furthermore, the education support section can provide vocational training programs to help evacuees improve their skills. This allows evacuees to continue learning even while they are in evacuation shelters, and to acquire skills that will be useful in their future lives.

[0092] The disaster prevention support system can also be equipped with a communication support unit that supports communication between evacuees. For example, it can provide bulletin boards and chat functions to make it easier for evacuees to share information with each other. The communication support unit can also provide communication methods to help evacuees contact their families and friends. Furthermore, the communication support unit can also build a system to collect evacuees' opinions and requests and reflect them in the operation of evacuation shelters. This can facilitate communication between evacuees and their families and improve the quality of life in evacuation shelters.

[0093] The disaster prevention support system can further include an emotional support unit that monitors the emotions of evacuees and provides support according to their emotions. For example, it can analyze the facial expressions and voices of evacuees to grasp their emotional state in real time. The emotional support unit can also provide messages according to the evacuees' emotions, giving them a sense of security. Furthermore, the emotional support unit can also build a system to provide necessary support based on the evacuees' emotional state. This makes it possible to provide support that takes into account the evacuees' emotions and reduce the stress of evacuation life.

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

[0095] Step 1: The disaster prevention radio information acquisition unit acquires disaster prevention radio information, such as evacuation instructions, disaster warnings, and emergency alerts, from the disaster prevention radio. Step 2: The weather information acquisition unit acquires weather information, such as earthquake alerts, weather forecasts, and weather warnings from the Japan Meteorological Agency. Step 3: The location information acquisition unit acquires location information of each household. For example, the location information of the household is acquired as GPS data, address information, or device location information. Step 4: The evacuation destination suggestion unit uses the generation AI to analyze the information acquired by the disaster prevention radio information acquisition unit and the weather information acquisition unit, and suggests the optimal evacuation destination based on the location information acquired by the location information acquisition unit. For example, the generation AI suggests the optimal evacuation destination taking into account the evacuation site's capacity, accessible routes, and safety. Step 5: The evacuation route presentation unit presents the optimal evacuation route to the evacuation destination proposed by the evacuation destination proposal unit. For example, the generation AI presents the optimal evacuation route taking into account road congestion and road closures due to the disaster, and displays the evacuation route on a map to make it visually easy for the user to understand.

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

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

[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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0124] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

[0140] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[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. This system uses AI to analyze local disaster prevention radio information and weather information, and suggests optimal evacuation destinations based on the location information of each household. a disaster prevention radio information acquisition unit that acquires disaster prevention radio information; a weather information acquisition unit for acquiring weather information; a location information acquisition unit that acquires location information of each household; an evacuation destination suggestion unit that analyzes the information acquired by the disaster prevention radio information acquisition unit and the weather information acquisition unit using the generation AI and suggests an optimal evacuation destination based on the location information acquired by the location information acquisition unit; an evacuation route presentation unit that presents an optimal evacuation route to the evacuation destination proposed by the evacuation destination proposal unit; A system characterized by:

2. The disaster prevention radio information acquisition unit Obtaining evacuation instructions from disaster prevention radio and earthquake early warning information from the Japan Meteorological Agency, and analyzing them to understand the disaster situation 2. The system of claim 1.

3. The location information acquisition unit Obtaining household location information as GPS data and using that data to identify the nearest safe evacuation site 2. The system of claim 1.

4. The evacuation route presentation unit Calculate the safest and fastest route based on road congestion or road closures due to disasters 2. The system of claim 1.

5. The evacuation destination suggestion unit Provide users with information on evacuation destination addresses or contact details, capacity, and facility status 2. The system of claim 1.

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