system

Abstract

The system according to the embodiment aims to provide quick and effective evacuation guidance in the event of a disaster. [Solution] A system according to an embodiment includes a receiving unit, a sharing unit, a guidance unit, and an information providing unit. The receiving unit receives an earthquake early warning. The sharing unit shares location information based on the information received by the receiving unit. The guidance unit provides guidance to an evacuation site based on the location information shared by the sharing unit. The information providing unit provides disaster prevention information.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to provide quick and effective emergency response and evacuation guidance in the event of a disaster.

[0005] The system according to the embodiment aims to provide quick and effective evacuation guidance in the event of a disaster. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a sharing unit, a guiding unit, and an information providing unit. The receiving unit receives an earthquake early warning. The sharing unit shares location information based on the information received by the receiving unit. The guiding unit provides guidance to an evacuation site based on the location information shared by the sharing unit. The information providing unit provides disaster prevention information. [Effects of the Invention]

[0007] The system according to the embodiment can provide quick and effective evacuation guidance in the event of a disaster. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The disaster response support system according to an embodiment of the present invention is an application system for supporting emergency response during disasters. When an earthquake early warning or similar event occurs, this disaster response support system automatically launches an application, and a voice-input enabled messaging application starts, allowing the user to write a message by voice. Furthermore, based on the location information at the time the application is launched, the system automatically shares the user's current location with pre-registered family members and related parties. In addition, it works in conjunction with a map application to automatically guide the user to an evacuation site. By coordinating with means of transportation such as automobiles, it provides the optimal route from the user's current location to the evacuation site. It works in conjunction with a disaster alert application to provide the latest information such as "evacuation information," "earthquake information," "tsunami forecasts," and "weather warnings." It visualizes the damage from secondary disasters based on location information and instructs the user on appropriate actions. For users with low smartphone literacy, the application has a function to automatically provide instructions. It collects the latest information on the provision of supplies by local governments from social media and provides instructions to the user. Furthermore, to address issues such as radio wave interference and battery depletion, it has a function that uses AI to find Wi-Fi hotspots and a function that uses a battery storage function to suppress battery consumption. This ensures that the application operates normally even when radio wave interference occurs. This allows the disaster response support system to provide rapid and effective support for emergency response during disasters.

[0029] The disaster response support system according to this embodiment comprises a receiving unit, a sharing unit, a guidance unit, and an information provision unit. The receiving unit receives earthquake early warnings. Earthquake early warnings can be received, for example, from the Japan Meteorological Agency or other public organizations. The receiving unit receives warnings, for example, via an internet connection. The receiving unit can also receive warnings via radio waves or television broadcasts. The sharing unit shares location information based on the information received by the receiving unit. Location information is obtained, for example, using GPS data or WiFi location information. The sharing unit automatically shares the acquired location information with family members and related parties who have been registered in advance. The sharing unit shares location information, for example, via an internet connection. The sharing unit can also share location information via SMS or email. The guidance unit provides guidance to evacuation sites based on the location information shared by the sharing unit. The guidance unit provides the optimal route to evacuation sites, for example, in cooperation with a map application. The guidance unit can update the route from the user's current location to the evacuation site in real time. The information provision unit provides disaster prevention information. Disaster prevention information includes, for example, "evacuation information," "earthquake information," "tsunami forecasts," and "weather warnings." The information provision unit provides the latest disaster prevention information in cooperation with the disaster prevention alert app. The information provision unit can instruct users on appropriate actions. As a result, the disaster response support system according to this embodiment can quickly and effectively support emergency response during a disaster.

[0030] The disaster response support system is equipped with a voice input unit that accepts voice input. The voice input unit allows users to input information by voice. For example, the voice input unit can input voice using the microphone of a smartphone. The voice input unit converts voice to text using speech recognition technology. For example, if a user says, "Tell me where the evacuation shelters are," the voice input unit converts the voice to text and displays the appropriate evacuation shelters. In addition, the voice input unit can improve the accuracy of speech recognition by removing ambient noise using noise cancellation technology. For example, the voice input unit can accurately recognize voice even in noisy environments. Furthermore, the voice input unit can operate applications based on the user's voice commands. For example, if a user says, "Show me the map," the voice input unit will launch a map application and display the current location. This allows the voice input unit to enable users to input information quickly.

[0031] The disaster response support system includes a Wi-Fi search unit that finds Wi-Fi hotspots. The Wi-Fi search unit searches for Wi-Fi hotspots available to the user. For example, the Wi-Fi search unit can detect nearby Wi-Fi hotspots using the Wi-Fi function of a smartphone. The Wi-Fi search unit lists available Wi-Fi hotspots, such as public Wi-Fi and home Wi-Fi. For example, the Wi-Fi search unit prioritizes displaying public Wi-Fi hotspots such as cafes and libraries. The Wi-Fi search unit can also evaluate the signal strength and connection speed of Wi-Fi hotspots and select the optimal Wi-Fi hotspot. For example, the Wi-Fi search unit prioritizes displaying Wi-Fi hotspots with strong signal strength and fast connection speeds. Furthermore, the Wi-Fi search unit can provide the optimal Wi-Fi hotspot based on the user's current location. For example, if the user is on the move, the Wi-Fi search unit displays Wi-Fi hotspots along the user's route. This ensures that communication is maintained even in the event of radio interference.

[0032] The disaster response support system includes a battery management unit that reduces battery consumption. The battery management unit optimizes battery consumption of the smartphone. For example, the battery management unit can reduce battery consumption using a power consumption optimization algorithm. The battery management unit optimizes app operation to reduce unnecessary power consumption. For example, the battery management unit automatically stops apps running in the background to reduce battery consumption. The battery management unit can also control display brightness and use of communication functions to minimize battery consumption. For example, the battery management unit automatically adjusts display brightness and controls communication functions to be used only when necessary. Furthermore, the battery management unit can optimize battery consumption based on user usage. For example, if a user frequently uses an app, the battery management unit sets settings to minimize battery consumption. This allows the battery management unit to operate for a long period of time.

[0033] The guidance unit can work in conjunction with a map app to automatically guide the user to an evacuation site. The guidance unit works in conjunction with the map app to guide the user to the evacuation site. The map app can use, for example, map apps such as Yahoo! (registered trademark) Maps or Google (registered trademark) Maps. The guidance unit provides the optimal evacuation route based on the user's current location. For example, if the user is traveling by car, the guidance unit can suggest a route exclusively for vehicles. Also, if the user is traveling on foot, the guidance unit can suggest a route exclusively for pedestrians. Furthermore, the guidance unit updates the route in real time to provide the optimal evacuation route. For example, the guidance unit updates the route taking into account traffic conditions and road closure information. In this way, the guidance unit enables the user to reach the evacuation site quickly and safely.

[0034] The information providing unit can cooperate with a disaster prevention alert app to provide the latest information such as evacuation information, earthquake information, tsunami forecasts, and weather warnings. The information providing unit cooperates with a disaster prevention alert app to provide the latest disaster prevention information. The disaster prevention alert app can use apps such as Yahoo! Disaster Prevention Alert and NHK News & Disaster Prevention. The information providing unit updates disaster prevention information in real time and provides it to the user. For example, if an earthquake occurs, the information providing unit immediately notifies the user of earthquake information. The information providing unit can also provide information such as tsunami forecasts and weather warnings. Furthermore, the information providing unit can instruct the user on appropriate actions. For example, if an evacuation order is issued, the information providing unit sends a notification urging the user to evacuate. In this way, the information providing unit enables the user to quickly obtain the latest disaster prevention information and take appropriate actions.

[0035] The information providing unit visualizes damage caused by secondary disasters based on location information and can instruct the user on appropriate actions. The information providing unit visualizes damage caused by secondary disasters based on location information. Secondary disasters include, for example, fires and landslides. The information providing unit evaluates the risk of secondary disasters based on the user's current location and instructs the user on appropriate actions. For example, if the user is in a fire area, the information providing unit provides evacuation routes and encourages rapid evacuation. The information providing unit can also send evacuation instructions to users in areas at risk of landslides. Furthermore, the information providing unit updates the damage status of secondary disasters in real time and provides it to the user. For example, the information providing unit notifies the user of the progress of a fire or the occurrence of a landslide. In this way, the information providing unit enables the user to take appropriate actions in response to secondary disasters.

[0036] The information providing unit can collect the latest information about the provision of supplies supported by local governments from SNS and provide instructions to the user. The information providing unit collects the latest information about the provision of supplies supported by local governments from SNS. The provided supplies include, for example, food, water, and medicine. The information providing unit provides appropriate instructions to the user based on the information collected through SNS. For example, the information providing unit notifies the user of the distribution locations and distribution times of supplies provided by local governments. The information providing unit can also inform the user of the stock status and distribution method of the supplies. Furthermore, the information providing unit guides the user to the optimal supply distribution location based on the user's current location. For example, the information providing unit guides the user to quickly access a nearby supply distribution location. In this way, the information providing unit enables the user to quickly obtain the necessary supplies.

[0037] When receiving an Earthquake Early Warning, the receiving unit can determine the priority of notifications by referring to past disaster data. When receiving an Earthquake Early Warning, the receiving unit can determine the priority of notifications by referring to past disaster data. Past disaster data includes, for example, past earthquake data and damage situation data. The receiving unit prioritizes notifications to areas that suffered the most damage based on the past disaster data. For example, the receiving unit analyzes past disaster data and quickly notifies areas where damage is predicted. The receiving unit can also delay notifications to areas that suffered less damage by referring to past disaster data. In this way, the receiving unit can appropriately determine the priority of notifications by referring to past disaster data.

[0038] The receiving unit can adjust the timing of the notification based on the user's current activity status when receiving an Earthquake Early Warning. The receiving unit adjusts the timing of the notification based on the user's current activity status when receiving an Earthquake Early Warning. The current activity status can be acquired using, for example, sensor information from a smartphone or the user's behavior history. For example, if the user is driving, the receiving unit can delay the notification and notify the user after the user has stopped in a safe place. Furthermore, if the user is in a meeting, the receiving unit can notify the user after the meeting ends. Furthermore, if the user is sleeping, the receiving unit can notify the user after the user wakes up. In this way, the receiving unit can adjust the timing of the notification according to the user's activity status and provide more appropriate notifications.

[0039] When receiving an Earthquake Early Warning, the receiving unit can customize the content of the notification taking into account the user's geographical location information. When receiving an Earthquake Early Warning, the receiving unit customizes the content of the notification taking into account the user's geographical location information. The geographical location information can be acquired using, for example, GPS data or map data. For example, if the user is near the coast, the receiving unit can provide a notification emphasizing the risk of a tsunami. Furthermore, if the user is in a high-rise building, the receiving unit can provide a detailed notification of an evacuation route. Furthermore, if the user is in a subway, the receiving unit can provide a notification of the nearest evacuation location. This allows the receiving unit to customize the content of the notification based on the user's geographical location information and provide more appropriate notifications.

[0040] The receiving unit can analyze the user's social media activity and notify them of relevant information upon receiving an earthquake early warning. Social media activity can be analyzed, for example, by analyzing posts and followers. For instance, if the user frequently posts disaster information on social media, the receiving unit will provide a detailed notification. Furthermore, if the user is searching for evacuation shelters on social media, the receiving unit can notify them of the nearest evacuation shelter. Additionally, if the user is contacting family members on social media, the receiving unit can notify them of the family's safety status. This allows the receiving unit to customize notification content based on the user's social media activity, enabling more appropriate notifications.

[0041] The sharing function can select the optimal recipient when sharing location information by referring to past sharing history. The sharing history includes, for example, past recipients and shared content. For instance, the sharing function prioritizes sharing location information with those with whom it has frequently shared in the past. Furthermore, the sharing function can suggest optimal recipients based on past sharing history. In addition, the sharing function can analyze past sharing history and automatically select recipients. Thus, the sharing function can select the optimal recipient by referring to past sharing history.

[0042] The sharing function can filter shared content based on the user's current areas of interest when sharing location information. Areas of interest can be identified, for example, using survey results or past search history. For instance, if the user is interested in disaster information, the sharing function will prioritize sharing disaster information. Similarly, if the user is interested in evacuation shelters, the sharing function will prioritize sharing information about evacuation shelters. Furthermore, if the user is interested in information about the safety of their family, the sharing function will prioritize sharing information about the safety of their family. This allows the sharing function to filter shared content based on the user's areas of interest and share more relevant information.

[0043] The shared information system can prioritize sharing highly relevant information by considering the user's geographical location when sharing location information. Geographic location information can be obtained, for example, using GPS data or map data. For example, if the user is near the coast, the shared information system can share information emphasizing tsunami risk. Also, if the user is inside a high-rise building, the shared information system can share detailed evacuation routes. Furthermore, if the user is inside a subway, the shared information system can share the nearest evacuation location. In this way, the shared information system can share highly relevant information based on the user's geographical location and provide more appropriate information.

[0044] The sharing function can analyze a user's social media activity when location information is shared and share relevant information. Social media activity can be analyzed, for example, by analyzing posts and followers. For instance, if a user frequently posts disaster information on social media, the sharing function can share detailed information. Also, if a user searches for evacuation shelters on social media, the sharing function can share information about the nearest evacuation shelters. Furthermore, if a user contacts family members on social media, the sharing function can share information about the safety of those family members. This allows the sharing function to share relevant information based on the user's social media activity and provide more appropriate information.

[0045] The guidance unit can select the optimal route during evacuation guidance by referring to past evacuation history. The guidance unit selects the optimal route by referring to past evacuation history. Evacuation history includes, for example, past evacuation routes and evacuation times. For example, the guidance unit proposes the optimal route based on previously used evacuation routes. Furthermore, the guidance unit can propose routes that avoid congestion based on past evacuation history. In addition, the guidance unit can analyze past evacuation history and propose the most efficient route. Thus, the guidance unit can select the optimal evacuation route by referring to past evacuation history.

[0046] The guidance unit can customize the guidance method based on the user's current means of transportation when providing evacuation guidance. The guidance unit customizes the guidance method based on the user's current means of transportation when providing evacuation guidance. The means of transportation includes, for example, walking, bicycle, car, etc. For example, when the user is traveling on foot, the guidance unit can suggest a route exclusively for pedestrians. Furthermore, when the user is traveling by car, the guidance unit can suggest a route exclusively for vehicles. Furthermore, when the user is traveling by bicycle, the guidance unit can suggest a route exclusively for bicycles. This allows the guidance unit to customize the guidance method according to the user's means of transportation and provide more appropriate evacuation guidance.

[0047] The guiding unit can provide an optimal evacuation route by taking into consideration the user's geographical location information when providing evacuation guidance. The guiding unit can provide an optimal evacuation route by taking into consideration the user's geographical location information when providing evacuation guidance. The geographical location information can be acquired using, for example, GPS data or map data. For example, if the user is near the coast, the guiding unit can provide a route that avoids the risk of a tsunami. Furthermore, if the user is in a high-rise building, the guiding unit can provide the nearest evacuation route. Furthermore, if the user is in a subway, the guiding unit can provide the nearest evacuation location. This allows the guiding unit to provide an optimal evacuation route based on the user's geographical location information and provide more appropriate evacuation guidance.

[0048] The guidance unit can analyze the user's social media activity and provide relevant information when guiding the user to an evacuation route. The guidance unit can analyze the user's social media activity and provide relevant information when guiding the user to an evacuation route. Social media activity can be analyzed, for example, by analyzing the content of posts or followers. For example, if the user frequently posts disaster information on the social media, the guidance unit can provide detailed evacuation information. Furthermore, if the user searches for evacuation locations on the social media, the guidance unit can provide information on the nearest evacuation location. Furthermore, if the user is in contact with family members on the social media, the guidance unit can provide information on the safety of the family members. This allows the guidance unit to provide relevant information based on the user's social media activity and provide more appropriate evacuation guidance.

[0049] When providing disaster prevention information, the information providing unit can determine the priority of information by referring to past disaster data. When providing disaster prevention information, the information providing unit can determine the priority of information by referring to past disaster data. Past disaster data includes, for example, past earthquake data and damage situation data. For example, the information providing unit can provide information preferentially to areas that suffered the most damage based on the past disaster data. In addition, the information providing unit can analyze the past disaster data and quickly provide information to areas where damage is predicted. Furthermore, the information providing unit can delay providing information to areas that suffered less damage by referring to the past disaster data. In this way, the information providing unit can appropriately determine the priority of information by referring to the past disaster data.

[0050] The information providing unit can filter information based on the user's current areas of interest when providing disaster prevention information. The information providing unit filters information based on the user's current areas of interest when providing disaster prevention information. The areas of interest can be identified using, for example, survey results or past search history. For example, if the user is interested in disaster information, the information providing unit can provide disaster information preferentially. Furthermore, if the user is interested in evacuation locations, the information providing unit can provide evacuation location information preferentially. Furthermore, if the user is interested in information on the safety of family members, the information providing unit can provide family safety information preferentially. This allows the information providing unit to filter information based on the user's areas of interest and provide more appropriate information.

[0051] When providing disaster prevention information, the information providing unit can prioritize providing highly relevant information taking into account the user's geographical location information. When providing disaster prevention information, the information providing unit can prioritize providing highly relevant information taking into account the user's geographical location information. The geographical location information can be acquired using, for example, GPS data or map data. For example, when the user is near the coast, the information providing unit can provide information emphasizing the risk of a tsunami. Furthermore, when the user is in a high-rise building, the information providing unit can provide detailed evacuation routes. Furthermore, when the user is in a subway, the information providing unit can provide the nearest evacuation location. This allows the information providing unit to provide highly relevant information based on the user's geographical location information, thereby providing more appropriate information.

[0052] The Information Provision Department can analyze users' social media activity when providing disaster prevention information and provide relevant information. Social media activity can be analyzed, for example, by analyzing post content or followers. For example, if a user frequently posts disaster information on social media, the Information Provision Department can provide detailed information. Also, if a user searches for evacuation shelters on social media, the Information Provision Department can provide information on the nearest evacuation shelters. Furthermore, if a user is in contact with family members on social media, the Information Provision Department can provide information on the safety of those family members. This allows the Information Provision Department to provide more appropriate information based on users' social media activity.

[0053] The voice input unit can select the optimal recognition algorithm by referring to past voice input history during voice input. The voice input history includes, for example, past voice commands and recognition results. For instance, the voice input unit prioritizes recognizing voice commands that have been frequently used in the past. Furthermore, the voice input unit can select the optimal recognition algorithm based on past voice input history. In addition, the voice input unit can analyze past voice input history to improve recognition accuracy. As a result, the voice input unit can select the optimal recognition algorithm by referring to past voice input history, enabling more appropriate voice input.

[0054] The audio input unit can perform noise cancellation taking into account the user's current environmental sound when inputting audio. The audio input unit can perform noise cancellation taking into account the user's current environmental sound when inputting audio. The environmental sound can be acquired using, for example, the type of microphone or noise filtering technology. For example, the audio input unit can enhance noise cancellation when the user is in a noisy place. Furthermore, the audio input unit can perform normal noise cancellation when the user is in a quiet place. Furthermore, the audio input unit can adjust noise cancellation according to the environmental sound when the user is moving. This allows the audio input unit to perform noise cancellation according to the user's environmental sound and perform more appropriate audio input.

[0055] The voice input unit can improve recognition accuracy by taking into account the user's geographical location information when inputting voice. The voice input unit improves recognition accuracy by taking into account the user's geographical location information when inputting voice. The geographical location information can be acquired using, for example, GPS data or map data. For example, when the user is near the coast, the voice input unit can prioritize recognizing voice input related to tsunami risk. Furthermore, when the user is in a high-rise building, the voice input unit can prioritize recognizing voice input related to evacuation routes. Furthermore, when the user is in a subway, the voice input unit can prioritize recognizing voice input related to the nearest evacuation location. This allows the voice input unit to improve recognition accuracy based on the user's geographical location information and perform more appropriate voice input.

[0056] The voice input unit can analyze the user's social media activity and recognize related information when the user inputs voice. The voice input unit can analyze the user's social media activity and recognize related information when the user inputs voice. Social media activity can be analyzed, for example, by analyzing the content of posts or followers. For example, if the user frequently posts disaster information on the social media, the voice input unit can recognize detailed voice input. Furthermore, if the user is searching for evacuation shelters on the social media, the voice input unit can recognize voice input regarding the nearest evacuation shelter. Furthermore, if the user is in contact with family members on the social media, the voice input unit can recognize voice input regarding information about the safety of family members. This allows the voice input unit to recognize related information based on the user's social media activity and provide more appropriate voice input.

[0057] When searching for a WiFi spot, the WiFi search unit can select the optimal spot by referring to past connection history. When searching for a WiFi spot, the WiFi search unit can select the optimal spot by referring to past connection history. The connection history includes, for example, past connection destinations and connection times. For example, the WiFi search unit prioritizes searching for WiFi spots that have been frequently connected to in the past. The WiFi search unit can also select the optimal WiFi spot based on the past connection history. Furthermore, the WiFi search unit can analyze the past connection history and select the most stable WiFi spot. As a result, the WiFi search unit can select the optimal WiFi spot by referring to the past connection history, and provide a more appropriate WiFi spot.

[0058] When searching for a WiFi spot, the WiFi search unit can customize the search range based on the user's current location information. When searching for a WiFi spot, the WiFi search unit can customize the search range based on the user's current location information. The location information can be obtained using, for example, GPS data or map data. For example, when the user is in an urban area, the WiFi search unit prioritizes searching for nearby WiFi spots. Furthermore, when the user is in a suburban area, the WiFi search unit can search for WiFi spots over a wide area. Furthermore, when the user is moving, the WiFi search unit can search for WiFi spots along the user's route. This allows the WiFi search unit to customize the search range based on the user's location information and provide more appropriate WiFi spots.

[0059] When searching for WiFi spots, the WiFi search unit can provide the optimal spot by taking into account the user's geographical location information. When searching for WiFi spots, the WiFi search unit can provide the optimal spot by taking into account the user's geographical location information. Geographical location information can be acquired using, for example, GPS data or map data. For example, if the user is near the coast, the WiFi search unit can provide the nearest WiFi spot. Also, if the user is in a high-rise building, the WiFi search unit can provide WiFi spots within the building. Furthermore, if the user is in a subway, the WiFi search unit can provide WiFi spots within the subway. In this way, the WiFi search unit can provide the optimal WiFi spot based on the user's geographical location information, thereby providing a more appropriate WiFi spot.

[0060] The WiFi search unit can analyze a user's social media activity and provide related information when searching for a WiFi spot. The WiFi search unit can analyze a user's social media activity and provide related information when searching for a WiFi spot. Social media activity can be analyzed, for example, by analyzing posts or followers. For example, if a user frequently posts information about WiFi spots on a social media site, the WiFi search unit can provide detailed WiFi spot information. Also, if a user is searching for a specific WiFi spot on a social media site, the WiFi search unit can provide information about the WiFi spot. Furthermore, if a user is in contact with friends on a social media site, the WiFi search unit can provide information about WiFi spots used by the friends. This allows the WiFi search unit to provide related information based on the user's social media activity and provide more appropriate WiFi spots.

[0061] The battery management unit can select an optimal management method by referring to past battery consumption history when managing batteries. The battery management unit can select an optimal management method by referring to past battery consumption history when managing batteries. The battery consumption history includes, for example, past consumption amounts and used apps. For example, the battery management unit proposes an optimal battery management method based on the past battery consumption history. The battery management unit can also analyze the past battery consumption history and select a method that minimizes battery consumption. Furthermore, the battery management unit can select a method that optimizes battery consumption by referring to the past battery consumption history. In this way, the battery management unit can select an optimal battery management method by referring to the past battery consumption history, thereby enabling more appropriate battery management.

[0062] The battery management unit can optimize consumption based on the user's current usage status during battery management. The battery management unit optimizes consumption based on the user's current usage status during battery management. The usage status can be acquired using, for example, app usage time or device usage status. For example, the battery management unit can set the battery to minimize battery consumption when the user frequently uses apps. Furthermore, the battery management unit can set normal battery consumption when the user does not use apps very often. Furthermore, the battery management unit can set the battery to optimize battery consumption when the user is on the move. This allows the battery management unit to optimize consumption based on the user's usage status and perform more appropriate battery management.

[0063] The battery management unit can provide an optimal battery management method by taking into account the user's geographical location information when managing the battery. The battery management unit can provide an optimal battery management method by taking into account the user's geographical location information when managing the battery. Geographical location information can be acquired using, for example, GPS data or map data. For example, the battery management unit can set the battery to minimize battery consumption when the user is near the coast. Furthermore, the battery management unit can set the battery to consume normal amounts of battery when the user is in a high-rise building. Furthermore, the battery management unit can set the battery to optimize battery consumption when the user is in a subway. This allows the battery management unit to provide an optimal battery management method based on the user's geographical location information and perform more appropriate battery management.

[0064] The battery management unit can analyze a user's social media activity and provide related information when managing the battery. The battery management unit can analyze a user's social media activity and provide related information when managing the battery. Social media activity can be analyzed, for example, by analyzing the content of posts or followers. For example, if a user frequently posts information about battery consumption on a social media site, the battery management unit can provide detailed battery management information. Also, if a user searches for the battery consumption of a specific app on a social media site, the battery management unit can provide battery management information for that app. Furthermore, if a user contacts friends on a social media site, the battery management unit can provide the battery management method used by the friends. This allows the battery management unit to provide related information based on the user's social media activity and perform more appropriate battery management.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The disaster response support system can also be equipped with a health management unit that monitors the user's health condition. The health management unit acquires vital data such as the user's heart rate and body temperature, and issues an emergency notification if an abnormality is detected. For example, if the user's heart rate suddenly increases, a notification urging the user to take deep breaths is sent. Also, if the body temperature is high, a notification can be sent warning of the risk of heatstroke and encouraging the user to drink more fluids. Furthermore, the health management unit can share the user's vital data with family and medical institutions to support a rapid response. This allows the user's health condition to be monitored in real time during a disaster, and appropriate measures can be taken.

[0067] The disaster response support system can also be equipped with a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit analyzes evacuation behavior patterns based on the user's past behavior data and proposes the optimal evacuation route. For example, it proposes a route that avoids congestion, taking into account evacuation routes and evacuation times used in the past. The behavior analysis unit can also support the selection of evacuation locations based on the user's behavior history. For example, it evaluates the safety and convenience of locations where people have evacuated in the past and proposes the optimal evacuation location. Furthermore, the behavior analysis unit can share the user's behavior history with family and related parties to support rapid evacuation. This can optimize the user's evacuation behavior in the event of a disaster and support rapid and safe evacuation.

[0068] The disaster response support system can also include a mobility management unit that manages the user's means of transportation. The mobility management unit grasps the user's current means of transportation and provides the optimal evacuation route. For example, if the user is traveling by car, it proposes a route exclusively for vehicles, and if the user is traveling on foot, it proposes a route exclusively for pedestrians. The mobility management unit can also support the selection of an evacuation site depending on the user's means of transportation. For example, if the user is traveling by car, it proposes an evacuation site with a parking lot, and if the user is traveling on foot, it proposes a nearby evacuation site. Furthermore, the mobility management unit can share the user's means of transportation with family and related parties to support rapid evacuation. This can optimize the user's movement in the event of a disaster and support rapid and safe evacuation.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The receiving unit receives an earthquake early warning. The earthquake early warning may be provided, for example, by the Japan Meteorological Agency or other public organizations. The receiving unit receives the warning via an internet connection, for example. The receiving unit may also receive the warning via radio waves or television broadcasts. Step 2: The sharing unit shares the location information based on the information received by the receiving unit. The location information is obtained, for example, using GPS data or WiFi location information. The sharing unit automatically shares the obtained location information with pre-registered family members and related parties. The sharing unit shares the location information, for example, via an Internet connection. The sharing unit can also share the location information via SMS or email. Step 3: The guidance unit guides the user to an evacuation site based on the location information shared by the sharing unit. The guidance unit provides the optimal route to the evacuation site, for example, in cooperation with a map app. The guidance unit can update the route from the user's current location to the evacuation site in real time. Step 4: The information provider provides disaster prevention information. Disaster prevention information includes, for example, "evacuation information," "earthquake information," "tsunami forecasts," and "weather warnings." The information provider provides the latest disaster prevention information in cooperation with the disaster prevention alert app. The information provider can instruct the user on appropriate actions.

[0071] (Example 2) The disaster response support system according to an embodiment of the present invention is an application system for supporting emergency response during disasters. When an earthquake early warning or similar event occurs, this disaster response support system automatically launches an application, and a voice-input enabled messaging application starts, allowing the user to write a message by voice. Furthermore, based on the location information at the time the application is launched, the system automatically shares the user's current location with pre-registered family members and related parties. In addition, it works in conjunction with a map application to automatically guide the user to an evacuation site. By coordinating with means of transportation such as automobiles, it provides the optimal route from the user's current location to the evacuation site. It works in conjunction with a disaster alert application to provide the latest information such as "evacuation information," "earthquake information," "tsunami forecasts," and "weather warnings." It visualizes the damage from secondary disasters based on location information and instructs the user on appropriate actions. For users with low smartphone literacy, the application has a function to automatically provide instructions. It collects the latest information on the provision of supplies by local governments from social media and provides instructions to the user. Furthermore, to address issues such as radio wave interference and battery depletion, it has a function that uses AI to find Wi-Fi hotspots and a function that uses a battery storage function to suppress battery consumption. This ensures that the application operates normally even when radio wave interference occurs. This allows the disaster response support system to provide rapid and effective support for emergency response during disasters.

[0072] The disaster response support system according to this embodiment comprises a receiving unit, a sharing unit, a guidance unit, and an information provision unit. The receiving unit receives earthquake early warnings. Earthquake early warnings can be received, for example, from the Japan Meteorological Agency or other public organizations. The receiving unit receives warnings, for example, via an internet connection. The receiving unit can also receive warnings via radio waves or television broadcasts. The sharing unit shares location information based on the information received by the receiving unit. Location information is obtained, for example, using GPS data or WiFi location information. The sharing unit automatically shares the acquired location information with family members and related parties who have been registered in advance. The sharing unit shares location information, for example, via an internet connection. The sharing unit can also share location information via SMS or email. The guidance unit provides guidance to evacuation sites based on the location information shared by the sharing unit. The guidance unit provides the optimal route to evacuation sites, for example, in cooperation with a map application. The guidance unit can update the route from the user's current location to the evacuation site in real time. The information provision unit provides disaster prevention information. Disaster prevention information includes, for example, "evacuation information," "earthquake information," "tsunami forecasts," and "weather warnings." The information provision unit provides the latest disaster prevention information in cooperation with the disaster prevention alert app. The information provision unit can instruct users on appropriate actions. As a result, the disaster response support system according to this embodiment can quickly and effectively support emergency response during a disaster.

[0073] The disaster response support system is equipped with a voice input unit that accepts voice input. The voice input unit allows users to input information by voice. For example, the voice input unit can input voice using the microphone of a smartphone. The voice input unit converts voice to text using speech recognition technology. For example, if a user says, "Tell me where the evacuation shelters are," the voice input unit converts the voice to text and displays the appropriate evacuation shelters. In addition, the voice input unit can improve the accuracy of speech recognition by removing ambient noise using noise cancellation technology. For example, the voice input unit can accurately recognize voice even in noisy environments. Furthermore, the voice input unit can operate applications based on the user's voice commands. For example, if a user says, "Show me the map," the voice input unit will launch a map application and display the current location. This allows the voice input unit to enable users to input information quickly.

[0074] The disaster response support system includes a Wi-Fi search unit that finds Wi-Fi hotspots. The Wi-Fi search unit searches for Wi-Fi hotspots available to the user. For example, the Wi-Fi search unit can detect nearby Wi-Fi hotspots using the Wi-Fi function of a smartphone. The Wi-Fi search unit lists available Wi-Fi hotspots, such as public Wi-Fi and home Wi-Fi. For example, the Wi-Fi search unit prioritizes displaying public Wi-Fi hotspots such as cafes and libraries. The Wi-Fi search unit can also evaluate the signal strength and connection speed of Wi-Fi hotspots and select the optimal Wi-Fi hotspot. For example, the Wi-Fi search unit prioritizes displaying Wi-Fi hotspots with strong signal strength and fast connection speeds. Furthermore, the Wi-Fi search unit can provide the optimal Wi-Fi hotspot based on the user's current location. For example, if the user is on the move, the Wi-Fi search unit displays Wi-Fi hotspots along the user's route. This ensures that communication is maintained even in the event of radio interference.

[0075] The disaster response support system includes a battery management unit that reduces battery consumption. The battery management unit optimizes battery consumption of the smartphone. For example, the battery management unit can reduce battery consumption using a power consumption optimization algorithm. The battery management unit optimizes app operation to reduce unnecessary power consumption. For example, the battery management unit automatically stops apps running in the background to reduce battery consumption. The battery management unit can also control display brightness and use of communication functions to minimize battery consumption. For example, the battery management unit automatically adjusts display brightness and controls communication functions to be used only when necessary. Furthermore, the battery management unit can optimize battery consumption based on user usage. For example, if a user frequently uses an app, the battery management unit sets settings to minimize battery consumption. This allows the battery management unit to operate for a long period of time.

[0076] The guidance unit can work in conjunction with a map application to automatically guide users to evacuation shelters. The guidance unit works with a map application to guide users to evacuation shelters. The map application can be, for example, Google Maps or Apple Maps. The guidance unit provides the optimal evacuation route based on the user's current location. For example, if the user is traveling by car, the guidance unit will suggest a vehicle-only route. It can also suggest a pedestrian-only route if the user is traveling on foot. Furthermore, the guidance unit updates the route in real time to provide the optimal evacuation route. For example, the guidance unit updates the route considering traffic conditions and road closure information. This ensures that users can reach evacuation shelters quickly and safely.

[0077] The information providing unit can cooperate with a disaster prevention alert app to provide the latest information such as evacuation information, earthquake information, tsunami forecasts, and weather warnings. The information providing unit cooperates with a disaster prevention alert app to provide the latest disaster prevention information. The disaster prevention alert app can use apps such as Yahoo! Disaster Prevention Alert and NHK News & Disaster Prevention. The information providing unit updates disaster prevention information in real time and provides it to the user. For example, if an earthquake occurs, the information providing unit immediately notifies the user of earthquake information. The information providing unit can also provide information such as tsunami forecasts and weather warnings. Furthermore, the information providing unit can instruct the user on appropriate actions. For example, if an evacuation order is issued, the information providing unit sends a notification urging the user to evacuate. In this way, the information providing unit enables the user to quickly obtain the latest disaster prevention information and take appropriate actions.

[0078] The information providing unit visualizes damage caused by secondary disasters based on location information and can instruct the user on appropriate actions. The information providing unit visualizes damage caused by secondary disasters based on location information. Secondary disasters include, for example, fires and landslides. The information providing unit evaluates the risk of secondary disasters based on the user's current location and instructs the user on appropriate actions. For example, if the user is in a fire area, the information providing unit provides evacuation routes and encourages rapid evacuation. The information providing unit can also send evacuation instructions to users in areas at risk of landslides. Furthermore, the information providing unit updates the damage status of secondary disasters in real time and provides it to the user. For example, the information providing unit notifies the user of the progress of a fire or the occurrence of a landslide. In this way, the information providing unit enables the user to take appropriate actions in response to secondary disasters.

[0079] The information providing unit can collect the latest information about the provision of supplies supported by local governments from SNS and provide instructions to the user. The information providing unit collects the latest information about the provision of supplies supported by local governments from SNS. The provided supplies include, for example, food, water, and medicine. The information providing unit provides appropriate instructions to the user based on the information collected through SNS. For example, the information providing unit notifies the user of the distribution locations and distribution times of supplies provided by local governments. The information providing unit can also inform the user of the stock status and distribution method of the supplies. Furthermore, the information providing unit guides the user to the optimal supply distribution location based on the user's current location. For example, the information providing unit guides the user to quickly access a nearby supply distribution location. In this way, the information providing unit enables the user to quickly obtain the necessary supplies.

[0080] The receiving unit can estimate the user's emotions and adjust the notification method of the Earthquake Early Warning based on the estimated user's emotions. The receiving unit can estimate the user's emotions and adjust the notification method of the Earthquake Early Warning based on the estimated user's emotions. Emotion estimation can be performed using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the receiving unit can change the notification sound to a calmer sound and simplify the notification content. Furthermore, if the user is relaxed, the receiving unit can provide detailed notification content and make the notification sound a normal sound. Furthermore, if the user is in a hurry, the receiving unit can increase the volume of the notification sound and prioritize important information. In this way, the receiving unit can adjust the notification method according to the user's emotions and provide more appropriate notifications.

[0081] When receiving an Earthquake Early Warning, the receiving unit can determine the priority of notifications by referring to past disaster data. When receiving an Earthquake Early Warning, the receiving unit can determine the priority of notifications by referring to past disaster data. Past disaster data includes, for example, past earthquake data and damage situation data. The receiving unit prioritizes notifications to areas that suffered the most damage based on the past disaster data. For example, the receiving unit analyzes past disaster data and quickly notifies areas where damage is predicted. The receiving unit can also delay notifications to areas that suffered less damage by referring to past disaster data. In this way, the receiving unit can appropriately determine the priority of notifications by referring to past disaster data.

[0082] The receiving unit can adjust the timing of the notification based on the user's current activity status when receiving an Earthquake Early Warning. The receiving unit adjusts the timing of the notification based on the user's current activity status when receiving an Earthquake Early Warning. The current activity status can be acquired using, for example, sensor information from a smartphone or the user's behavior history. For example, if the user is driving, the receiving unit can delay the notification and notify the user after the user has stopped in a safe place. Furthermore, if the user is in a meeting, the receiving unit can notify the user after the meeting ends. Furthermore, if the user is sleeping, the receiving unit can notify the user after the user wakes up. In this way, the receiving unit can adjust the timing of the notification according to the user's activity status and provide more appropriate notifications.

[0083] The receiving unit can estimate the user's emotion and change the type of notification sound based on the estimated user's emotion. The receiving unit can estimate the user's emotion and change the type of notification sound based on the estimated user's emotion. Emotion estimation can be performed using techniques such as facial expression recognition and voice analysis. For example, the receiving unit can change the notification sound to a calm sound when the user is nervous. Furthermore, the receiving unit can change the notification sound to a normal sound when the user is relaxed. Furthermore, the receiving unit can increase the volume of the notification sound when the user is in a hurry. In this way, the receiving unit can change the notification sound according to the user's emotion and provide more appropriate notifications.

[0084] When receiving an Earthquake Early Warning, the receiving unit can customize the content of the notification taking into account the user's geographical location information. When receiving an Earthquake Early Warning, the receiving unit customizes the content of the notification taking into account the user's geographical location information. The geographical location information can be acquired using, for example, GPS data or map data. For example, if the user is near the coast, the receiving unit can provide a notification emphasizing the risk of a tsunami. Furthermore, if the user is in a high-rise building, the receiving unit can provide a detailed notification of an evacuation route. Furthermore, if the user is in a subway, the receiving unit can provide a notification of the nearest evacuation location. This allows the receiving unit to customize the content of the notification based on the user's geographical location information and provide more appropriate notifications.

[0085] The receiving unit can analyze the user's social media activity and notify them of relevant information upon receiving an earthquake early warning. Social media activity can be analyzed, for example, by analyzing posts and followers. For instance, if the user frequently posts disaster information on social media, the receiving unit will provide a detailed notification. Furthermore, if the user is searching for evacuation shelters on social media, the receiving unit can notify them of the nearest evacuation shelter. Additionally, if the user is contacting family members on social media, the receiving unit can notify them of the family's safety status. This allows the receiving unit to customize notification content based on the user's social media activity, enabling more appropriate notifications.

[0086] The sharing unit can estimate the user's emotion and adjust the method of sharing location information based on the estimated user's emotion. The sharing unit can estimate the user's emotion and adjust the method of sharing location information based on the estimated user's emotion. Emotion estimation can be performed using techniques such as facial expression recognition and voice analysis. For example, the sharing unit can automatically share location information when the user is nervous. Furthermore, the sharing unit can manually share location information when the user is relaxed. Furthermore, the sharing unit can quickly share location information when the user is in a hurry. This allows the sharing unit to adjust the method of sharing location information according to the user's emotion and perform more appropriate sharing.

[0087] The sharing function can select the optimal recipient when sharing location information by referring to past sharing history. The sharing history includes, for example, past recipients and shared content. For instance, the sharing function prioritizes sharing location information with those with whom it has frequently shared in the past. Furthermore, the sharing function can suggest optimal recipients based on past sharing history. In addition, the sharing function can analyze past sharing history and automatically select recipients. Thus, the sharing function can select the optimal recipient by referring to past sharing history.

[0088] The sharing function can filter shared content based on the user's current areas of interest when sharing location information. Areas of interest can be identified, for example, using survey results or past search history. For instance, if the user is interested in disaster information, the sharing function will prioritize sharing disaster information. Similarly, if the user is interested in evacuation shelters, the sharing function will prioritize sharing information about evacuation shelters. Furthermore, if the user is interested in information about the safety of their family, the sharing function will prioritize sharing information about the safety of their family. This allows the sharing function to filter shared content based on the user's areas of interest and share more relevant information.

[0089] The sharing unit can estimate the user's emotions and determine the priority of location information to share based on those emotions. Emotion estimation can be performed using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the sharing unit will prioritize sharing important location information. If the user is relaxed, the sharing unit can share more detailed location information. Furthermore, if the user is in a hurry, the sharing unit can quickly share location information. This allows the sharing unit to prioritize location information according to the user's emotions and share more appropriate information.

[0090] The shared information system can prioritize sharing highly relevant information by considering the user's geographical location when sharing location information. Geographic location information can be obtained, for example, using GPS data or map data. For example, if the user is near the coast, the shared information system can share information emphasizing tsunami risk. Also, if the user is inside a high-rise building, the shared information system can share detailed evacuation routes. Furthermore, if the user is inside a subway, the shared information system can share the nearest evacuation location. In this way, the shared information system can share highly relevant information based on the user's geographical location and provide more appropriate information.

[0091] The sharing function can analyze a user's social media activity when location information is shared and share relevant information. Social media activity can be analyzed, for example, by analyzing posts and followers. For instance, if a user frequently posts disaster information on social media, the sharing function can share detailed information. Also, if a user searches for evacuation shelters on social media, the sharing function can share information about the nearest evacuation shelters. Furthermore, if a user contacts family members on social media, the sharing function can share information about the safety of those family members. This allows the sharing function to share relevant information based on the user's social media activity and provide more appropriate information.

[0092] The guidance unit can estimate the user's emotions and adjust the evacuation guidance method based on the estimated user's emotions. The guidance unit can estimate the user's emotions and adjust the evacuation guidance method based on the estimated user's emotions. Emotion estimation can be performed using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the guidance unit can provide a simple and highly visible guidance method. Furthermore, if the user is relaxed, the guidance unit can provide detailed guidance information. Furthermore, if the user is in a hurry, the guidance unit can provide a guidance method that emphasizes the shortest route. This allows the guidance unit to adjust the evacuation guidance method according to the user's emotions and provide more appropriate evacuation guidance.

[0093] The guidance unit can select the optimal route during evacuation guidance by referring to past evacuation history. The guidance unit selects the optimal route by referring to past evacuation history. Evacuation history includes, for example, past evacuation routes and evacuation times. For example, the guidance unit proposes the optimal route based on previously used evacuation routes. Furthermore, the guidance unit can propose routes that avoid congestion based on past evacuation history. In addition, the guidance unit can analyze past evacuation history and propose the most efficient route. Thus, the guidance unit can select the optimal evacuation route by referring to past evacuation history.

[0094] The guidance unit can customize the guidance method based on the user's current means of transportation when providing evacuation guidance. The guidance unit customizes the guidance method based on the user's current means of transportation when providing evacuation guidance. The means of transportation includes, for example, walking, bicycle, car, etc. For example, when the user is traveling on foot, the guidance unit can suggest a route exclusively for pedestrians. Furthermore, when the user is traveling by car, the guidance unit can suggest a route exclusively for vehicles. Furthermore, when the user is traveling by bicycle, the guidance unit can suggest a route exclusively for bicycles. This allows the guidance unit to customize the guidance method according to the user's means of transportation and provide more appropriate evacuation guidance.

[0095] The guidance unit can estimate the user's emotions and determine the priority of evacuation guidance based on the estimated user's emotions. The guidance unit can estimate the user's emotions and determine the priority of evacuation guidance based on the estimated user's emotions. Emotion estimation can be performed using techniques such as facial expression recognition and voice analysis. For example, if the user is nervous, the guidance unit can provide important evacuation information preferentially. Furthermore, if the user is relaxed, the guidance unit can provide detailed evacuation information quickly. Furthermore, if the user is in a hurry, the guidance unit can determine the priority of evacuation guidance based on the user's emotions and provide more appropriate evacuation guidance.

[0096] The guiding unit can provide an optimal evacuation route by taking into consideration the user's geographical location information when providing evacuation guidance. The guiding unit can provide an optimal evacuation route by taking into consideration the user's geographical location information when providing evacuation guidance. The geographical location information can be acquired using, for example, GPS data or map data. For example, if the user is near the coast, the guiding unit can provide a route that avoids the risk of a tsunami. Furthermore, if the user is in a high-rise building, the guiding unit can provide the nearest evacuation route. Furthermore, if the user is in a subway, the guiding unit can provide the nearest evacuation location. This allows the guiding unit to provide an optimal evacuation route based on the user's geographical location information and provide more appropriate evacuation guidance.

[0097] The guidance unit can analyze the user's social media activity and provide relevant information when guiding the user to an evacuation route. The guidance unit can analyze the user's social media activity and provide relevant information when guiding the user to an evacuation route. Social media activity can be analyzed, for example, by analyzing the content of posts or followers. For example, if the user frequently posts disaster information on the social media, the guidance unit can provide detailed evacuation information. Furthermore, if the user searches for evacuation locations on the social media, the guidance unit can provide information on the nearest evacuation location. Furthermore, if the user is in contact with family members on the social media, the guidance unit can provide information on the safety of the family members. This allows the guidance unit to provide relevant information based on the user's social media activity and provide more appropriate evacuation guidance.

[0098] The information providing unit can estimate the user's emotions and adjust the display method of disaster prevention information based on the estimated user emotions. The information providing unit can estimate the user's emotions and adjust the display method of disaster prevention information based on the estimated user emotions. Emotion estimation can be performed using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the information providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the information providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the information providing unit can provide a display method that focuses on the main points. In this way, the information providing unit can adjust the display method of disaster prevention information according to the user's emotions and provide more appropriate information.

[0099] When providing disaster prevention information, the information providing unit can determine the priority of information by referring to past disaster data. When providing disaster prevention information, the information providing unit can determine the priority of information by referring to past disaster data. Past disaster data includes, for example, past earthquake data and damage situation data. For example, the information providing unit can provide information preferentially to areas that suffered the most damage based on the past disaster data. In addition, the information providing unit can analyze the past disaster data and quickly provide information to areas where damage is predicted. Furthermore, the information providing unit can delay providing information to areas that suffered less damage by referring to the past disaster data. In this way, the information providing unit can appropriately determine the priority of information by referring to the past disaster data.

[0100] The information providing unit can filter information based on the user's current areas of interest when providing disaster prevention information. The information providing unit filters information based on the user's current areas of interest when providing disaster prevention information. The areas of interest can be identified using, for example, survey results or past search history. For example, if the user is interested in disaster information, the information providing unit can provide disaster information preferentially. Furthermore, if the user is interested in evacuation locations, the information providing unit can provide evacuation location information preferentially. Furthermore, if the user is interested in information on the safety of family members, the information providing unit can provide family safety information preferentially. This allows the information providing unit to filter information based on the user's areas of interest and provide more appropriate information.

[0101] The information provision unit can estimate the user's emotions and adjust the display order of disaster prevention information based on the estimated emotions. Emotion estimation can be performed using technologies such as facial recognition and voice analysis. For example, if the user is tense, the information provision unit will prioritize displaying important information. Furthermore, if the user is relaxed, the information provision unit can display detailed information. Additionally, if the user is in a hurry, the information provision unit can display information quickly. This allows the information provision unit to adjust the display order of disaster prevention information according to the user's emotions, enabling more appropriate information provision.

[0102] When providing disaster prevention information, the information providing unit can prioritize providing highly relevant information taking into account the user's geographical location information. When providing disaster prevention information, the information providing unit can prioritize providing highly relevant information taking into account the user's geographical location information. The geographical location information can be acquired using, for example, GPS data or map data. For example, when the user is near the coast, the information providing unit can provide information emphasizing the risk of a tsunami. Furthermore, when the user is in a high-rise building, the information providing unit can provide detailed evacuation routes. Furthermore, when the user is in a subway, the information providing unit can provide the nearest evacuation location. This allows the information providing unit to provide highly relevant information based on the user's geographical location information, thereby providing more appropriate information.

[0103] The Information Provision Department can analyze users' social media activity when providing disaster prevention information and provide relevant information. Social media activity can be analyzed, for example, by analyzing post content or followers. For example, if a user frequently posts disaster information on social media, the Information Provision Department can provide detailed information. Also, if a user searches for evacuation shelters on social media, the Information Provision Department can provide information on the nearest evacuation shelters. Furthermore, if a user is in contact with family members on social media, the Information Provision Department can provide information on the safety of those family members. This allows the Information Provision Department to provide more appropriate information based on users' social media activity.

[0104] The voice input unit can estimate the user's emotion and adjust the recognition accuracy of the voice input based on the estimated user's emotion. The voice input unit can estimate the user's emotion and adjust the recognition accuracy of the voice input based on the estimated user's emotion. Emotion estimation can be performed using techniques such as facial expression recognition and voice analysis. For example, if the user is nervous, the voice input unit increases the sensitivity of the voice input to improve recognition accuracy. Furthermore, if the user is relaxed, the voice input unit can perform voice input with normal recognition accuracy. Furthermore, if the user is in a hurry, the voice input unit can adjust the sensitivity to quickly recognize the voice input. As a result, the voice input unit can adjust the recognition accuracy of the voice input according to the user's emotion and perform more appropriate voice input.

[0105] The voice input unit can select the optimal recognition algorithm by referring to past voice input history during voice input. The voice input history includes, for example, past voice commands and recognition results. For instance, the voice input unit prioritizes recognizing voice commands that have been frequently used in the past. Furthermore, the voice input unit can select the optimal recognition algorithm based on past voice input history. In addition, the voice input unit can analyze past voice input history to improve recognition accuracy. As a result, the voice input unit can select the optimal recognition algorithm by referring to past voice input history, enabling more appropriate voice input.

[0106] The audio input unit can perform noise cancellation taking into account the user's current environmental sound when inputting audio. The audio input unit can perform noise cancellation taking into account the user's current environmental sound when inputting audio. The environmental sound can be acquired using, for example, the type of microphone or noise filtering technology. For example, the audio input unit can enhance noise cancellation when the user is in a noisy place. Furthermore, the audio input unit can perform normal noise cancellation when the user is in a quiet place. Furthermore, the audio input unit can adjust noise cancellation according to the environmental sound when the user is moving. This allows the audio input unit to perform noise cancellation according to the user's environmental sound and perform more appropriate audio input.

[0107] The voice input unit can estimate the user's emotions and determine the priority of voice input based on the estimated emotions. Emotion estimation can be performed using technologies such as facial recognition and voice analysis. For example, if the user is tense, the voice input unit will prioritize recognizing important voice input. Furthermore, if the user is relaxed, the voice input unit can recognize detailed voice input. Additionally, if the user is in a hurry, the voice input unit can quickly recognize voice input. This allows the voice input unit to prioritize voice input according to the user's emotions, enabling more appropriate voice input.

[0108] The voice input unit can improve recognition accuracy by taking into account the user's geographical location information when inputting voice. The voice input unit improves recognition accuracy by taking into account the user's geographical location information when inputting voice. The geographical location information can be acquired using, for example, GPS data or map data. For example, when the user is near the coast, the voice input unit can prioritize recognizing voice input related to tsunami risk. Furthermore, when the user is in a high-rise building, the voice input unit can prioritize recognizing voice input related to evacuation routes. Furthermore, when the user is in a subway, the voice input unit can prioritize recognizing voice input related to the nearest evacuation location. This allows the voice input unit to improve recognition accuracy based on the user's geographical location information and perform more appropriate voice input.

[0109] The voice input unit can analyze the user's social media activity and recognize related information when the user inputs voice. The voice input unit can analyze the user's social media activity and recognize related information when the user inputs voice. Social media activity can be analyzed, for example, by analyzing the content of posts or followers. For example, if the user frequently posts disaster information on the social media, the voice input unit can recognize detailed voice input. Furthermore, if the user is searching for evacuation shelters on the social media, the voice input unit can recognize voice input regarding the nearest evacuation shelter. Furthermore, if the user is in contact with family members on the social media, the voice input unit can recognize voice input regarding information about the safety of family members. This allows the voice input unit to recognize related information based on the user's social media activity and provide more appropriate voice input.

[0110] The WiFi search unit can estimate a user's emotion and adjust a WiFi spot search method based on the estimated user emotion. The WiFi search unit can estimate a user's emotion and adjust a WiFi spot search method based on the estimated user emotion. Emotion estimation can be performed using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the WiFi search unit can quickly search for a WiFi spot. Also, if the user is relaxed, the WiFi search unit can provide detailed WiFi spot information. Furthermore, if the user is in a hurry, the WiFi search unit can prioritize searching for the nearest WiFi spot. As a result, the WiFi search unit can adjust a WiFi spot search method according to the user's emotion and provide a more appropriate WiFi spot.

[0111] When searching for a WiFi spot, the WiFi search unit can select the optimal spot by referring to past connection history. When searching for a WiFi spot, the WiFi search unit can select the optimal spot by referring to past connection history. The connection history includes, for example, past connection destinations and connection times. For example, the WiFi search unit prioritizes searching for WiFi spots that have been frequently connected to in the past. The WiFi search unit can also select the optimal WiFi spot based on the past connection history. Furthermore, the WiFi search unit can analyze the past connection history and select the most stable WiFi spot. As a result, the WiFi search unit can select the optimal WiFi spot by referring to the past connection history, and provide a more appropriate WiFi spot.

[0112] When searching for a WiFi spot, the WiFi search unit can customize the search range based on the user's current location information. When searching for a WiFi spot, the WiFi search unit can customize the search range based on the user's current location information. The location information can be obtained using, for example, GPS data or map data. For example, when the user is in an urban area, the WiFi search unit prioritizes searching for nearby WiFi spots. Furthermore, when the user is in a suburban area, the WiFi search unit can search for WiFi spots over a wide area. Furthermore, when the user is moving, the WiFi search unit can search for WiFi spots along the user's route. This allows the WiFi search unit to customize the search range based on the user's location information and provide more appropriate WiFi spots.

[0113] The WiFi search unit can estimate a user's emotion and prioritize WiFi spots based on the estimated user emotion. The WiFi search unit can estimate a user's emotion and prioritize WiFi spots based on the estimated user emotion. Emotion estimation can be performed using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the WiFi search unit can prioritize providing the most stable WiFi spot. Furthermore, if the user is relaxed, the WiFi search unit can prioritize providing the nearest WiFi spot. In this way, the WiFi search unit can prioritize WiFi spots according to the user's emotion and provide a more appropriate WiFi spot.

[0114] When searching for WiFi spots, the WiFi search unit can provide the optimal spot by taking into account the user's geographical location information. When searching for WiFi spots, the WiFi search unit can provide the optimal spot by taking into account the user's geographical location information. Geographical location information can be acquired using, for example, GPS data or map data. For example, if the user is near the coast, the WiFi search unit can provide the nearest WiFi spot. Also, if the user is in a high-rise building, the WiFi search unit can provide WiFi spots within the building. Furthermore, if the user is in a subway, the WiFi search unit can provide WiFi spots within the subway. In this way, the WiFi search unit can provide the optimal WiFi spot based on the user's geographical location information, thereby providing a more appropriate WiFi spot.

[0115] The WiFi search unit can analyze a user's social media activity and provide related information when searching for a WiFi spot. The WiFi search unit can analyze a user's social media activity and provide related information when searching for a WiFi spot. Social media activity can be analyzed, for example, by analyzing posts or followers. For example, if a user frequently posts information about WiFi spots on a social media site, the WiFi search unit can provide detailed WiFi spot information. Also, if a user is searching for a specific WiFi spot on a social media site, the WiFi search unit can provide information about the WiFi spot. Furthermore, if a user is in contact with friends on a social media site, the WiFi search unit can provide information about WiFi spots used by the friends. This allows the WiFi search unit to provide related information based on the user's social media activity and provide more appropriate WiFi spots.

[0116] The battery management unit can estimate the user's emotions and adjust the battery consumption management method based on the estimated user emotions. The battery management unit can estimate the user's emotions and adjust the battery consumption management method based on the estimated user emotions. Emotion estimation can be performed using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the battery management unit can set the battery to minimize battery consumption. Also, if the user is relaxed, the battery management unit can set the battery to normal battery consumption. Furthermore, if the user is in a hurry, the battery management unit can set the battery to optimize battery consumption. In this way, the battery management unit can adjust the battery consumption management method according to the user's emotions and perform more appropriate battery management.

[0117] The battery management unit can select an optimal management method by referring to past battery consumption history when managing batteries. The battery management unit can select an optimal management method by referring to past battery consumption history when managing batteries. The battery consumption history includes, for example, past consumption amounts and used apps. For example, the battery management unit proposes an optimal battery management method based on the past battery consumption history. The battery management unit can also analyze the past battery consumption history and select a method that minimizes battery consumption. Furthermore, the battery management unit can select a method that optimizes battery consumption by referring to the past battery consumption history. In this way, the battery management unit can select an optimal battery management method by referring to the past battery consumption history, thereby enabling more appropriate battery management.

[0118] The battery management unit can optimize consumption based on the user's current usage status during battery management. The battery management unit optimizes consumption based on the user's current usage status during battery management. The usage status can be acquired using, for example, app usage time or device usage status. For example, the battery management unit can set the battery to minimize battery consumption when the user frequently uses apps. Furthermore, the battery management unit can set normal battery consumption when the user does not use apps very often. Furthermore, the battery management unit can set the battery to optimize battery consumption when the user is on the move. This allows the battery management unit to optimize consumption based on the user's usage status and perform more appropriate battery management.

[0119] The battery management unit can estimate the user's emotions and determine the priority of battery consumption based on the estimated user's emotions. The battery management unit can estimate the user's emotions and determine the priority of battery consumption based on the estimated user's emotions. Emotion estimation can be performed using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the battery management unit can prioritize battery consumption of important apps. Furthermore, if the user is relaxed, the battery management unit can perform detailed battery consumption management. Furthermore, if the user is in a hurry, the battery management unit can quickly manage battery consumption. This allows the battery management unit to determine the priority of battery consumption according to the user's emotions and perform more appropriate battery management.

[0120] The battery management unit can provide an optimal battery management method by taking into account the user's geographical location information when managing the battery. The battery management unit can provide an optimal battery management method by taking into account the user's geographical location information when managing the battery. Geographical location information can be acquired using, for example, GPS data or map data. For example, the battery management unit can set the battery to minimize battery consumption when the user is near the coast. Furthermore, the battery management unit can set the battery to consume normal amounts of battery when the user is in a high-rise building. Furthermore, the battery management unit can set the battery to optimize battery consumption when the user is in a subway. This allows the battery management unit to provide an optimal battery management method based on the user's geographical location information and perform more appropriate battery management.

[0121] The battery management unit can analyze a user's social media activity and provide related information when managing the battery. The battery management unit can analyze a user's social media activity and provide related information when managing the battery. Social media activity can be analyzed, for example, by analyzing the content of posts or followers. For example, if a user frequently posts information about battery consumption on a social media site, the battery management unit can provide detailed battery management information. Also, if a user searches for the battery consumption of a specific app on a social media site, the battery management unit can provide battery management information for that app. Furthermore, if a user contacts friends on a social media site, the battery management unit can provide the battery management method used by the friends. This allows the battery management unit to provide related information based on the user's social media activity and perform more appropriate battery management. === Hard Collateral 1-1 === Each of the above-described elements, including the receiving unit, sharing unit, guidance unit, information providing unit, voice input unit, WiFi searching unit, battery management unit, and emotion estimation function, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the receiving unit receives an earthquake early warning via the communication I / F 44 of the smart device 14. The sharing unit acquires location information using the GPS function of the smart device 14 and shares the information with family and related parties via the specific processing unit 290 of the data processing device 12. The guidance unit provides routes to evacuation sites in cooperation with a map app of the smart device 14. The information providing unit provides disaster prevention information via the specific processing unit 290 of the data processing device 12. The voice input unit inputs voice using the microphone 38B of the smart device 14 and converts the voice into text via the specific processing unit 290 of the data processing device 12. The WiFi searching unit detects nearby WiFi hotspots using the WiFi function of the smart device 14. The battery management unit optimizes battery consumption via the control unit 46A of the smart device 14. The emotion estimation function estimates the user's emotions using the camera 42 and microphone 38B of the smart device 14, and the identification processing unit 290 of the data processing device 12 adjusts the notification method. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned receiving unit, sharing unit, guidance unit, information providing unit, voice input unit, WiFi searching unit, battery management unit, and emotion estimation function, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the receiving unit receives an emergency earthquake alert via the communication I / F 44 of the smart glasses 214. The sharing unit acquires location information using the GPS function of the smart glasses 214 and shares the information with family and related parties via the specific processing unit 290 of the data processing device 12. The guidance unit provides routes to evacuation sites in cooperation with a map app of the smart glasses 214. The information providing unit provides disaster prevention information via the specific processing unit 290 of the data processing device 12. The voice input unit inputs voice using the microphone 238 of the smart glasses 214 and converts the voice into text via the specific processing unit 290 of the data processing device 12. The WiFi searching unit detects nearby WiFi hotspots using the WiFi function of the smart glasses 214. The battery management unit optimizes battery consumption via the control unit 46A of the smart glasses 214. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the smart glasses 214, and adjusts the notification method by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned receiving unit, sharing unit, guidance unit, information providing unit, voice input unit, WiFi searching unit, battery management unit, and emotion estimation function, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the receiving unit receives an emergency earthquake alert via the communication I / F 44 of the headset type terminal 314. The sharing unit acquires location information using the GPS function of the headset type terminal 314 and shares the information with family and related parties via the identification processing unit 290 of the data processing device 12. The guidance unit provides routes to evacuation sites in cooperation with a map app of the headset type terminal 314. The information providing unit provides disaster prevention information via the identification processing unit 290 of the data processing device 12. The voice input unit inputs voice using the microphone 238 of the headset type terminal 314 and converts the voice into text via the identification processing unit 290 of the data processing device 12. The WiFi searching unit detects nearby WiFi hotspots using the WiFi function of the headset type terminal 314. The battery management unit optimizes battery consumption via the control unit 46A of the headset type terminal 314. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the headset terminal 314, and adjusts the notification method by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the receiving unit, sharing unit, guidance unit, information providing unit, voice input unit, WiFi search unit, battery management unit, and emotion estimation function, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the receiving unit receives an emergency earthquake alert via the communication I / F 44 of the robot 414. The sharing unit acquires location information using the GPS function of the robot 414 and shares the information with family and related parties via the specific processing unit 290 of the data processing device 12. The guidance unit provides routes to evacuation sites in cooperation with the map app of the robot 414. The information providing unit provides disaster prevention information via the specific processing unit 290 of the data processing device 12. The voice input unit inputs voice using the microphone 238 of the robot 414 and converts the voice into text via the specific processing unit 290 of the data processing device 12. The WiFi search unit detects nearby WiFi hotspots using the WiFi function of the robot 414. The battery management unit optimizes battery consumption via the control unit 46A of the robot 414. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the robot 414, and adjusts the notification method by the specific processing unit 290 of the data processing device 12.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The disaster response support system can also be equipped with a health management unit that monitors the user's health condition. The health management unit acquires vital data such as the user's heart rate and body temperature, and issues an emergency notification if an abnormality is detected. For example, if the user's heart rate suddenly increases, a notification urging the user to take deep breaths is sent. Also, if the body temperature is high, a notification can be sent warning of the risk of heatstroke and encouraging the user to drink more fluids. Furthermore, the health management unit can share the user's vital data with family and medical institutions to support a rapid response. This allows the user's health condition to be monitored in real time during a disaster, and appropriate measures can be taken.

[0124] The disaster response support system can also include a psychological evaluation unit that evaluates the user's psychological state. The psychological evaluation unit estimates the user's stress level from their voice and facial expressions and suggests relaxation methods as needed. For example, if the user is in a high stress state, it provides guidance on deep breathing or meditation. The psychological evaluation unit can also adjust the frequency and content of notifications according to the user's stress level. For example, if the user is under high stress, it can provide simple notifications, and if the user is relaxed, it can provide detailed information. Furthermore, the psychological evaluation unit can share the user's psychological state with family members or experts and provide appropriate support. This can reduce the user's psychological burden during a disaster and support appropriate responses.

[0125] The disaster response support system can also be equipped with a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit analyzes evacuation behavior patterns based on the user's past behavior data and proposes the optimal evacuation route. For example, it proposes a route that avoids congestion, taking into account evacuation routes and evacuation times used in the past. The behavior analysis unit can also support the selection of evacuation locations based on the user's behavior history. For example, it evaluates the safety and convenience of locations where people have evacuated in the past and proposes the optimal evacuation location. Furthermore, the behavior analysis unit can share the user's behavior history with family and related parties to support rapid evacuation. This can optimize the user's evacuation behavior in the event of a disaster and support rapid and safe evacuation.

[0126] The disaster response support system may further include a communication support unit that supports user communication. The communication support unit enables users to send messages by voice or text, facilitating communication with family and related parties. For example, a message can be sent using voice input, and the recipient can reply by text. The communication support unit can also estimate the user's emotions and provide message templates according to the emotions. For example, if the user is nervous, the communication support unit can suggest an encouraging message, and if the user is relaxed, the communication support unit can provide detailed information. Furthermore, the communication support unit can analyze the user's message history and suggest appropriate replies. This supports user communication during a disaster and enables rapid information sharing.

[0127] The disaster response support system can also include a mobility management unit that manages the user's means of transportation. The mobility management unit grasps the user's current means of transportation and provides the optimal evacuation route. For example, if the user is traveling by car, it proposes a route exclusively for vehicles, and if the user is traveling on foot, it proposes a route exclusively for pedestrians. The mobility management unit can also support the selection of an evacuation site depending on the user's means of transportation. For example, if the user is traveling by car, it proposes an evacuation site with a parking lot, and if the user is traveling on foot, it proposes a nearby evacuation site. Furthermore, the mobility management unit can share the user's means of transportation with family and related parties to support rapid evacuation. This can optimize the user's movement in the event of a disaster and support rapid and safe evacuation.

[0128] The disaster response support system can further estimate the user's emotions and adjust the evacuation guidance method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible guidance method can be provided. If the user is relaxed, detailed guidance information can be provided. Furthermore, if the user is in a hurry, a guidance method that emphasizes the shortest route can be provided. This allows the evacuation guidance method to be adjusted according to the user's emotions, making it possible to provide more appropriate evacuation guidance.

[0129] The disaster response support system can further estimate the user's emotions and adjust the display method of disaster prevention information based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method of disaster prevention information to be adjusted according to the user's emotions, making it possible to provide more appropriate information.

[0130] The disaster response support system can further estimate the user's emotions and adjust the recognition accuracy of the voice input based on the estimated user's emotions. For example, if the user is nervous, the sensitivity of the voice input can be increased to improve recognition accuracy. On the other hand, if the user is relaxed, voice input can be performed with normal recognition accuracy. Furthermore, if the user is in a hurry, the sensitivity can be adjusted to quickly recognize the voice input. In this way, the recognition accuracy of the voice input can be adjusted according to the user's emotions, and more appropriate voice input can be performed.

[0131] The disaster response support system can further estimate the user's emotions and adjust battery consumption management based on those emotions. For example, if the user is stressed, it can be set to minimize battery consumption. If the user is relaxed, it can be set to normal battery consumption settings. Furthermore, if the user is in a hurry, it can be set to optimize battery consumption. This allows for more appropriate battery management by adjusting battery consumption management according to the user's emotions.

[0132] The disaster response support system can further estimate the user's emotions and adjust its Wi-Fi hotspot search method based on those emotions. For example, if the user is stressed, it can quickly search for Wi-Fi hotspots. If the user is relaxed, it can provide detailed Wi-Fi hotspot information. Furthermore, if the user is in a hurry, it can prioritize searching for the nearest Wi-Fi hotspot. This allows the system to adjust its Wi-Fi hotspot search method according to the user's emotions and provide a more appropriate Wi-Fi hotspot.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The receiving unit receives an earthquake early warning. The earthquake early warning may be provided, for example, by the Japan Meteorological Agency or other public organizations. The receiving unit receives the warning via an internet connection, for example. The receiving unit may also receive the warning via radio waves or television broadcasts. Step 2: The sharing unit shares the location information based on the information received by the receiving unit. The location information is obtained, for example, using GPS data or WiFi location information. The sharing unit automatically shares the obtained location information with pre-registered family members and related parties. The sharing unit shares the location information, for example, via an Internet connection. The sharing unit can also share the location information via SMS or email. Step 3: The guidance unit guides the user to an evacuation site based on the location information shared by the sharing unit. The guidance unit provides the optimal route to the evacuation site, for example, in cooperation with a map app. The guidance unit can update the route from the user's current location to the evacuation site in real time. Step 4: The information provider provides disaster prevention information. Disaster prevention information includes, for example, "evacuation information," "earthquake information," "tsunami forecasts," and "weather warnings." The information provider provides the latest disaster prevention information in cooperation with the disaster prevention alert app. The information provider can instruct the user on appropriate actions.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0183] 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.

[0184] 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.

[0185] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 receiving unit for receiving an emergency earthquake alert; a sharing unit that shares location information based on the information received by the receiving unit; a guidance unit that guides the user to an evacuation site based on the location information shared by the sharing unit; an information providing unit that provides disaster prevention information; A system characterized by:

2. Equipped with an audio input unit that accepts audio input 2. The system of claim 1.

3. Equipped with a WiFi search function that finds WiFi hotspots 2. The system of claim 1.

4. Equipped with a battery management unit that reduces battery consumption 2. The system of claim 1.

5. The induction section is Links with map apps to automatically guide users to evacuation shelters 2. The system of claim 1.

6. The information providing unit Linking with disaster prevention alert apps to provide the latest information on one or more of the following: evacuation information, earthquake information, tsunami forecasts, and weather warnings 2. The system of claim 1.

7. The information providing unit Visualizing damage caused by secondary disasters using location information and instructing users on appropriate actions 2. The system of claim 1.

8. The information providing unit Collects the latest information on local government support for relief supplies from social media and provides instructions to users 2. The system of claim 1.

9. The receiving unit Estimates user emotions and adjusts the notification method for earthquake early warnings based on the estimated user emotions.

2. The system of claim 1.

10. The receiving unit When an emergency earthquake alert is received, past disaster data is referenced to determine the priority of notifications.

2. The system of claim 1.

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