System

Abstract

An object of a system according to an embodiment is to provide information on an evacuation site at the time of occurrence of a disaster and support rescue activities.SOLUTION: A system includes a navigation unit, a visualization unit, a registration unit, an upload unit, and a matching unit. The navigation unit navigates to the nearest evacuation site according to the type of disaster. The visualization unit visualizes an opening status and an accommodation status of the shelter. The registration unit registers information of a rescue requester and shares the information on a map. The upload unit uploads an image or a moving image of a disaster area to visualize a dangerous area or a non-passable area. The matching unit matches a volunteer requester with a volunteer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not provide sufficient information on evacuation sites or support rescue efforts when a disaster occurs, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information on evacuation sites in the event of a disaster and to support rescue operations. [Means for solving the problem]

[0006] The system according to the embodiment includes a navigation unit, a visualization unit, a registration unit, an upload unit, and a matching unit. The navigation unit navigates to the nearest evacuation site depending on the type of disaster. The visualization unit visualizes the opening status and accommodation status of evacuation sites. The registration unit registers information on people in need of rescue and shares it on a map. The upload unit uploads images and videos of the disaster area and visualizes dangerous areas and impassable areas. The matching unit matches people requesting volunteers with volunteers. [Effects of the Invention]

[0007] The system according to the embodiment can provide information on evacuation sites when a disaster occurs and support rescue operations. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A support system according to an embodiment of the present invention resolves various challenges faced by disaster victims and provides prompt and efficient support. When a disaster occurs, this system navigates to the nearest evacuation site depending on the type of disaster and visualizes the availability and accommodation status of evacuation sites. It also supports rescue operations by registering information about people needing rescue and sharing it on a map. Furthermore, by uploading images and videos of the disaster-stricken area, dangerous and impassable areas are visualized on a map. Finally, it provides a function for matching volunteer requesters with volunteers. This enables the support system to resolve various challenges faced by disaster victims and provide prompt and efficient support. For example, navigation to evacuation sites enables rapid evacuation, and visualization of the availability and accommodation status of evacuation sites allows appropriate evacuation sites to be selected. Furthermore, sharing information about people needing rescue improves the efficiency of rescue operations, and visualization of dangerous areas in the disaster-stricken area enables safe travel. Furthermore, matching volunteers allows necessary support to be provided promptly.

[0029] The support system according to the embodiment includes a navigation unit, a visualization unit, a registration unit, an upload unit, and a matching unit. The navigation unit navigates to the nearest evacuation site depending on the type of disaster. For example, the generation AI receives information on the type of disaster (earthquake, flood, fire, etc.) and the current location as input and suggests the optimal evacuation site. The generation AI provides guidance in the form of, for example, "An earthquake has occurred. The nearest evacuation site is XX Park. Please head this way." The visualization unit visualizes the opening and accommodating status of evacuation sites. For example, the generation AI collects and analyzes evacuation site data and displays currently open evacuation sites and the number of people they can accommodate. The generation AI provides information such as, "XX evacuation site is currently open and can accommodate 50 people." The registration unit registers information on people needing rescue and shares it on a map. For example, the generation AI analyzes the location and situation of the person needing rescue and suggests the optimal rescue route to the rescue team. The generation AI provides guidance in the form of, for example, "A person needing rescue is at XX location. Please head this way." The uploading unit uploads images and videos of the disaster-stricken area and visualizes dangerous and impassable areas. For example, the generation AI analyzes the uploaded images and videos to identify dangerous and impassable areas. The generation AI provides information such as, "This area is impassable. Please select an alternative route." The matching unit matches volunteer requesters with volunteers. For example, the generation AI analyzes the requester's needs and the volunteers' skills and location information to optimally match them. The generation AI provides guidance such as, "Mr. / Ms. XX needs a volunteer. Your skills would be helpful." This enables the support system according to the embodiment to resolve various challenges faced by disaster victims and provide prompt and efficient support. For example, navigation to evacuation sites enables rapid evacuation, and visualization of the opening and occupancy status of evacuation sites allows appropriate evacuation sites to be selected. Furthermore, sharing information about those in need of rescue improves the efficiency of rescue operations, and visualization of dangerous areas in the disaster-stricken area enables safe travel. Furthermore, matching volunteers ensures the prompt provision of necessary support.

[0030] The navigation unit can propose the optimal evacuation route by taking into account the user's movement speed and health condition. For example, the generation AI in the navigation unit measures the user's movement speed in real time and dynamically adjusts the evacuation route. For example, for elderly people who walk slowly, it will propose a safe and flat route rather than the shortest distance. The generation AI in the navigation unit also considers the user's health condition and adjusts the evacuation route. For example, for a user in poor health, it will propose an evacuation route close to medical facilities. This makes it possible to propose the optimal evacuation route based on the user's movement speed and health condition.

[0031] The navigation unit can select evacuation sites taking into account not only the type of disaster, but also the time of day and weather information. In the navigation unit, for example, the generation AI obtains real-time weather information and adjusts the evacuation route. For example, if it is raining heavily, it will suggest a route with a low risk of flooding. The navigation unit also adjusts the evacuation route taking into account the time of day. For example, if it is nighttime, it will suggest a route with good lighting. This makes it possible to select evacuation sites taking into account the time of day and weather information.

[0032] The navigation unit can reflect the operating status of public transportation in real time. In the navigation unit, for example, the generation AI obtains the operating status of public transportation in real time and adjusts evacuation routes. For example, it proposes evacuation routes using buses and trains that are currently in operation. In addition, the navigation unit proposes the optimal evacuation route by having the generation AI reflect the operating status of public transportation in real time. For example, it proposes an evacuation route using public transportation that is currently in operation. This makes it possible to propose evacuation routes that reflect the operating status of public transportation in real time.

[0033] The navigation unit integrates the location information of pets and family members, and can suggest routes that allow everyone to evacuate together. For example, the generation AI of the navigation unit obtains the location information of pets and family members in real time, and suggests routes that allow everyone to evacuate together. For example, it can guide you to a route that passes through a waypoint where all family members can gather. The navigation unit also integrates the location information of pets and family members, and suggests routes that allow everyone to evacuate together. For example, it can suggest routes that allow you to evacuate together with your pets. This makes it possible to suggest routes that allow you to evacuate together with your pets and family members.

[0034] The visualization unit can display the accommodation status of evacuation shelters in detail based on the age group and special needs of the evacuees. For example, the generation AI in the visualization unit analyzes the age group of evacuees and displays the accommodation status of the evacuation shelter in detail. For example, it can identify evacuation shelters with a high percentage of elderly people and prepare for the need for medical assistance. The visualization unit can also analyze the special needs of evacuees and display the accommodation status of the evacuation shelter in detail. For example, it can identify evacuation shelters with a high percentage of disabled people and make them barrier-free. This makes it possible to display the accommodation status in detail based on the age group and special needs of the evacuees.

[0035] The visualization unit can predict the opening status of evacuation shelters by comparing it with past data and predict future capacity. In the visualization unit, for example, the generation AI analyzes past evacuation shelter opening data and predicts future capacity. For example, it predicts the capacity of evacuation shelters based on data from past disasters. In addition, the visualization unit predicts the opening status of evacuation shelters by comparing it with past data. For example, it predicts the opening status of evacuation shelters based on past data. This makes it possible to predict future capacity based on a prediction made in comparison with past data.

[0036] The visualization unit can share the opening and occupancy status of evacuation centers with other disaster response organizations in real time. In the visualization unit, for example, the generation AI collects the opening and occupancy status of evacuation centers in real time and shares it with other disaster response organizations. For example, sharing information with the fire department and police enables a rapid response. In addition, the visualization unit enables efficient disaster response by having the generation AI share the opening and occupancy status of evacuation centers in real time. For example, the opening and occupancy status of evacuation centers is shared with other disaster response organizations. This enables efficient disaster response by sharing information with other disaster response organizations in real time.

[0037] The visualization unit can display the accommodation status of the evacuation shelter, including the accommodation status of the evacuees' pets. In the visualization unit, for example, the generation AI collects information about the evacuees' pets and reflects this in the accommodation status of the evacuation shelter. For example, the visualization unit identifies and guides to evacuation shelters that can accommodate pets. Furthermore, the visualization unit displays the accommodation status of the evacuation shelter in detail by having the generation AI display the accommodation status of the evacuees' pets as well. For example, the visualization unit displays the accommodation status of the evacuation shelter, including the accommodation status of pets. This makes it possible to display the accommodation status, including the accommodation status of the evacuees' pets.

[0038] The registration unit can automatically update the information of the person in need of rescue and notify the rescue team in real time. For example, the generation AI in the registration unit tracks the location information of the person in need of rescue in real time and automatically updates the information. For example, if the person in need of rescue moves, the registration unit notifies the rescue team. The generation AI in the registration unit also automatically updates the information of the person in need of rescue and notifies the rescue team in real time. For example, if the situation of the person in need of rescue changes, the registration unit notifies the rescue team. This makes it possible to automatically update the information of the person in need of rescue and notify the rescue team in real time.

[0039] The registration unit can include information on the person in need of rescue, such as their health condition and chronic illnesses, to make the information useful during rescue operations. For example, the registration unit may register information on the person in need of rescue in advance to make the information useful during rescue operations. For example, an emergency medical team may be dispatched to a person in need of rescue who has heart disease. The registration unit also registers information on the person in need of rescue, including their health condition and chronic illnesses, to make the information useful during rescue operations. For example, allergy information may be registered to make the information useful during rescue operations. This makes it possible to use information on the person in need of rescue, including their health condition and chronic illnesses, to make the information useful during rescue operations.

[0040] The registration unit can improve the accuracy of information on persons requiring rescue by using drones or robots to confirm the location information of persons requiring rescue on site. The registration unit, for example, uses a drone to confirm the location information of persons requiring rescue on site and improve the accuracy of the information. For example, a drone confirms the location of a person requiring rescue from the air and notifies a rescue team. The registration unit also improves the accuracy by having the generation AI use a drone or robot to confirm the information of persons requiring rescue on site. For example, a robot confirms the status of a person requiring rescue and updates the information. This makes it possible to improve the accuracy of information on persons requiring rescue by using drones or robots to confirm the location on site.

[0041] The registration unit can link information on rescue victims with other rescue operation applications and centrally manage the information. The registration unit, for example, builds a system that links information on rescue victims with other rescue operation applications and centrally manages the information. For example, it collects and integrates information from multiple applications. The registration unit also links the generation AI with information on rescue victims with other rescue operation applications and centrally manages the information. For example, it links with a disaster information sharing app and centrally manages the information. This enables centralized management of information linked with other rescue operation applications.

[0042] The uploading unit can automatically analyze uploaded images and videos to assess the extent of damage. In the uploading unit, for example, the generation AI automatically analyzes uploaded images and videos to assess the extent of damage. For example, it analyzes the damage to buildings and quantifies the extent of damage. In addition, the uploading unit can automatically analyze images and videos to assess the extent of damage. For example, it analyzes the damage to roads and assesses the extent of damage. This makes it possible to assess the extent of damage by automatically analyzing uploaded images and videos.

[0043] The uploading unit can display changes in images and videos of the disaster-stricken area compared with past data, thereby visualizing the progress of the damage. In the uploading unit, for example, the generation AI compares images and videos of the disaster-stricken area with past data and displays changes. For example, it compares the damage status of buildings with past images and visualizes the progress of the damage. In addition, the uploading unit can display changes in images and videos of the disaster-stricken area compared with past data and visualize the progress of the damage. For example, it compares the damage status of roads with past data and visualizes the progress of the damage. This makes it possible to visualize the progress of the damage by comparing images and videos of the disaster-stricken area with past data.

[0044] The uploading unit can share images and videos of the disaster-stricken area with other disaster response organizations in real time. In the uploading unit, for example, the generation AI collects images and videos of the disaster-stricken area in real time and shares them with other disaster response organizations. For example, information can be shared with fire departments and police, enabling a rapid response. In addition, the uploading unit can share images and videos of the disaster-stricken area with other disaster response organizations in real time. For example, images and videos of the disaster-stricken area can be shared with other disaster response organizations. This enables efficient disaster response by sharing information with other disaster response organizations in real time.

[0045] The uploading unit converts images and videos of the disaster-stricken area into a 3D model, enabling the damage situation to be visualized in three dimensions. In the uploading unit, for example, the generation AI converts images and videos of the disaster-stricken area into a 3D model, thereby visualizing the damage situation in three dimensions. For example, the damage situation of buildings is displayed in a 3D model. In addition, the uploading unit converts images and videos of the disaster-stricken area into a 3D model, thereby visualizing the damage situation in three dimensions. For example, the damage situation of roads is displayed in a 3D model. This makes it possible to visualize the damage situation in three dimensions by converting images and videos of the disaster-stricken area into a 3D model.

[0046] The matching unit can analyze in detail the needs of those requesting volunteers and suggest the most suitable volunteers. For example, the generation AI in the matching unit analyzes in detail the needs of those requesting volunteers and suggests the most suitable volunteers. For example, if medical support is needed, it will suggest volunteers with medical skills. The matching unit can also analyze in detail the needs of those requesting volunteers and suggest the most suitable volunteers. For example, if building repairs are needed, it will suggest volunteers with construction skills. This makes it possible to analyze in detail the needs of those requesting volunteers and suggest the most suitable volunteers.

[0047] The matching unit can evaluate the skills and experience of volunteers and match appropriate volunteers to requesters. For example, the generation AI in the matching unit evaluates the skills and experience of volunteers and matches appropriate volunteers to requesters. For example, it matches volunteers with medical skills to requesters who need medical support. The generation AI in the matching unit also evaluates the skills and experience of volunteers and matches appropriate volunteers to requesters. For example, it matches volunteers with construction skills to requesters who need building repairs. This makes it possible to evaluate the skills and experience of volunteers and match appropriate volunteers to requesters.

[0048] The matching unit can match volunteer requesters with volunteers in cooperation with other volunteer platforms. In the matching unit, for example, the generation AI works with other volunteer platforms to match requesters with volunteers. For example, it collects information from multiple platforms and makes the optimal match. In addition, the matching unit can work with other volunteer platforms to match requesters with volunteers. For example, it works with local volunteer organizations to match requesters with volunteers. This makes it possible to perform matching in cooperation with other volunteer platforms.

[0049] The matching unit can automatically update the skills of volunteers and perform matching based on the latest information. For example, the generation AI in the matching unit automatically updates the skills of volunteers and performs matching based on the latest information. For example, newly acquired skills are registered in a database and matching is performed according to the needs of the requester. The matching unit can also automatically update the skills of volunteers using the generation AI and perform matching based on the latest information. For example, regular skill checks are performed and matching is performed based on the latest information. This makes it possible to automatically update the skills of volunteers and perform matching based on the latest information.

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

[0051] The navigation unit can propose the optimal evacuation route by taking into account the user's movement speed and health condition. For example, the generation AI measures the user's movement speed in real time and dynamically adjusts the evacuation route. For example, for elderly people who walk slowly, it will propose a safe and flat route rather than the shortest distance. The navigation unit also adjusts the evacuation route by taking into account the user's health condition. For example, for a user in poor health, it will propose an evacuation route close to medical facilities. This makes it possible to propose the optimal evacuation route based on the user's movement speed and health condition.

[0052] The navigation unit can select evacuation sites taking into account not only the type of disaster, but also the time of day and weather information. For example, the generation AI obtains real-time weather information and adjusts evacuation routes. For example, if there is heavy rain, it will suggest a route with a low risk of flooding. The navigation unit also adjusts evacuation routes taking into account the time of day. For example, if it is nighttime, it will suggest a route with good lighting. This makes it possible to select evacuation sites taking into account the time of day and weather information.

[0053] The navigation unit can reflect the operating status of public transportation in real time. For example, the generation AI obtains the operating status of public transportation in real time and adjusts evacuation routes. For example, it can propose evacuation routes using buses and trains that are currently in operation. The navigation unit also proposes optimal evacuation routes by having the generation AI reflect the operating status of public transportation in real time. For example, it can propose evacuation routes using public transportation that is currently in operation. This makes it possible to propose evacuation routes that reflect the operating status of public transportation in real time.

[0054] The navigation unit integrates the location information of pets and family members, and can suggest routes that allow everyone to evacuate together. For example, the generation AI obtains the location information of pets and family members in real time, and suggests routes that allow everyone to evacuate together. For example, it can guide you to a route that passes through an intermediate point where all family members can gather. The navigation unit also integrates the location information of pets and family members, and suggests routes that allow everyone to evacuate together. For example, it can suggest routes that allow you to evacuate together with your pets. This makes it possible to suggest routes that allow you to evacuate together with your pets and family members.

[0055] The visualization unit can display the accommodation status of evacuation shelters in detail based on the age group and special needs of the evacuees. For example, the generation AI can analyze the age group of the evacuees and display the accommodation status of the evacuation shelter in detail. For example, it can identify evacuation shelters with a high percentage of elderly people and prepare for the need for medical assistance. The visualization unit can also analyze the special needs of evacuees and display the accommodation status of the evacuation shelter in detail. For example, it can identify evacuation shelters with a high percentage of disabled people and make them barrier-free. This makes it possible to display the accommodation status in detail based on the age group and special needs of the evacuees.

[0056] The visualization unit can predict the opening status of evacuation shelters by comparing it with past data and predicting future capacity. For example, the generation AI analyzes past evacuation shelter opening data and predicts future capacity. For example, it predicts the capacity of evacuation shelters based on data from past disasters. The visualization unit also predicts the opening status of evacuation shelters by comparing it with past data. For example, it predicts the opening status of evacuation shelters based on past data. This makes it possible to predict future capacity based on predictions made in comparison with past data.

[0057] The visualization unit can share the opening and occupancy status of evacuation centers with other disaster response organizations in real time. For example, the generation AI collects the opening and occupancy status of evacuation centers in real time and shares it with other disaster response organizations. For example, sharing information with the fire department and police enables a rapid response. The visualization unit also enables efficient disaster response by having the generation AI share the opening and occupancy status of evacuation centers in real time. For example, sharing the opening and occupancy status of evacuation centers with other disaster response organizations. This enables efficient disaster response by sharing information with other disaster response organizations in real time.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The navigation unit navigates to the nearest evacuation site depending on the type of disaster. For example, the generation AI receives the type of disaster (earthquake, flood, fire, etc.) and current location information as input, and suggests the optimal evacuation site. For example, the generation AI provides guidance such as, "An earthquake has occurred. The nearest evacuation site is XX Park. Please head this way." Step 2: The visualization unit visualizes the opening status and occupancy status of evacuation shelters. For example, the generation AI collects and analyzes data on evacuation shelters and displays the currently open evacuation shelters and the number of people they can accommodate. For example, the generation AI provides information such as, "X evacuation shelter is currently open and can accommodate 50 people." Step 3: The registration unit registers information about the person in need of rescue and shares it on a map. For example, the generation AI analyzes the location and situation of the person in need of rescue and suggests the optimal rescue route to the rescue team. For example, the generation AI provides guidance such as, "The person in need of rescue is at location XX. Please head this way." Step 4: The uploading unit uploads images and videos of the affected area and visualizes dangerous and impassable areas. For example, the generating AI analyzes the uploaded images and videos and identifies dangerous and impassable areas. For example, the generating AI provides information such as, "This area is impassable. Please select an alternative route." Step 5: The matching unit matches volunteer requesters with volunteers. For example, the generation AI analyzes the needs of the requester and the skills and location information of the volunteers to make the optimal match. For example, the generation AI provides guidance such as, "Mr. / Ms. XX needs a volunteer. Your skills would be useful."

[0060] (Example 2) A support system according to an embodiment of the present invention resolves various challenges faced by disaster victims and provides prompt and efficient support. When a disaster occurs, this system navigates to the nearest evacuation site depending on the type of disaster and visualizes the availability and accommodation status of evacuation sites. It also supports rescue operations by registering information about people needing rescue and sharing it on a map. Furthermore, by uploading images and videos of the disaster-stricken area, dangerous and impassable areas are visualized on a map. Finally, it provides a function for matching volunteer requesters with volunteers. This enables the support system to resolve various challenges faced by disaster victims and provide prompt and efficient support. For example, navigation to evacuation sites enables rapid evacuation, and visualization of the availability and accommodation status of evacuation sites allows appropriate evacuation sites to be selected. Furthermore, sharing information about people needing rescue improves the efficiency of rescue operations, and visualization of dangerous areas in the disaster-stricken area enables safe travel. Furthermore, matching volunteers allows necessary support to be provided promptly.

[0061] The support system according to the embodiment includes a navigation unit, a visualization unit, a registration unit, an upload unit, and a matching unit. The navigation unit navigates to the nearest evacuation site depending on the type of disaster. For example, the generation AI receives information on the type of disaster (earthquake, flood, fire, etc.) and the current location as input and suggests the optimal evacuation site. The generation AI provides guidance in the form of, for example, "An earthquake has occurred. The nearest evacuation site is XX Park. Please head this way." The visualization unit visualizes the opening and accommodating status of evacuation sites. For example, the generation AI collects and analyzes evacuation site data and displays currently open evacuation sites and the number of people they can accommodate. The generation AI provides information such as, "XX evacuation site is currently open and can accommodate 50 people." The registration unit registers information on people needing rescue and shares it on a map. For example, the generation AI analyzes the location and situation of the person needing rescue and suggests the optimal rescue route to the rescue team. The generation AI provides guidance in the form of, for example, "A person needing rescue is at XX location. Please head this way." The uploading unit uploads images and videos of the disaster-stricken area and visualizes dangerous and impassable areas. For example, the generation AI analyzes the uploaded images and videos to identify dangerous and impassable areas. The generation AI provides information such as, "This area is impassable. Please select an alternative route." The matching unit matches volunteer requesters with volunteers. For example, the generation AI analyzes the requester's needs and the volunteers' skills and location information to optimally match them. The generation AI provides guidance such as, "Mr. / Ms. XX needs a volunteer. Your skills would be helpful." This enables the support system according to the embodiment to resolve various challenges faced by disaster victims and provide prompt and efficient support. For example, navigation to evacuation sites enables rapid evacuation, and visualization of the opening and occupancy status of evacuation sites allows appropriate evacuation sites to be selected. Furthermore, sharing information about those in need of rescue improves the efficiency of rescue operations, and visualization of dangerous areas in the disaster-stricken area enables safe travel. Furthermore, matching volunteers ensures the prompt provision of necessary support.

[0062] The navigation unit can propose the optimal evacuation route by taking into account the user's movement speed and health condition. For example, the generation AI in the navigation unit measures the user's movement speed in real time and dynamically adjusts the evacuation route. For example, for elderly people who walk slowly, it will propose a safe and flat route rather than the shortest distance. The generation AI in the navigation unit also considers the user's health condition and adjusts the evacuation route. For example, for a user in poor health, it will propose an evacuation route close to medical facilities. This makes it possible to propose the optimal evacuation route based on the user's movement speed and health condition.

[0063] The navigation unit can select evacuation sites taking into account not only the type of disaster, but also the time of day and weather information. In the navigation unit, for example, the generation AI obtains real-time weather information and adjusts the evacuation route. For example, if it is raining heavily, it will suggest a route with a low risk of flooding. The navigation unit also adjusts the evacuation route taking into account the time of day. For example, if it is nighttime, it will suggest a route with good lighting. This makes it possible to select evacuation sites taking into account the time of day and weather information.

[0064] The navigation unit uses an emotion estimation function to evaluate the user's stress level and suggest an evacuation route to reduce stress. For example, the generation AI in the navigation unit analyzes the user's facial expressions and voice to evaluate the stress level in real time. For example, if stress is high, it will suggest a quiet route. The navigation unit also uses the generation AI to evaluate the user's stress level and suggest an evacuation route to reduce stress. For example, if stress is high, it will suggest a route with a rich natural environment. This makes it possible to suggest an evacuation route according to the user's stress level.

[0065] The navigation unit can reflect the operating status of public transportation in real time. In the navigation unit, for example, the generation AI obtains the operating status of public transportation in real time and adjusts evacuation routes. For example, it proposes evacuation routes using buses and trains that are currently in operation. In addition, the navigation unit proposes the optimal evacuation route by having the generation AI reflect the operating status of public transportation in real time. For example, it proposes an evacuation route using public transportation that is currently in operation. This makes it possible to propose evacuation routes that reflect the operating status of public transportation in real time.

[0066] The navigation unit integrates the location information of pets and family members, and can suggest routes that allow everyone to evacuate together. For example, the generation AI of the navigation unit obtains the location information of pets and family members in real time, and suggests routes that allow everyone to evacuate together. For example, it can guide you to a route that passes through a waypoint where all family members can gather. The navigation unit also integrates the location information of pets and family members, and suggests routes that allow everyone to evacuate together. For example, it can suggest routes that allow you to evacuate together with your pets. This makes it possible to suggest routes that allow you to evacuate together with your pets and family members.

[0067] The navigation unit can use the emotion estimation function to provide audio guidance and encouraging messages to reduce the user's anxiety during evacuation. For example, the generation AI in the navigation unit analyzes the user's facial expressions and voice and provides encouraging messages if the user is feeling anxious. For example, it sends a message such as, "Don't worry, you'll soon arrive at the evacuation site." The navigation unit can also use the emotion estimation function to provide audio guidance and encouraging messages to reduce the user's anxiety during evacuation. For example, it sends a message such as, "Don't worry, the evacuation site is nearby." This makes it possible to provide audio guidance and encouraging messages to reduce the user's anxiety during evacuation.

[0068] The visualization unit can display the accommodation status of evacuation shelters in detail based on the age group and special needs of the evacuees. For example, the generation AI in the visualization unit analyzes the age group of evacuees and displays the accommodation status of the evacuation shelter in detail. For example, it can identify evacuation shelters with a high percentage of elderly people and prepare for the need for medical assistance. The visualization unit can also analyze the special needs of evacuees and display the accommodation status of the evacuation shelter in detail. For example, it can identify evacuation shelters with a high percentage of disabled people and make them barrier-free. This makes it possible to display the accommodation status in detail based on the age group and special needs of the evacuees.

[0069] The visualization unit can predict the opening status of evacuation shelters by comparing it with past data and predict future capacity. In the visualization unit, for example, the generation AI analyzes past evacuation shelter opening data and predicts future capacity. For example, it predicts the capacity of evacuation shelters based on data from past disasters. In addition, the visualization unit predicts the opening status of evacuation shelters by comparing it with past data. For example, it predicts the opening status of evacuation shelters based on past data. This makes it possible to predict future capacity based on a prediction made in comparison with past data.

[0070] The visualization unit can use the emotion estimation function to evaluate the atmosphere of the evacuation shelter and the satisfaction of the evacuees, and provide this as reference information for selecting a shelter. In the visualization unit, for example, the generation AI analyzes the facial expressions and voices of the evacuees to evaluate the atmosphere of the evacuation shelter. For example, it identifies evacuation shelters where evacuees feel relaxed. In addition, the visualization unit uses the generation AI to evaluate the satisfaction of the evacuees, and provide this as reference information for selecting a shelter. For example, it identifies evacuation shelters where evacuees are highly satisfied. This makes it possible to provide reference information for selecting a shelter based on the atmosphere of the evacuation shelter and the satisfaction of the evacuees.

[0071] The visualization unit can share the opening and occupancy status of evacuation centers with other disaster response organizations in real time. In the visualization unit, for example, the generation AI collects the opening and occupancy status of evacuation centers in real time and shares it with other disaster response organizations. For example, sharing information with the fire department and police enables a rapid response. In addition, the visualization unit enables efficient disaster response by having the generation AI share the opening and occupancy status of evacuation centers in real time. For example, the opening and occupancy status of evacuation centers is shared with other disaster response organizations. This enables efficient disaster response by sharing information with other disaster response organizations in real time.

[0072] The visualization unit can display the accommodation status of the evacuation shelter, including the accommodation status of the evacuees' pets. In the visualization unit, for example, the generation AI collects information about the evacuees' pets and reflects this in the accommodation status of the evacuation shelter. For example, the visualization unit identifies and guides to evacuation shelters that can accommodate pets. Furthermore, the visualization unit displays the accommodation status of the evacuation shelter in detail by having the generation AI display the accommodation status of the evacuees' pets as well. For example, the visualization unit displays the accommodation status of the evacuation shelter, including the accommodation status of pets. This makes it possible to display the accommodation status, including the accommodation status of the evacuees' pets.

[0073] The visualization unit can use the emotion estimation function to provide psychological support according to the accommodation situation at the evacuation shelter. For example, the generation AI in the visualization unit analyzes the facial expressions and voices of evacuees to identify evacuees who need psychological support. For example, it sends a relaxing message to evacuees who are highly stressed. The visualization unit also allows the generation AI to provide psychological support according to the accommodation situation at the evacuation shelter. For example, it provides counseling to evacuees who need psychological support according to the accommodation situation at the evacuation shelter. This makes it possible to provide psychological support according to the accommodation situation at the evacuation shelter.

[0074] The registration unit can automatically update the information of the person in need of rescue and notify the rescue team in real time. For example, the generation AI in the registration unit tracks the location information of the person in need of rescue in real time and automatically updates the information. For example, if the person in need of rescue moves, the registration unit notifies the rescue team. The generation AI in the registration unit also automatically updates the information of the person in need of rescue and notifies the rescue team in real time. For example, if the situation of the person in need of rescue changes, the registration unit notifies the rescue team. This makes it possible to automatically update the information of the person in need of rescue and notify the rescue team in real time.

[0075] The registration unit can include information on the person in need of rescue, such as their health condition and chronic illnesses, to make the information useful during rescue operations. For example, the registration unit may register information on the person in need of rescue in advance to make the information useful during rescue operations. For example, an emergency medical team may be dispatched to a person in need of rescue who has heart disease. The registration unit also registers information on the person in need of rescue, including their health condition and chronic illnesses, to make the information useful during rescue operations. For example, allergy information may be registered to make the information useful during rescue operations. This makes it possible to use information on the person in need of rescue, including their health condition and chronic illnesses, to make the information useful during rescue operations.

[0076] The registration unit uses the emotion estimation function to evaluate the psychological state of the person in need of rescue and propose appropriate ways of responding to the rescue team. In the registration unit, for example, the generation AI analyzes the facial expressions and voice of the person in need of rescue and evaluates their psychological state. For example, a message to help the person in need of rescue who is under high stress is sent to help them relax. In addition, the registration unit uses the generation AI to evaluate the psychological state of the person in need of rescue and propose appropriate ways of responding to the rescue team. For example, counseling is suggested for the person in need of rescue who needs psychological support. This makes it possible to propose appropriate ways of responding according to the psychological state of the person in need of rescue.

[0077] The registration unit can improve the accuracy of information on persons requiring rescue by using drones or robots to confirm the location information of persons requiring rescue on site. The registration unit, for example, uses a drone to confirm the location information of persons requiring rescue on site and improve the accuracy of the information. For example, a drone confirms the location of a person requiring rescue from the air and notifies a rescue team. The registration unit also improves the accuracy by having the generation AI use a drone or robot to confirm the information of persons requiring rescue on site. For example, a robot confirms the status of a person requiring rescue and updates the information. This makes it possible to improve the accuracy of information on persons requiring rescue by using drones or robots to confirm the location on site.

[0078] The registration unit can link information on rescue victims with other rescue operation applications and centrally manage the information. The registration unit, for example, builds a system that links information on rescue victims with other rescue operation applications and centrally manages the information. For example, it collects and integrates information from multiple applications. The registration unit also links the generation AI with information on rescue victims with other rescue operation applications and centrally manages the information. For example, it links with a disaster information sharing app and centrally manages the information. This enables centralized management of information linked with other rescue operation applications.

[0079] The registration unit can use the emotion estimation function to automatically generate a message that gives a sense of security to the family of the person in need of rescue. For example, the generation AI in the registration unit analyzes the emotions of the family of the person in need of rescue and automatically generates a message that gives a sense of security. For example, it sends a message such as, "The person in need of rescue is safe. A rescue team is on the way." The registration unit also uses the emotion estimation function to automatically generate a message that gives a sense of security to the family of the person in need of rescue. For example, it sends a message such as, "The person in need of rescue is safe. Rescue operations are underway." This makes it possible to automatically generate a message that gives a sense of security to the family of the person in need of rescue.

[0080] The uploading unit can automatically analyze uploaded images and videos to assess the extent of damage. In the uploading unit, for example, the generation AI automatically analyzes uploaded images and videos to assess the extent of damage. For example, it analyzes the damage to buildings and quantifies the extent of damage. In addition, the uploading unit can automatically analyze images and videos to assess the extent of damage. For example, it analyzes the damage to roads and assesses the extent of damage. This makes it possible to assess the extent of damage by automatically analyzing uploaded images and videos.

[0081] The uploading unit can display changes in images and videos of the disaster-stricken area compared with past data, thereby visualizing the progress of the damage. In the uploading unit, for example, the generation AI compares images and videos of the disaster-stricken area with past data and displays changes. For example, it compares the damage status of buildings with past images and visualizes the progress of the damage. In addition, the uploading unit can display changes in images and videos of the disaster-stricken area compared with past data and visualize the progress of the damage. For example, it compares the damage status of roads with past data and visualizes the progress of the damage. This makes it possible to visualize the progress of the damage by comparing images and videos of the disaster-stricken area with past data.

[0082] The uploading unit uses the emotion estimation function to analyze the emotions of disaster victims and identify areas where psychological support is needed. In the uploading unit, for example, the generation AI analyzes the facial expressions and voice of disaster victims and evaluates their emotions. For example, it identifies areas where stress is high and provides psychological support. In addition, the uploading unit uses the emotion estimation function to analyze the emotions of disaster victims and identify areas where psychological support is needed. For example, it analyzes the emotions of disaster victims and identifies areas where psychological support is needed. This makes it possible to identify areas where psychological support is needed based on an analysis of the emotions of disaster victims.

[0083] The uploading unit can share images and videos of the disaster-stricken area with other disaster response organizations in real time. In the uploading unit, for example, the generation AI collects images and videos of the disaster-stricken area in real time and shares them with other disaster response organizations. For example, information can be shared with fire departments and police, enabling a rapid response. In addition, the uploading unit can share images and videos of the disaster-stricken area with other disaster response organizations in real time. For example, images and videos of the disaster-stricken area can be shared with other disaster response organizations. This enables efficient disaster response by sharing information with other disaster response organizations in real time.

[0084] The uploading unit converts images and videos of the disaster-stricken area into a 3D model, enabling the damage situation to be visualized in three dimensions. In the uploading unit, for example, the generation AI converts images and videos of the disaster-stricken area into a 3D model, thereby visualizing the damage situation in three dimensions. For example, the damage situation of buildings is displayed in a 3D model. In addition, the uploading unit converts images and videos of the disaster-stricken area into a 3D model, thereby visualizing the damage situation in three dimensions. For example, the damage situation of roads is displayed in a 3D model. This makes it possible to visualize the damage situation in three dimensions by converting images and videos of the disaster-stricken area into a 3D model.

[0085] The matching unit can analyze in detail the needs of those requesting volunteers and suggest the most suitable volunteers. For example, the generation AI in the matching unit analyzes in detail the needs of those requesting volunteers and suggests the most suitable volunteers. For example, if medical support is needed, it will suggest volunteers with medical skills. The matching unit can also analyze in detail the needs of those requesting volunteers and suggest the most suitable volunteers. For example, if building repairs are needed, it will suggest volunteers with construction skills. This makes it possible to analyze in detail the needs of those requesting volunteers and suggest the most suitable volunteers.

[0086] The matching unit can evaluate the skills and experience of volunteers and match appropriate volunteers to requesters. For example, the generation AI in the matching unit evaluates the skills and experience of volunteers and matches appropriate volunteers to requesters. For example, it matches volunteers with medical skills to requesters who need medical support. The generation AI in the matching unit also evaluates the skills and experience of volunteers and matches appropriate volunteers to requesters. For example, it matches volunteers with construction skills to requesters who need building repairs. This makes it possible to evaluate the skills and experience of volunteers and match appropriate volunteers to requesters.

[0087] The matching unit uses an emotion estimation function to evaluate the motivation of volunteers and perform matching at the optimal timing. For example, the generation AI in the matching unit analyzes the facial expressions and voices of volunteers to evaluate their motivation. For example, it prioritizes matching with highly motivated volunteers. The matching unit also uses an emotion estimation function to evaluate the motivation of volunteers and perform matching at the optimal timing. For example, it matches volunteers when their motivation is high. This makes it possible to evaluate the motivation of volunteers and perform matching at the optimal timing.

[0088] The matching unit can match volunteer requesters with volunteers in cooperation with other volunteer platforms. In the matching unit, for example, the generation AI works with other volunteer platforms to match requesters with volunteers. For example, it collects information from multiple platforms and makes the optimal match. In addition, the matching unit can work with other volunteer platforms to match requesters with volunteers. For example, it works with local volunteer organizations to match requesters with volunteers. This makes it possible to perform matching in cooperation with other volunteer platforms.

[0089] The matching unit can automatically update the skills of volunteers and perform matching based on the latest information. For example, the generation AI in the matching unit automatically updates the skills of volunteers and performs matching based on the latest information. For example, newly acquired skills are registered in a database and matching is performed according to the needs of the requester. The matching unit can also automatically update the skills of volunteers using the generation AI and perform matching based on the latest information. For example, regular skill checks are performed and matching is performed based on the latest information. This makes it possible to automatically update the skills of volunteers and perform matching based on the latest information.

[0090] The matching unit can use the emotion estimation function to provide support to reduce the psychological burden on volunteers. For example, the generation AI in the matching unit analyzes the facial expressions and voice of volunteers to evaluate their psychological burden. For example, it can send a relaxing message to a volunteer who is highly stressed. The matching unit also uses the emotion estimation function to provide support to reduce the psychological burden on volunteers. For example, it can provide counseling to volunteers who need psychological support. This makes it possible to provide support to reduce the psychological burden on volunteers.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The navigation unit can propose the optimal evacuation route by taking into account the user's movement speed and health condition. For example, the generation AI measures the user's movement speed in real time and dynamically adjusts the evacuation route. For example, for elderly people who walk slowly, it will propose a safe and flat route rather than the shortest distance. The navigation unit also adjusts the evacuation route by taking into account the user's health condition. For example, for a user in poor health, it will propose an evacuation route close to medical facilities. This makes it possible to propose the optimal evacuation route based on the user's movement speed and health condition.

[0093] The navigation unit can select evacuation sites taking into account not only the type of disaster, but also the time of day and weather information. For example, the generation AI obtains real-time weather information and adjusts evacuation routes. For example, if there is heavy rain, it will suggest a route with a low risk of flooding. The navigation unit also adjusts evacuation routes taking into account the time of day. For example, if it is nighttime, it will suggest a route with good lighting. This makes it possible to select evacuation sites taking into account the time of day and weather information.

[0094] The navigation unit uses an emotion estimation function to assess the user's stress level and suggest evacuation routes to reduce stress. For example, the generation AI analyzes the user's facial expressions and voice to assess the stress level in real time. For example, if stress is high, it will suggest a quiet route. The navigation unit also uses the generation AI to assess the user's stress level and suggest evacuation routes to reduce stress. For example, if stress is high, it will suggest a route with a rich natural environment. This makes it possible to suggest evacuation routes according to the user's stress level.

[0095] The navigation unit can reflect the operating status of public transportation in real time. For example, the generation AI obtains the operating status of public transportation in real time and adjusts evacuation routes. For example, it can propose evacuation routes using buses and trains that are currently in operation. The navigation unit also proposes optimal evacuation routes by having the generation AI reflect the operating status of public transportation in real time. For example, it can propose evacuation routes using public transportation that is currently in operation. This makes it possible to propose evacuation routes that reflect the operating status of public transportation in real time.

[0096] The navigation unit integrates the location information of pets and family members, and can suggest routes that allow everyone to evacuate together. For example, the generation AI obtains the location information of pets and family members in real time, and suggests routes that allow everyone to evacuate together. For example, it can guide you to a route that passes through an intermediate point where all family members can gather. The navigation unit also integrates the location information of pets and family members, and suggests routes that allow everyone to evacuate together. For example, it can suggest routes that allow you to evacuate together with your pets. This makes it possible to suggest routes that allow you to evacuate together with your pets and family members.

[0097] The navigation unit can use the emotion estimation function to provide audio guidance and encouraging messages to reduce the user's anxiety during evacuation. For example, the generation AI can analyze the user's facial expressions and voice and provide an encouraging message if the user is feeling anxious. For example, it can send a message such as, "Don't worry, we'll soon arrive at the evacuation site." The navigation unit can also use the emotion estimation function to provide audio guidance and encouraging messages to reduce the user's anxiety during evacuation. For example, it can send a message such as, "Don't worry, the evacuation site is nearby." This makes it possible to provide audio guidance and encouraging messages to reduce the user's anxiety during evacuation.

[0098] The visualization unit can display the accommodation status of evacuation shelters in detail based on the age group and special needs of the evacuees. For example, the generation AI can analyze the age group of the evacuees and display the accommodation status of the evacuation shelter in detail. For example, it can identify evacuation shelters with a high percentage of elderly people and prepare for the need for medical assistance. The visualization unit can also analyze the special needs of evacuees and display the accommodation status of the evacuation shelter in detail. For example, it can identify evacuation shelters with a high percentage of disabled people and make them barrier-free. This makes it possible to display the accommodation status in detail based on the age group and special needs of the evacuees.

[0099] The visualization unit can predict the opening status of evacuation shelters by comparing it with past data and predicting future capacity. For example, the generation AI analyzes past evacuation shelter opening data and predicts future capacity. For example, it predicts the capacity of evacuation shelters based on data from past disasters. The visualization unit also predicts the opening status of evacuation shelters by comparing it with past data. For example, it predicts the opening status of evacuation shelters based on past data. This makes it possible to predict future capacity based on predictions made in comparison with past data.

[0100] The visualization unit uses the emotion estimation function to evaluate the atmosphere of the evacuation shelter and the satisfaction of the evacuees, and can provide this as reference information for selecting a shelter. For example, the generation AI analyzes the facial expressions and voices of the evacuees to evaluate the atmosphere of the evacuation shelter. For example, it identifies evacuation shelters where evacuees feel relaxed. The visualization unit also uses the generation AI to evaluate the satisfaction of the evacuees and provide this as reference information for selecting a shelter. For example, it identifies evacuation shelters where evacuees are highly satisfied. This makes it possible to provide reference information for selecting a shelter based on the atmosphere of the evacuation shelter and the satisfaction of the evacuees.

[0101] The visualization unit can share the opening and occupancy status of evacuation centers with other disaster response organizations in real time. For example, the generation AI collects the opening and occupancy status of evacuation centers in real time and shares it with other disaster response organizations. For example, sharing information with the fire department and police enables a rapid response. The visualization unit also enables efficient disaster response by having the generation AI share the opening and occupancy status of evacuation centers in real time. For example, sharing the opening and occupancy status of evacuation centers with other disaster response organizations. This enables efficient disaster response by sharing information with other disaster response organizations in real time.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The navigation unit navigates to the nearest evacuation site depending on the type of disaster. For example, the generation AI receives the type of disaster (earthquake, flood, fire, etc.) and current location information as input, and suggests the optimal evacuation site. For example, the generation AI provides guidance such as, "An earthquake has occurred. The nearest evacuation site is XX Park. Please head this way." Step 2: The visualization unit visualizes the opening status and occupancy status of evacuation shelters. For example, the generation AI collects and analyzes data on evacuation shelters and displays the currently open evacuation shelters and the number of people they can accommodate. For example, the generation AI provides information such as, "X evacuation shelter is currently open and can accommodate 50 people." Step 3: The registration unit registers information about the person in need of rescue and shares it on a map. For example, the generation AI analyzes the location and situation of the person in need of rescue and suggests the optimal rescue route to the rescue team. For example, the generation AI provides guidance such as, "The person in need of rescue is at location XX. Please head this way." Step 4: The uploading unit uploads images and videos of the affected area and visualizes dangerous and impassable areas. For example, the generating AI analyzes the uploaded images and videos and identifies dangerous and impassable areas. For example, the generating AI provides information such as, "This area is impassable. Please select an alternative route." Step 5: The matching unit matches volunteer requesters with volunteers. For example, the generation AI analyzes the needs of the requester and the skills and location information of the volunteers to make the optimal match. For example, the generation AI provides guidance such as, "Mr. / Ms. XX needs a volunteer. Your skills would be useful."

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A navigation section that navigates to the nearest evacuation site depending on the type of disaster, A visualization section that visualizes the opening status and accommodation status of evacuation centers, A registration section that registers information about people in need of rescue and shares it on a map. An uploading section uploads images and videos of the affected areas and visualizes dangerous and impassable areas. A matching unit that matches volunteer requesters with volunteers. A system characterized by:

2. The navigation unit Reflecting public transport operation status in real time 2. The system of claim 1.

3. The visualization unit Detail the capacity of the shelter based on age group or special needs of the evacuees.

2. The system of claim 1.

4. The registration unit Automatically update the information of the person in need of rescue and notify rescue teams in real time.

2. The system of claim 1.

5. The upload unit The uploaded images and videos are automatically analyzed to assess the extent of the damage.

2. The system of claim 1.

6. The matching unit Using emotion estimation function, the motivation of the volunteers is evaluated and matching is performed at the optimal timing.

2. The system of claim 1.

7. The navigation unit Emotion estimation function is used to assess the user's stress level and suggest evacuation routes to reduce stress.

2. The system of claim 1.

Images