System

Abstract

A system is provided.SOLUTION: This system includes a means for recording the action pattern of a user, a means for acquiring the present location information of the user in a disaster, a means for detecting a difference between the acquired present location information and the action pattern, and a means for providing the nearest shelter information to the user when the difference is detected.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] During large-scale disasters, it is difficult to grasp the location information of victims, and rapid rescue is required, but disruptions to communication infrastructure and large amounts of information make it difficult to respond appropriately. Rescue teams also need to carry out rescue operations efficiently, so it is important to communicate with victims and secure their location information. However, conventional technology lacks a system for effectively collecting and sharing location information of victims, making rapid rescue difficult. [Means for solving the problem]

[0005] This invention is a system that records a user's behavioral patterns in advance, detects abnormal location information during a disaster, and provides appropriate information. Specifically, it includes a means for recording the user's behavioral patterns, a means for acquiring the user's current location information during a disaster, a means for detecting differences between the acquired current location information and the user's behavioral patterns, and a means for providing the user with information about the nearest evacuation shelter if a difference is detected. It also includes a means for notifying a rescue team of the user's current location information, displaying the user's location information on the rescue team's device, and increasing the volume of a beep when the rescue team approaches the user. It also includes a means for collecting and analyzing weather information and information from local governments to provide the user with the optimal evacuation route. This enables rapid and efficient rescue operations, ensuring the safety of victims and rescue team members.

[0006] "Behavioral patterns" refer to the locations and routes that a user normally takes, and the history of location information during a specific time period.

[0007] "Disaster" refers to a natural disaster or other emergency that requires an emergency response in a specific area.

[0008] "Current location information" refers to the latitude and longitude location information identified by the GPS data currently being transmitted by the user's terminal.

[0009] "Means for detecting differences" refers to algorithms and data processing systems that compare normal behavioral patterns with current location information during a disaster and identify abnormal location information.

[0010] "Evacuation shelter information" refers to information about safe evacuation locations in the event of a disaster, as well as how to get to and use those locations.

[0011] A "rescue team" refers to a specialized team or organization dispatched to search for victims and carry out rescue operations during a disaster.

[0012] "Means to increase the volume of the call volume" refers to a function that adjusts the volume of the voice notification when the rescue team's device approaches a victim, making it easier to identify the victim's location.

[0013] "Weather information" refers to data relating to current weather conditions and forecasts, including, among other things, information on precipitation, temperature, wind speed, and wind direction.

[0014] "Information from local governments" refers to disaster-related information provided by public institutions, including evacuation orders, warnings, traffic information, etc.

[0015] "Analysis" refers to the process of analyzing the situation based on collected data and deriving the optimal evacuation route and instructions.

[0016] The "optimal evacuation route" refers to a route that allows the user to reach a shelter safely and quickly, and is calculated taking into account meteorological information and other circumstances. [Brief explanation of the drawings]

[0017] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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, a 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), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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), Bluetooth (registered trademark), etc.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention provides a system that effectively manages user location information during a disaster and supports rapid evacuation and rescue operations. This system is realized using user terminals and a server-side infrastructure.

[0039] Recording behavioral patterns

[0040] The device records the user's usual behavioral patterns, including the places the user regularly visits, their route, and their location during specific times of the day. The device periodically (e.g., every minute) acquires GPS data and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[0041] Detecting abnormal situations during disasters

[0042] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake or typhoon warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly.

[0043] Providing evacuation shelter information

[0044] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[0045] Rescue operation support

[0046] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are essential. The server collects location information of victims within a 2,000m radius of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the dedicated terminals carried by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated terminal will increase the volume of its beep, enabling them to identify the person to be rescued and respond quickly.

[0047] Real-time situation analysis and instructions

[0048] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. Using generative AI, the server calculates the safest and quickest evacuation route. Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[0049] The above is a specific embodiment of the system of the present invention. This system ensures the safety of users and supports the efficient activities of rescue teams. It provides rapid and accurate information and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue team members.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The device periodically (e.g., every minute) acquires GPS data to monitor the user's daily activities. The device records the user's current latitude, longitude, and timestamp.

[0053] Step 2:

[0054] The device sends the acquired GPS data to the server, which includes latitude, longitude, and a timestamp.

[0055] Step 3:

[0056] The server receives the GPS data sent from the device and stores it in a database. The server organizes the data for each user and learns their behavioral patterns.

[0057] Step 4:

[0058] When a disaster occurs, the server obtains information from the disaster information service in real time. For example, let's assume that an earthquake or typhoon warning is issued.

[0059] Step 5:

[0060] The server checks the user's current location information as current GPS data, compares the acquired current location information with daily behavior patterns, and detects abnormalities.

[0061] Step 6:

[0062] If an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location, obtains the location information of the evacuation shelter, and sends it to the user's device.

[0063] Step 7:

[0064] The device then notifies the user of the received evacuation shelter information and guides them to the shelter using map display and route guidance functions.

[0065] Step 8:

[0066] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and displays it on a map, allowing rescue teams to grasp the locations of disaster victims.

[0067] Step 9:

[0068] The server transmits the location information of the victims to the dedicated terminal of the rescue team, which visually displays the location information.

[0069] Step 10:

[0070] When the rescue team's dedicated device approaches a victim, it activates a louder beep, allowing the rescue team to quickly pinpoint the victim's exact location.

[0071] Step 11:

[0072] As the situation changes every moment, the server collects weather information and information from local governments and analyzes it in real time using generative AI.

[0073] Step 12:

[0074] The server calculates the safest and quickest evacuation route based on the analysis results and sends the optimal evacuation instructions to the user's device.

[0075] Step 13:

[0076] The device notifies the user of the received evacuation instructions and prompts them to take specific evacuation actions, thereby supporting the user's quick and safe evacuation.

[0077] Example 1

[0078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0079] When a disaster occurs, it is extremely important to quickly and accurately grasp users' location information, provide appropriate evacuation instructions, and carry out efficient rescue operations. However, conventional systems lack the ability to record and analyze users' behavioral patterns in detail, which can delay the detection of abnormalities during a disaster and lead to inappropriate subsequent responses. Another issue is the insufficient ability of rescue teams to quickly identify victims and support their rescue operations. Furthermore, there is a need for systems that can comprehensively analyze meteorological information collected in real time and information from local governments to provide users with optimal evacuation routes.

[0080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0081] In this invention, the server includes a means for recording a user's behavioral patterns, a means for acquiring the user's current location information during a disaster, a means for detecting differences between the acquired current location information and the user's behavioral patterns, a means for providing the user with information on the nearest evacuation shelter when a difference is detected, a means for notifying the user of a route to the evacuation shelter, and a means for collecting and displaying location information on other disaster victims within a certain radius of the evacuation shelter. This allows for detailed analysis of the user's location information during a disaster and early detection of abnormalities. Furthermore, by providing evacuation shelter information promptly and supporting efficient rescue efforts for disaster victims, the safety of the user and rescue teams can be ensured. Furthermore, the system can provide optimal evacuation routes using real-time weather information and information from local governments, more effectively supporting the safe evacuation of disaster victims.

[0082] "Behavioral patterns" are data that show the consistency of location information, travel routes, and places visited in a user's daily life.

[0083] "Location information" refers to a user's real-time location data, typically geographic coordinates obtained from a GPS sensor or similar.

[0084] "Anomaly detection" refers to a process of detecting whether the acquired current location information significantly deviates from the user's normal behavior pattern.

[0085] "Evacuation shelter information" refers to location data and access routes to evacuation destinations that are necessary for users to evacuate safely in the event of a disaster.

[0086] "Route notification" is a process that has the function of instructing the user on the route to the nearest evacuation shelter.

[0087] "Victim location information" refers to data collected on the current locations of victims who are within a certain distance of an evacuation shelter and used to support rescue efforts.

[0088] A "rescue team" refers to an organization or group that carries out rescue and assistance activities for victims when a disaster occurs.

[0089] The "evacuation route" is information indicating the optimal route for a user to safely evacuate in the event of a disaster.

[0090] "Generative AI" refers to artificial intelligence models and algorithms that generate optimal results or suggestions based on input data.

[0091] "Weather information" refers to data relating to weather, including real-time weather data such as temperature, humidity, wind speed, and precipitation.

[0092] "Municipal information" refers to official information provided by local governments, such as emergency information, disaster situations, and evacuation instructions.

[0093] The present invention provides a system that effectively manages user location information during a disaster and supports rapid evacuation and rescue operations. This system is realized using user terminals and a server-side infrastructure.

[0094] Recording behavioral patterns

[0095] The device records the user's daily behavioral patterns. Specifically, it activates the GPS sensor and acquires current location information every minute. The acquired location information is temporarily stored in local memory and then periodically sent to the server using the communication module. The server receives the location data sent from the device and stores it in a database. It analyzes the stored data and applies machine learning algorithms to learn the user's behavioral patterns.

[0096] Specific examples

[0097] The device records the user's commute route from home to work every weekday morning at 8:00 a.m., and the data is stored on the server for about a month. The server analyzes this data and learns the user's commute route.

[0098] Detecting abnormal situations

[0099] When a disaster occurs, the server uses the disaster information service API to obtain real-time information about earthquakes and typhoons. When disaster information is received, it compares the user's behavioral patterns with their current location. If the current location significantly deviates from the normal behavioral pattern, it detects this as an anomaly.

[0100] Specific examples

[0101] The server receives typhoon warnings and flags an anomaly if it detects that a user is in an unusual location outside of the workday.

[0102] Providing evacuation shelter information

[0103] When an abnormality is detected, the server searches for information on the nearest evacuation shelter based on the user's current location and sends that location information to the user's device. The device receives the evacuation shelter information and notifies the user. The notification includes a map display and route guidance functions to support safe evacuation.

[0104] Specific examples

[0105] When the user is in an abnormal area, the server calculates the coordinates of the nearest evacuation shelter (e.g., a community center) and sends them to the device. The device receives this information and notifies the user that "there is a shelter 500 meters from here."

[0106] Rescue operation support

[0107] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and transmits it to the rescue team's dedicated terminal as a map display. The rescue team's terminal displays a map visualizing the location information of disaster victims and has a function that increases the volume of a beep when the rescue team approaches.

[0108] Specific examples

[0109] A map of victims around the evacuation shelter is displayed on the rescue team's device, and when they get within a certain distance, an alert sounds saying "Victims approaching."

[0110] Real-time situation analysis and instructions

[0111] The server collects meteorological information and information from local governments in real time and analyzes this information using generative AI. Based on the analysis results, the optimal evacuation route is displayed on the user's device. The device then displays a map and provides voice guidance to encourage appropriate evacuation behavior.

[0112] Specific examples

[0113] Prompt for generative AI model: "Based on current weather information and behavioral patterns, what is the best route for the user to evacuate?"

[0114] The AI ​​analyzes and determines that "it would be best for the user to walk 500m south, then 300m west towards the evacuation shelter," and sends the result to the user's device.

[0115] The system of the present invention ensures the safety of users and efficiently supports the activities of rescue teams. It provides rapid and accurate information and rescue activities in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue team members.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] Recording user behavior patterns

[0119] Input: User location information (GPS data)

[0120] Output: Behavioral pattern data sent to the server

[0121] The device activates the GPS sensor and acquires current location information every minute. The acquired location information is temporarily stored in local memory and sent to the server at regular intervals (e.g., every hour) using the communication module. Specifically, it constantly records the places the user usually visits, the routes they take, and their location during specific time periods.

[0122] Step 2:

[0123] Saving and analyzing behavioral pattern data

[0124] Input: Behavioral pattern data sent from the device

[0125] Output: User behavioral pattern profile

[0126] The server receives the location data sent from the device and stores it in a database. The stored data is analyzed using machine learning algorithms to learn the user's behavioral patterns. Specifically, a profile is created of the user's usual route and time of day.

[0127] Step 3:

[0128] Receiving disaster information and detecting abnormal situations

[0129] Input: Real-time disaster information, user's current location information

[0130] Output: Anomaly detection flag

[0131] The server uses the disaster information service API to obtain real-time information on earthquakes and typhoons. Based on the obtained disaster information, it compares the user's current location with their behavioral pattern profile to detect abnormalities. Specifically, if the user is in a location that deviates from their normal behavioral pattern, it flags it as an abnormality.

[0132] Step 4:

[0133] Providing evacuation shelter information

[0134] Input: Anomaly detection flag, user's current location information

[0135] Output: Location information of evacuation shelter

[0136] If an abnormality is detected, the server searches for information on the nearest evacuation shelter based on the user's current location and sends that location information to the user's device. Specifically, the server compares the coordinates of the evacuation shelter with the user's location information and calculates the optimal evacuation route.

[0137] Step 5:

[0138] User notification and route guidance

[0139] Input: Location of evacuation shelter

[0140] Output: Notification and route guidance to the user

[0141] The device will then send a notification to the user based on the received evacuation shelter information. The notification will include a map display and route guidance to safely guide the user to the evacuation shelter. Specifically, the device will display the route to the evacuation shelter on its screen and begin providing voice guidance.

[0142] Step 6:

[0143] Collecting victim location information and notifying rescue teams

[0144] Input: Location information of victims around the evacuation center

[0145] Output: Sending victim location information to rescue team terminal

[0146] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and transmits it to the rescue team's dedicated terminal as a map display. Specifically, the location information of multiple disaster victims is plotted on the map.

[0147] Step 7:

[0148] Supporting rescue efforts by rescue teams

[0149] Input: Victim location information

[0150] Output: Victim location and rescue

[0151] The rescue team's dedicated device visualizes the location of the victim and displays it on a map. Furthermore, when the rescue team approaches the victim, the device will increase the volume of its beep to alert them. Specifically, the device will display an alert saying "Victim approaching."

[0152] Step 8:

[0153] Real-time situation analysis and optimal route provision

[0154] Input: Weather information, information from local governments, user's current location information

[0155] Output: Optimal evacuation route

[0156] The server collects meteorological information and information from local governments in real time and analyzes it using generative AI. Based on the analysis results, the optimal evacuation route is displayed on the user's device. Specifically, a prompt is entered to the generative AI model, asking, "Based on current weather information and behavioral patterns, please tell me the optimal route for the user to evacuate." The AI ​​generates the analysis results and sends them to the user's device.

[0157] These steps realize a system that ensures user safety during disasters and supports efficient rescue operations.

[0158] (Application example 1)

[0159] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0160] During a disaster, many people become confused, making it difficult to take swift and safe evacuation action. For this reason, there is a need for a system that can grasp the location information of victims in real time and provide optimal evacuation routes. It is also important to provide information that allows rescue teams to efficiently find and rescue victims. Conventional systems lack the technology to address these challenges.

[0161] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0162] In this invention, the server includes means for recording user behavior patterns, means for acquiring user current location information in the event of a disaster, means for detecting a difference between the acquired current location information and the behavior pattern, means for providing the user with information on the nearest evacuation shelter when a difference is detected, means for notifying the user of an abnormality based on location information that deviates from the behavior pattern, and means for providing an optimal evacuation route based on the evacuation shelter information. This makes it possible to quickly ensure the safety of users in the event of a disaster and efficiently support rescue operations.

[0163] The "means for recording user behavior patterns" refers to a method or device for recording the places that a user regularly visits, the route, location information during a specific time period, and the like.

[0164] The "means for acquiring user's current location information in the event of a disaster" refers to a method or device for collecting user's location information in real time when a disaster occurs.

[0165] The "means for detecting differences between acquired current location information and behavioral patterns" refers to a method or device for comparing the user's current location information with pre-recorded behavioral patterns to detect abnormalities.

[0166] The "means for providing the user with information about the nearest evacuation shelter" refers to a method or device for searching for location information of the nearest evacuation shelter based on the user's current location and notifying the user of the location information.

[0167] "Means for notifying an abnormality based on location information that deviates from a behavioral pattern" refers to a method or device for notifying a user of an abnormality when the user's current location deviates from a pre-recorded behavioral pattern.

[0168] The "means for providing an optimal evacuation route based on information about evacuation shelters" refers to a method or device for calculating and providing an optimal evacuation route for a user based on information about the location of evacuation shelters.

[0169] The "means for displaying the user's location information on a terminal held by a rescue team" refers to a method or device for displaying the user's location information in real time on a terminal used by a rescue team.

[0170] "Means for increasing the volume of the beep when the rescue team's terminal approaches the user" refers to a method or device for increasing the volume of the beep when the rescue team's terminal approaches the user, thereby visually and audibly identifying the user's location.

[0171] "Means for displaying user location information in real time and analyzing it based on disaster information" refers to a method or device for collecting and displaying user location information in real time and further analyzing it based on disaster information.

[0172] "Means for collecting weather information and information from local governments" refers to methods and devices for collecting weather information and information from local governments in real time.

[0173] The "means for encouraging the user to take specific evacuation action based on the analysis results" refers to a method or device for analyzing collected information and instructing the user to take specific evacuation action.

[0174] This invention is a system that effectively manages user location information during disasters and supports rapid evacuation and rescue operations. This system is realized using users' terminals and server-side infrastructure.

[0175] Recording behavioral patterns

[0176] The device records the user's daily behavioral patterns, including the places the user usually visits, the route they take, and their location during specific times. The device periodically acquires GPS data and sends it to a server. The server stores the received data in a database and analyzes and records the user's behavioral patterns.

[0177] Detecting abnormal situations during disasters

[0178] The server obtains disaster information in real time from the disaster information service. For example, earthquake or typhoon warnings may be issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly. In this case, the server notifies the user of the anomaly and instructs them on the next course of action.

[0179] Providing evacuation shelter information

[0180] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[0181] Rescue operation support

[0182] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are required. The server collects location information of victims within a certain range of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the devices held by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated device will increase the volume of its beep, allowing them to identify the person to be rescued and respond quickly.

[0183] Real-time situation analysis and instructions

[0184] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. This information is analyzed using generative AI to calculate the safest and fastest evacuation route. The server then sends optimal evacuation instructions based on the analysis results to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[0185] The hardware used to realize this system includes the user's smartphone or smart glasses, the dedicated terminals of the rescue team, and the server that supports these. The software includes an API for acquiring location information, an API for collecting disaster information, and an analysis engine using a generative AI model. As a concrete example, we present an example of a prompt for designing a system that "calculates the optimal evacuation shelter and evacuation route based on the user's current location information and notifies the user in real time if the user's current location information deviates from their normal behavioral pattern."

[0186] Prompt Sentence Examples

[0187] "If a user's current location information deviates from their normal behavioral patterns, please design a system that calculates the optimal evacuation shelter and evacuation route based on that location information and notifies them in real time."

[0188] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0189] Step 1:

[0190] The server records the user's behavioral patterns. In this process, it receives GPS data periodically sent from the device and stores the data in a database. Specifically, it collects the user's current location, places visited, travel route, and location information during specific time periods. The input is the GPS data from the device, and the output is the behavioral pattern data stored in the database.

[0191] Step 2:

[0192] The server obtains the latest disaster information from the disaster information service. The input is a data request from the disaster information service, and the output is the latest disaster information. Specifically, information such as earthquake and typhoon warnings is obtained via API and updated in real time.

[0193] Step 3:

[0194] The server obtains the user's current location information from the device. The input is the real-time location information sent from the device, and the output is the user's current location data. The server periodically checks the user's current location and stores it in a database.

[0195] Step 4:

[0196] The server compares the acquired current location information with the recorded behavioral patterns to detect anomalies. The input is current location data and behavioral pattern data, and the output is the anomaly detection results. Specifically, a generative AI model is used to analyze the degree of deviation and determine whether the behavior deviates from normal behavioral patterns.

[0197] Step 5:

[0198] If an anomaly is detected, the server searches for the nearest evacuation shelter and provides that information to the user. The input is the user's current location information, and the output is the nearest evacuation shelter information. The server searches a database of evacuation shelters and identifies the nearest evacuation shelter to the user.

[0199] Step 6:

[0200] The server calculates the optimal evacuation route and notifies the user. The input is the user's current location and the location information of the evacuation shelter, and the output is guidance on the optimal evacuation route, allowing the user to reach the shelter safely and quickly.

[0201] Step 7:

[0202] The server notifies the rescue team of the user's current location information obtained in real time and displays it on the rescue team's terminal. The input is the user's location information, and the output is the victim's location information displayed on the rescue team's terminal. The location information is mapped and visually displayed on the rescue team's terminal.

[0203] Step 8:

[0204] The server increases the volume of the beep when the rescue team's device approaches the user. The input is the relative position information between the rescue team and the user, and the output is the volume of the beep. The beep increases when the device approaches within a set range, making it easy to identify the person being rescued.

[0205] Step 9:

[0206] The server collects weather information and information from local governments and analyzes it using a generative AI model. The input is weather information and data from local governments, and the output is the analysis results. Based on the analysis results, the server sends optimal evacuation instructions to the user.

[0207] Step 10:

[0208] The user's device receives the evacuation route and evacuation instructions sent from the server and notifies the user. The input is the evacuation instructions from the server, and the output is the notification to the user. Specifically, the route guidance displayed on the device screen and voice notification help the user take appropriate evacuation actions.

[0209] The above is the processing flow of the system that realizes the application example.

[0210] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0211] This invention is a system for effectively managing a user's location information and emotional state during a disaster, and supporting rapid evacuation and rescue operations. This system is implemented using the user's device and a server-side infrastructure. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it enables appropriate responses according to the user's mental state.

[0212] Recording behavioral patterns

[0213] The device records the user's usual behavioral patterns, including the places the user regularly visits, their route, and their location during specific times of the day. The device periodically (e.g., every minute) acquires GPS data and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[0214] Detecting abnormal situations during disasters

[0215] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake or typhoon warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly.

[0216] Providing evacuation shelter information

[0217] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[0218] Utilizing the Emotion Engine

[0219] The device uses a built-in emotion engine to recognize the user's emotions in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The user's emotion data recognized by the emotion engine is sent to a server. The server analyzes this data and evaluates the user's stress level and psychological state during a disaster.

[0220] Customized evacuation routes

[0221] The server takes into account the user's emotional state to customize the optimal evacuation shelter information and evacuation route. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. It also provides additional information and support to users who need more attention.

[0222] Rescue operation support

[0223] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are essential. The server collects location information of victims within a 2,000m radius of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the dedicated terminals carried by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated terminal will increase the volume of its beep, enabling them to identify the person to be rescued and respond quickly.

[0224] Real-time situation analysis and instructions

[0225] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. Using generative AI, the server calculates the safest and quickest evacuation route. Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[0226] The above is a specific embodiment of the system of the present invention. The present invention effectively manages the location information and emotional state of a user, enabling the provision of quick and accurate information and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue workers.

[0227] The processing flow will be explained below.

[0228] Step 1:

[0229] The device periodically (e.g., every minute) acquires GPS data to monitor the user's daily activities. The device records the user's current latitude, longitude, and timestamp.

[0230] Step 2:

[0231] The device sends the acquired GPS data to the server, which includes latitude, longitude, and a timestamp.

[0232] Step 3:

[0233] The server receives the GPS data sent from the device and stores it in a database. The server organizes the data for each user and learns their behavioral patterns.

[0234] Step 4:

[0235] The device analyzes the user's emotions using a built-in emotion engine, which analyzes the user's facial expressions, voice, and heart rate in real time to recognize their emotional state.

[0236] Step 5:

[0237] The device transmits the recognized emotion data, which includes the user's emotional state and associated biometric data, to the server.

[0238] Step 6:

[0239] When a disaster occurs, the server obtains information from the disaster information service in real time. For example, consider the case where an earthquake or typhoon warning is issued.

[0240] Step 7:

[0241] The server checks the user's current location information. If the acquired current location information deviates from the user's daily behavior pattern, it is detected as an anomaly.

[0242] Step 8:

[0243] When an anomaly is detected, the server searches for the nearest shelter from the user's current location, taking into account the user's emotional state to select the most suitable shelter.

[0244] Step 9:

[0245] The server sends the location information of the selected evacuation shelter to the user's device, and provides customized information on evacuation shelters and evacuation routes according to the user's emotional state.

[0246] Step 10:

[0247] The device then notifies the user of the received evacuation shelter information and guides them to the shelter using map display and route guidance functions.

[0248] Step 11:

[0249] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and displays it on a map, allowing rescue teams to immediately grasp where disaster victims are concentrated.

[0250] Step 12:

[0251] The server transmits the location information of the victims to the dedicated terminal of the rescue team, which visually displays the location information.

[0252] Step 13:

[0253] When the rescue team's dedicated device approaches a victim, it activates a louder beep, allowing the rescue team to quickly pinpoint the victim's exact location.

[0254] Step 14:

[0255] As the situation changes every moment, the server collects weather information and information from local governments and analyzes it in real time using generative AI.

[0256] Step 15:

[0257] The server calculates the safest and quickest evacuation route based on the analysis results and sends the optimal evacuation instructions to the user's device.

[0258] Step 16:

[0259] The device notifies the user of the received evacuation instructions and prompts them to take specific evacuation actions, thereby supporting the user's quick and safe evacuation.

[0260] Example 2

[0261] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0262] Conventional evacuation support systems in the event of a disaster have difficulty in quickly providing evacuation shelter information using smartphones and other devices that users use on a daily basis, and have also been unable to take appropriate measures by taking the user's emotional state into consideration. Furthermore, in rescue operations, it has been difficult to effectively display the location information of victims in real time, making it difficult for rescue teams to quickly identify victims. Therefore, the present invention aims to provide a system that manages the user's location information and emotional state in real time and provides quick and appropriate evacuation support and rescue operations in the event of a disaster.

[0263] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0264] In this invention, the server includes a means for recording the user's behavioral patterns, a means for acquiring the user's current location information in the event of a disaster, and a means for recognizing the user's emotional state and acquiring emotion data. This allows the server to provide the user with optimal evacuation routes and evacuation shelter information based on the acquired location information and emotion data, and also enables rescue teams to grasp the locations of disaster victims in real time.

[0265] "User" refers to a person who uses the evacuation support system in the event of a disaster.

[0266] "Behavioral patterns" represent information such as the user's daily movement history, the places they visit, and the time periods they visit.

[0267] "Current location information" refers to location information such as GPS data that indicates the user's current location.

[0268] "Nearest evacuation shelter information" refers to the location information and related details of the evacuation shelter nearest to the current location.

[0269] "Emotional state" refers to a user's mental condition, such as their psychological state or stress level.

[0270] "Emotional data" refers to psychological information obtained from the user's facial expressions, voice, heart rate, etc.

[0271] A "rescue team" refers to a specialized unit or organization that carries out rescue operations for victims during disasters.

[0272] "Weather information" refers to real-time data on weather and climate.

[0273] "Information from local governments" refers to disaster-related notifications and instructions issued by local governments.

[0274] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate optimal evacuation routes and evacuation instructions.

[0275] This invention is a system that effectively manages a user's location information and emotional state during a disaster, supporting rapid evacuation and rescue operations. This system is implemented using the user's device and a server-side infrastructure. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it enables appropriate responses according to the user's mental state.

[0276] Recording behavioral patterns

[0277] The device records the user's daily behavioral patterns. Specifically, this includes the places the user regularly visits, their route, and their location during specific time periods. The device periodically acquires GPS data (e.g., every minute) and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[0278] Example: If a user leaves home at 8am and arrives at the office at 9am every day, their route and time of day will be recorded.

[0279] Detecting abnormal situations during disasters

[0280] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral pattern, it is detected as an anomaly.

[0281] For example, if you are tens of kilometers away from your home during your normal commute, this will be detected as an anomaly.

[0282] Providing evacuation shelter information

[0283] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[0284] Example: If a user is in a location outside their normal behavioral patterns during a disaster, they will be guided to the nearest evacuation shelter.

[0285] Utilizing the Emotion Engine

[0286] The device uses a built-in emotion engine to recognize the user's emotions in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The recognized emotion data is sent to a server, which analyzes this data and evaluates the user's stress level and psychological state.

[0287] Example: If a user feels very high stress during a disaster, that data is analyzed on the server.

[0288] Customized evacuation routes

[0289] The server takes into account the user's emotional state to customize the best evacuation route and information for them. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. Additional information and support is also provided to users who need more attention.

[0290] Example: If the user is experiencing high stress, provide them with a simpler and more direct escape route than usual.

[0291] Rescue operation support

[0292] In the event of a disaster, the server collects the location information of victims within a 2,000m radius of the evacuation center and displays it on a map. Rescue teams can visually confirm the location of victims using dedicated terminals, and as they approach, the terminals will increase the volume of their beeps, enabling faster rescue.

[0293] Example: When rescuers approach a victim, the device will emit an audible alarm to identify their location.

[0294] Real-time situation analysis and instructions

[0295] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes it in real time. Using a generative AI model, the server calculates the safest and quickest evacuation route. The device notifies the user of the results and encourages specific evacuation actions.

[0296] Example: When a disaster occurs, evacuation routes are recalculated based on new wind direction and precipitation information, and evacuees are guided to the new route.

[0297] Prompt Sentence Examples

[0298] Here are some example prompts to input to a generative AI model:

[0299] "Based on the current weather and road conditions, what is the best evacuation route?"

[0300] "Please suggest a safe and quick way to escape when the user is in a stressful situation."

[0301] "When a disaster occurs, monitor the user's location and emotional state in real time and display a route to the nearest evacuation shelter."

[0302] The above is a specific embodiment of the present invention, which effectively manages a user's location information and emotional state, and realizes prompt and accurate information provision and rescue operations in the event of a disaster, thereby contributing to the early rescue of victims and ensuring the safety of rescue workers.

[0303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0304] Step 1:

[0305] Obtaining location information

[0306] The device periodically (e.g., every minute) acquires GPS data. Specifically, the device records a log message stating "Acquiring current location."

[0307] The input is location data from the device's GPS function, and the output is the acquired current location information (latitude and longitude).

[0308] Step 2:

[0309] Sending data

[0310] The device sends the acquired GPS data to the server. Specifically, the device records a log stating "GPS data has been sent to the server."

[0311] The input is the acquired current location information, and the output is the location information data sent to the server.

[0312] Step 3:

[0313] Data storage

[0314] The server stores the received GPS data in a database. Specifically, the server records a log stating, "GPS data has been stored in the database."

[0315] The input is location information data, and the output is location information data stored in a database.

[0316] Step 4:

[0317] Behavioral pattern analysis

[0318] The server analyzes and learns the user's behavioral patterns based on the saved data. Specifically, the server records a log stating "Behavior pattern analysis completed" and identifies the user's daily route and time of day.

[0319] The input is location data pulled from a database, and the output is a model of analyzed behavioral patterns.

[0320] Step 5:

[0321] Obtaining disaster information

[0322] When a disaster occurs, the server obtains information from the disaster information service in real time. Specifically, the server records a log stating, "An earthquake warning has been received."

[0323] The input is disaster information from the disaster information service, and the output is the acquired disaster information.

[0324] Step 6:

[0325] Check your current location

[0326] The server acquires the user's current location information and compares it with their daily behavioral patterns. Specifically, the server records a log stating, "Current location information confirmed. Outside of normal activity range."

[0327] The input is the current location information stored on the server and the user's behavior pattern data, and the output is the detection result of whether it is normal or abnormal.

[0328] Step 7:

[0329] Anomaly detection

[0330] The server detects any deviation from the normal behavioral pattern as an anomaly, and records a log stating "An anomaly has been detected."

[0331] The input is the result of matching current location information with behavioral patterns, and the output is a flag indicating anomaly detection.

[0332] Step 8:

[0333] Search for evacuation shelters

[0334] The server searches for the nearest evacuation shelter from the user's current location. Specifically, the server records a log stating "The nearest evacuation shelter has been searched."

[0335] The input is the user's current location information, and the output is information about the nearest evacuation shelter.

[0336] Step 9:

[0337] Sending evacuation shelter information

[0338] The server sends the location information of the evacuation shelter to the terminal. Specifically, the server records a log stating "The evacuation shelter information has been sent to the terminal."

[0339] The input is the nearest evacuation shelter information, and the output is the evacuation shelter information sent to the terminal.

[0340] Step 10:

[0341] Notification and Guidance

[0342] The device notifies the user of the received evacuation shelter information and guides them to a safe evacuation location through map display and route guidance functions. Specifically, the device records a log stating, "Evacuation shelter information has been notified and a map has been displayed."

[0343] The input is the evacuation shelter information received from the server, and the output is the evacuation shelter information notified to the user and the displayed map.

[0344] Step 11:

[0345] Recognition of emotional states

[0346] The device uses a built-in emotion engine to recognize the user's emotions in real time, and records a log stating, "The device has recognized the user's stressful situation from their facial expression."

[0347] The input is data such as the user's facial expression, voice, and heart rate, and the output is recognized emotion data.

[0348] Step 12:

[0349] Sending emotional data

[0350] The device sends the recognized emotion data to the server. Specifically, the device records a log stating, "Emotion data has been sent to the server."

[0351] The input is the recognized emotion data, and the output is the emotion data sent to the server.

[0352] Step 13:

[0353] Emotional state analysis

[0354] The server analyzes the received emotion data and evaluates the user's stress level and psychological state. Specifically, the server records a log stating, "Emotion data analyzed. Stress level is high."

[0355] The input is the received emotional data, and the output is the analyzed evaluation results of the user's stress level and psychological state.

[0356] Step 14:

[0357] Selection of the optimal evacuation route

[0358] The server selects the optimal evacuation shelter information and evacuation route, taking into account the user's emotional state. Specifically, the server records a log stating, "The optimal evacuation route has been selected, taking into account the user's stress level."

[0359] The input is the evaluation result of the emotional state, and the output is the selected optimal evacuation route.

[0360] Step 15:

[0361] Providing additional information

[0362] The server will provide additional information or support to the user as needed. Specifically, the server will record a log stating, "Additional shelter information has been provided."

[0363] The input is a request for additional information if necessary, and the output is the additional information provided.

[0364] Step 16:

[0365] Collection of victim location information

[0366] The server collects location information of disaster victims within a 2000m radius of the evacuation center and displays it on a map. Specifically, the server records a log stating, "Victim location information has been displayed on a map."

[0367] The input is the location information of the victims, and the output is a map showing the locations of the victims.

[0368] Step 17:

[0369] Identifying victims

[0370] The dedicated terminal visually displays the victim's location to the rescue team. When the rescue team approaches the victim, the terminal increases the volume of its beep. Specifically, the dedicated terminal records a log stating, "The victim's location has been identified and the beep volume has been increased."

[0371] The input is victim location information, and the output is the identified victim location and an amplified beep.

[0372] Step 18:

[0373] Information collection and analysis

[0374] The server collects weather information and information from local governments and analyzes it in real time. Specifically, the server records a log stating, "The latest weather information has been collected and analyzed."

[0375] The input is meteorological information and local government information, and the output is the latest analyzed situation information.

[0376] Step 19:

[0377] Calculating evacuation routes

[0378] The generative AI model calculates the safest and quickest evacuation route. Specifically, the generative AI model records a log stating, "The optimal evacuation route has been calculated."

[0379] The input is collected and shared disaster information and user emotion data, and the output is the generated optimal evacuation route.

[0380] Step 20:

[0381] Sending evacuation instructions

[0382] Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions. Specifically, the device records a log stating, "Evacuation instructions were received and the user was notified."

[0383] The input is the generated optimal evacuation route, and the output is the evacuation instructions and specific evacuation actions notified to the user.

[0384] (Application example 2)

[0385] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0386] When a disaster occurs, prompt and appropriate evacuation and rescue operations are required, but uniform evacuation instructions that do not take into account users' usual behavioral patterns and emotional states make it difficult to respond appropriately. In particular, there is a risk that evacuation routes may be designated without considering the user's emotional state, or that some evacuees may unknowingly be excluded from rescue efforts. Furthermore, there is a lack of systems that can process large amounts of location information and emotional data in real time to provide optimized evacuation routes and rescue operations. This poses a challenge for improving user safety and achieving prompt and effective disaster response.

[0387] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording the user's behavioral patterns, means for acquiring the user's current location information during a disaster, means for detecting differences between the acquired current location information and the behavioral patterns, means for analyzing the user's emotional state and customizing an evacuation route, and means for visually displaying location information of disaster victims to rescue teams. This makes it possible to provide optimal evacuation routes that take the user's emotional state into consideration in real time, allowing rescue teams to quickly locate disaster victims and realize safe and efficient rescue operations.

[0388] The "behavior pattern recording means" is a function that collects information on the user's daily actions, places visited, route information, etc., and stores it in a database.

[0389] The "current location information acquisition means" is a function that collects GPS information and other data from the user's device in the event of a disaster and sends it to the server.

[0390] The "abnormality detection means" is a function that determines an abnormality when the current location information acquired during a disaster deviates from the normal behavioral patterns.

[0391] The "shelter information providing means" is a function that searches for and notifies the user of the location information of the nearest shelter.

[0392] The "emotional state analysis means" is a function that analyzes the user's emotional state using facial expressions, voice data, heart rate, etc., and evaluates the stress level, etc.

[0393] The "evacuation route customization means" is a function that calculates and provides the optimal evacuation route to the user based on the analysis results of the emotional state.

[0394] The "rescue team display means" is a function that visually displays the location information of victims on the terminals held by rescue teams, thereby supporting rescue operations.

[0395] The "location information notification means" is a function that transmits the user's current location information to a rescue team and carries out rescue operations based on that information.

[0396] "Real-time analysis means" is a function that collects and analyzes the latest weather information and information from local governments according to the disaster situation, and instructs users on the most appropriate evacuation actions.

[0397] A "generative AI model" is an artificial intelligence model that calculates and suggests optimal evacuation routes and rescue operations based on collected data.

[0398] This invention realizes a system that manages users' location information and emotional state during disasters, and supports prompt and optimal evacuation and rescue operations. The system is composed mainly of users' terminals and a server.

[0399] Recording behavioral patterns

[0400] To record the user's usual behavioral patterns, the device periodically acquires location information using a GPS module and sends the data to a server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[0401] Detecting abnormal situations during disasters

[0402] When a disaster occurs, the server obtains information from the disaster information service in real time. When a specific disaster warning is issued, the server checks the user's current location information and detects an anomaly if the obtained current location information deviates from the user's daily behavior pattern.

[0403] Providing evacuation shelter information

[0404] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location and sends the shelter's location information to the user's device. The device then notifies the user of the received evacuation shelter information and uses map display and route guidance functions to guide the user appropriately.

[0405] Utilizing the Emotion Engine

[0406] The device uses a built-in emotion engine to recognize the user's emotional state in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The user's emotional data recognized by the emotion engine is sent to a server, which analyzes this data and evaluates the user's stress level and psychological state during a disaster.

[0407] Customized evacuation routes

[0408] The server customizes the optimal evacuation route and shelter information based on the user's emotional state. For example, if the user is feeling highly stressed, the server selects the quickest and least stressful route. The server uses a generative AI model to calculate the evacuation route and provides that information to the user.

[0409] Rescue operation support

[0410] The server collects location information of victims within a certain distance from the evacuation center and visually displays it on the rescue team's device. This allows the rescue team to immediately grasp the location of the victim. Furthermore, as the rescue team approaches the victim, the device's beep sounds louder, supporting rapid rescue operations.

[0411] Real-time situation analysis and instructions

[0412] As the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes it in real time. The server then uses a generative AI model to analyze the information and calculate the safest and quickest evacuation route. A specific example of a prompt is "Please input location information and emotion analysis data and calculate the optimal evacuation route for the user." This allows the generative AI model to provide an appropriate evacuation route and notify the user.

[0413] The above is a specific embodiment for implementing the present invention. This system effectively manages the user's location information and emotional state, enabling rapid and accurate information provision and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue workers.

[0414] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0415] Step 1:

[0416] Recording user behavior patterns

[0417] The device periodically obtains the user's daily location and route information using a GPS module. The obtained data is sent to a server and stored in a database. The server analyzes the received data to learn the user's behavioral patterns. The input is GPS data, and the output is a record of the behavioral patterns.

[0418] Step 2:

[0419] Obtaining disaster information

[0420] The server retrieves disaster-related data from the disaster information service in real time. The retrieved information includes earthquake, typhoon, and flood warnings. The input is disaster information, and the output is confirmation of the occurrence of a disaster.

[0421] Step 3:

[0422] Comparison of current location information and behavioral patterns

[0423] The server compares the user's current location information obtained from the device when a disaster occurs with their daily behavioral patterns. If an abnormality is detected, a difference is detected. The input is the current location information and behavioral pattern data, and the output is a judgment of whether or not there is an abnormality.

[0424] Step 4:

[0425] Providing evacuation shelter information

[0426] If an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location and sends its location information to the device. The device then notifies the user of the received evacuation shelter information and provides map display and route guidance functions. The input is the current location information and evacuation shelter data, and the output is the evacuation shelter's location information.

[0427] Step 5:

[0428] Emotional state analysis

[0429] The device uses an emotion engine to analyze the user's facial expression, voice, heart rate, etc. in real time to obtain emotional data. The obtained emotional data is sent to the server. The input is the user's facial expression, voice, and heart rate data, and the output is emotional state data.

[0430] Step 6:

[0431] Customized evacuation routes

[0432] The server uses a generative AI model to calculate the optimal evacuation route for the user based on the acquired emotional state data. The prompt used is "Please take location information and emotion analysis data as input and calculate the optimal evacuation route for the user." The input is current location information and emotional state data, and the output is the optimal evacuation route.

[0433] Step 7:

[0434] rescue team support

[0435] The server collects location information of victims within a certain distance from the evacuation shelter and visually displays it on the rescue team's terminal. When the rescue team approaches a victim, the terminal will increase the volume of the beep to help identify the victim. The input is the victim's location information, and the output is a notification to the rescue team's terminal and the beep sound.

[0436] Step 8:

[0437] Real-time situation analysis and guidance

[0438] The server collects weather information and information from local governments in real time and uses a generative AI model to calculate the optimal evacuation route. The collected data is analyzed and specific evacuation instructions are provided to the user. The input is weather information and local government information, and the output is the optimal evacuation route and evacuation instructions.

[0439] 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.

[0440] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0441] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0442] [Second embodiment]

[0443] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0444] 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.

[0445] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0446] 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.

[0447] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0448] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0449] 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.

[0450] 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.

[0451] 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 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.

[0452] 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.

[0453] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0454] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0455] The present invention provides a system that effectively manages user location information during a disaster and supports rapid evacuation and rescue operations. This system is realized using user terminals and a server-side infrastructure.

[0456] Recording behavioral patterns

[0457] The device records the user's usual behavioral patterns, including the places the user regularly visits, their route, and their location during specific times of the day. The device periodically (e.g., every minute) acquires GPS data and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[0458] Detecting abnormal situations during disasters

[0459] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake or typhoon warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly.

[0460] Providing evacuation shelter information

[0461] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[0462] Rescue operation support

[0463] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are essential. The server collects location information of victims within a 2,000m radius of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the dedicated terminals carried by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated terminal will increase the volume of its beep, enabling them to identify the person to be rescued and respond quickly.

[0464] Real-time situation analysis and instructions

[0465] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. Using generative AI, the server calculates the safest and quickest evacuation route. Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[0466] The above is a specific embodiment of the system of the present invention. This system ensures the safety of users and supports the efficient activities of rescue teams. It provides rapid and accurate information and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue team members.

[0467] The processing flow will be explained below.

[0468] Step 1:

[0469] The device periodically (e.g., every minute) acquires GPS data to monitor the user's daily activities. The device records the user's current latitude, longitude, and timestamp.

[0470] Step 2:

[0471] The device sends the acquired GPS data to the server, which includes latitude, longitude, and a timestamp.

[0472] Step 3:

[0473] The server receives the GPS data sent from the device and stores it in a database. The server organizes the data for each user and learns their behavioral patterns.

[0474] Step 4:

[0475] When a disaster occurs, the server obtains information from the disaster information service in real time. For example, let's assume that an earthquake or typhoon warning is issued.

[0476] Step 5:

[0477] The server checks the user's current location information as current GPS data, compares the acquired current location information with daily behavior patterns, and detects abnormalities.

[0478] Step 6:

[0479] If an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location, obtains the location information of the evacuation shelter, and sends it to the user's device.

[0480] Step 7:

[0481] The device then notifies the user of the received evacuation shelter information and guides them to the shelter using map display and route guidance functions.

[0482] Step 8:

[0483] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and displays it on a map, allowing rescue teams to grasp the locations of disaster victims.

[0484] Step 9:

[0485] The server transmits the location information of the victims to the dedicated terminal of the rescue team, which visually displays the location information.

[0486] Step 10:

[0487] When the rescue team's dedicated device approaches a victim, it activates a louder beep, allowing the rescue team to quickly pinpoint the victim's exact location.

[0488] Step 11:

[0489] As the situation changes every moment, the server collects weather information and information from local governments and analyzes it in real time using generative AI.

[0490] Step 12:

[0491] The server calculates the safest and quickest evacuation route based on the analysis results and sends the optimal evacuation instructions to the user's device.

[0492] Step 13:

[0493] The device notifies the user of the received evacuation instructions and prompts them to take specific evacuation actions, thereby supporting the user's quick and safe evacuation.

[0494] Example 1

[0495] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0496] When a disaster occurs, it is extremely important to quickly and accurately grasp users' location information, provide appropriate evacuation instructions, and carry out efficient rescue operations. However, conventional systems lack the ability to record and analyze users' behavioral patterns in detail, which can delay the detection of abnormalities during a disaster and lead to inappropriate subsequent responses. Another issue is the insufficient ability of rescue teams to quickly identify victims and support rescue operations. Furthermore, there is a need for systems that can comprehensively analyze meteorological information collected in real time and information from local governments to provide users with optimal evacuation routes.

[0497] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0498] In this invention, the server includes a means for recording a user's behavioral patterns, a means for acquiring the user's current location information during a disaster, a means for detecting differences between the acquired current location information and the user's behavioral patterns, a means for providing the user with information on the nearest evacuation shelter when a difference is detected, a means for notifying the user of a route to the evacuation shelter, and a means for collecting and displaying location information on other disaster victims within a certain radius of the evacuation shelter. This allows for detailed analysis of the user's location information during a disaster and early detection of abnormalities. Furthermore, by providing evacuation shelter information promptly and supporting efficient rescue efforts for disaster victims, the safety of the user and rescue teams can be ensured. Furthermore, the system can provide optimal evacuation routes using real-time weather information and information from local governments, more effectively supporting the safe evacuation of disaster victims.

[0499] "Behavioral patterns" are data that show the consistency of location information, travel routes, and places visited in a user's daily life.

[0500] "Location information" refers to a user's real-time location data, typically geographic coordinates obtained from a GPS sensor or similar.

[0501] "Anomaly detection" refers to a process of detecting whether the acquired current location information significantly deviates from the user's normal behavior pattern.

[0502] "Evacuation shelter information" refers to location data and access routes to evacuation destinations that are necessary for users to evacuate safely in the event of a disaster.

[0503] "Route notification" is a process that has the function of instructing the user on the route to the nearest evacuation shelter.

[0504] "Victim location information" refers to data collected on the current locations of victims who are within a certain distance of an evacuation shelter and used to support rescue efforts.

[0505] A "rescue team" refers to an organization or group that carries out rescue and assistance activities for victims when a disaster occurs.

[0506] The "evacuation route" is information indicating the optimal route for a user to safely evacuate in the event of a disaster.

[0507] "Generative AI" refers to artificial intelligence models and algorithms that generate optimal results or suggestions based on input data.

[0508] "Weather information" refers to data relating to weather, including real-time weather data such as temperature, humidity, wind speed, and precipitation.

[0509] "Municipal information" refers to official information provided by local governments, such as emergency information, disaster situations, and evacuation instructions.

[0510] The present invention provides a system that effectively manages user location information during a disaster and supports rapid evacuation and rescue operations. This system is realized using user terminals and a server-side infrastructure.

[0511] Recording behavioral patterns

[0512] The device records the user's daily behavioral patterns. Specifically, it activates the GPS sensor and acquires current location information every minute. The acquired location information is temporarily stored in local memory and then periodically sent to the server using the communication module. The server receives the location data sent from the device and stores it in a database. It analyzes the stored data and applies machine learning algorithms to learn the user's behavioral patterns.

[0513] Specific examples

[0514] The device records the user's commute route from home to work every weekday morning at 8:00 a.m., and the data is stored on the server for about a month. The server analyzes this data and learns the user's commute route.

[0515] Detecting abnormal situations

[0516] When a disaster occurs, the server uses the disaster information service API to obtain real-time information about earthquakes and typhoons. When disaster information is received, it compares the user's behavioral patterns with their current location. If the current location significantly deviates from the normal behavioral pattern, it detects this as an anomaly.

[0517] Specific examples

[0518] The server receives typhoon warnings and flags an anomaly if it detects that a user is in an unusual location outside of the workday.

[0519] Providing evacuation shelter information

[0520] When an abnormality is detected, the server searches for information on the nearest evacuation shelter based on the user's current location and sends that location information to the user's device. The device receives the evacuation shelter information and notifies the user. The notification includes a map display and route guidance functions to support safe evacuation.

[0521] Specific examples

[0522] When the user is in an abnormal area, the server calculates the coordinates of the nearest evacuation shelter (e.g., a community center) and sends them to the device. The device receives this information and notifies the user that "there is a shelter 500 meters from here."

[0523] Rescue operation support

[0524] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and transmits it to the rescue team's dedicated terminal as a map display. The rescue team's terminal displays a map visualizing the location information of disaster victims and has a function that increases the volume of a beep when the rescue team approaches.

[0525] Specific examples

[0526] A map of victims around the evacuation shelter is displayed on the rescue team's device, and when they get within a certain distance, an alert sounds saying "Victims approaching."

[0527] Real-time situation analysis and instructions

[0528] The server collects meteorological information and information from local governments in real time and analyzes this information using generative AI. Based on the analysis results, the optimal evacuation route is displayed on the user's device. The device then displays a map and provides voice guidance to encourage appropriate evacuation behavior.

[0529] Specific examples

[0530] Prompt for generative AI model: "Based on current weather information and behavioral patterns, what is the best route for the user to evacuate?"

[0531] The AI ​​analyzes and determines that "it would be best for the user to walk 500m south, then 300m west towards the evacuation shelter," and sends the result to the user's device.

[0532] The system of the present invention ensures the safety of users and efficiently supports the activities of rescue teams. It provides rapid and accurate information and rescue activities in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue team members.

[0533] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0534] Step 1:

[0535] Recording user behavior patterns

[0536] Input: User location information (GPS data)

[0537] Output: Behavioral pattern data sent to the server

[0538] The device activates the GPS sensor and acquires current location information every minute. The acquired location information is temporarily stored in local memory and sent to the server at regular intervals (e.g., every hour) using the communication module. Specifically, it constantly records the places the user usually visits, the routes they take, and their location during specific time periods.

[0539] Step 2:

[0540] Saving and analyzing behavioral pattern data

[0541] Input: Behavioral pattern data sent from the device

[0542] Output: User behavioral pattern profile

[0543] The server receives the location data sent from the device and stores it in a database. The stored data is analyzed using machine learning algorithms to learn the user's behavioral patterns. Specifically, a profile is created of the user's usual route and time of day.

[0544] Step 3:

[0545] Receiving disaster information and detecting abnormal situations

[0546] Input: Real-time disaster information, user's current location information

[0547] Output: Anomaly detection flag

[0548] The server uses the disaster information service API to obtain real-time information on earthquakes and typhoons. Based on the obtained disaster information, it compares the user's current location with their behavioral pattern profile to detect abnormalities. Specifically, if the user is in a location that deviates from their normal behavioral pattern, it flags it as an abnormality.

[0549] Step 4:

[0550] Providing evacuation shelter information

[0551] Input: Anomaly detection flag, user's current location information

[0552] Output: Location information of evacuation shelter

[0553] If an abnormality is detected, the server searches for information on the nearest evacuation shelter based on the user's current location and sends that location information to the user's device. Specifically, the server compares the coordinates of the evacuation shelter with the user's location information and calculates the optimal evacuation route.

[0554] Step 5:

[0555] User notification and route guidance

[0556] Input: Location of evacuation shelter

[0557] Output: Notification and route guidance to the user

[0558] The device will then send a notification to the user based on the received evacuation shelter information. The notification will include a map display and route guidance to safely guide the user to the evacuation shelter. Specifically, the device will display the route to the evacuation shelter on its screen and begin providing voice guidance.

[0559] Step 6:

[0560] Collecting victim location information and notifying rescue teams

[0561] Input: Location information of victims around the evacuation center

[0562] Output: Sending victim location information to rescue team terminal

[0563] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and transmits it to the rescue team's dedicated terminal as a map display. Specifically, the location information of multiple disaster victims is plotted on the map.

[0564] Step 7:

[0565] Supporting rescue efforts by rescue teams

[0566] Input: Victim location information

[0567] Output: Victim location and rescue

[0568] The rescue team's dedicated device visualizes the location of the victim and displays it on a map. Furthermore, when the rescue team approaches the victim, the device will increase the volume of its beep to alert them. Specifically, the device will display an alert saying "Victim approaching."

[0569] Step 8:

[0570] Real-time situation analysis and optimal route provision

[0571] Input: Weather information, information from local governments, user's current location information

[0572] Output: Optimal evacuation route

[0573] The server collects meteorological information and information from local governments in real time and analyzes it using generative AI. Based on the analysis results, the optimal evacuation route is displayed on the user's device. Specifically, a prompt is entered to the generative AI model, asking, "Based on current weather information and behavioral patterns, please tell me the optimal route for the user to evacuate." The AI ​​generates the analysis results and sends them to the user's device.

[0574] These steps realize a system that ensures user safety during disasters and supports efficient rescue operations.

[0575] (Application example 1)

[0576] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0577] During a disaster, many people become confused, making it difficult to take swift and safe evacuation action. For this reason, there is a need for a system that can grasp the location information of victims in real time and provide optimal evacuation routes. It is also important to provide information that allows rescue teams to efficiently find and rescue victims. Conventional systems lack the technology to address these challenges.

[0578] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0579] In this invention, the server includes means for recording user behavior patterns, means for acquiring user current location information in the event of a disaster, means for detecting a difference between the acquired current location information and the behavior pattern, means for providing the user with information on the nearest evacuation shelter when a difference is detected, means for notifying the user of an abnormality based on location information that deviates from the behavior pattern, and means for providing an optimal evacuation route based on the evacuation shelter information. This makes it possible to quickly ensure the safety of users in the event of a disaster and efficiently support rescue operations.

[0580] The "means for recording user behavior patterns" refers to a method or device for recording the places that a user regularly visits, the route, location information during a specific time period, and the like.

[0581] The "means for acquiring user's current location information in the event of a disaster" refers to a method or device for collecting user's location information in real time when a disaster occurs.

[0582] The "means for detecting differences between acquired current location information and behavioral patterns" refers to a method or device for comparing the user's current location information with pre-recorded behavioral patterns to detect abnormalities.

[0583] The "means for providing the user with information about the nearest evacuation shelter" refers to a method or device for searching for location information of the nearest evacuation shelter based on the user's current location and notifying the user of the location information.

[0584] "Means for notifying an abnormality based on location information that deviates from a behavioral pattern" refers to a method or device for notifying a user of an abnormality when the user's current location deviates from a pre-recorded behavioral pattern.

[0585] The "means for providing an optimal evacuation route based on information about evacuation shelters" refers to a method or device for calculating and providing an optimal evacuation route for a user based on information about the location of evacuation shelters.

[0586] The "means for displaying the user's location information on a terminal held by a rescue team" refers to a method or device for displaying the user's location information in real time on a terminal used by a rescue team.

[0587] "Means for increasing the volume of the beep when the rescue team's terminal approaches the user" refers to a method or device for increasing the volume of the beep when the rescue team's terminal approaches the user, thereby visually and audibly identifying the user's location.

[0588] "Means for displaying user location information in real time and analyzing it based on disaster information" refers to a method or device for collecting and displaying user location information in real time and further analyzing it based on disaster information.

[0589] "Means for collecting weather information and information from local governments" refers to methods and devices for collecting weather information and information from local governments in real time.

[0590] The "means for encouraging the user to take specific evacuation action based on the analysis results" refers to a method or device for analyzing collected information and instructing the user to take specific evacuation action.

[0591] This invention is a system that effectively manages user location information during disasters and supports rapid evacuation and rescue operations. This system is realized using users' terminals and server-side infrastructure.

[0592] Recording behavioral patterns

[0593] The device records the user's daily behavioral patterns, including the places the user usually visits, the route they take, and their location during specific times. The device periodically acquires GPS data and sends it to a server. The server stores the received data in a database and analyzes and records the user's behavioral patterns.

[0594] Detecting abnormal situations during disasters

[0595] The server obtains disaster information in real time from the disaster information service. For example, earthquake or typhoon warnings may be issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly. In this case, the server notifies the user of the anomaly and instructs them on the next course of action.

[0596] Providing evacuation shelter information

[0597] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[0598] Rescue operation support

[0599] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are required. The server collects location information of victims within a certain range of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the devices held by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated device will increase the volume of its beep, allowing them to identify the person to be rescued and respond quickly.

[0600] Real-time situation analysis and instructions

[0601] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. This information is analyzed using generative AI to calculate the safest and fastest evacuation route. The server then sends optimal evacuation instructions based on the analysis results to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[0602] The hardware used to realize this system includes the user's smartphone or smart glasses, the dedicated terminals of the rescue team, and the server that supports these. The software includes an API for acquiring location information, an API for collecting disaster information, and an analysis engine using a generative AI model. As a concrete example, we present an example of a prompt for designing a system that "calculates the optimal evacuation shelter and evacuation route based on the user's current location information and notifies the user in real time if the user's current location information deviates from their normal behavioral pattern."

[0603] Prompt Sentence Examples

[0604] "If a user's current location information deviates from their normal behavioral patterns, please design a system that calculates the optimal evacuation shelter and evacuation route based on that location information and notifies them in real time."

[0605] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0606] Step 1:

[0607] The server records the user's behavioral patterns. In this process, it receives GPS data periodically sent from the device and stores the data in a database. Specifically, it collects the user's current location, places visited, travel route, and location information during specific time periods. The input is the GPS data from the device, and the output is the behavioral pattern data stored in the database.

[0608] Step 2:

[0609] The server obtains the latest disaster information from the disaster information service. The input is a data request from the disaster information service, and the output is the latest disaster information. Specifically, information such as earthquake and typhoon warnings is obtained via API and updated in real time.

[0610] Step 3:

[0611] The server obtains the user's current location information from the device. The input is the real-time location information sent from the device, and the output is the user's current location data. The server periodically checks the user's current location and stores it in a database.

[0612] Step 4:

[0613] The server compares the acquired current location information with the recorded behavioral patterns to detect anomalies. The input is current location data and behavioral pattern data, and the output is the anomaly detection results. Specifically, a generative AI model is used to analyze the degree of deviation and determine whether the behavior deviates from normal behavioral patterns.

[0614] Step 5:

[0615] If an anomaly is detected, the server searches for the nearest evacuation shelter and provides that information to the user. The input is the user's current location information, and the output is the nearest evacuation shelter information. The server searches a database of evacuation shelters and identifies the nearest evacuation shelter to the user.

[0616] Step 6:

[0617] The server calculates the optimal evacuation route and notifies the user. The input is the user's current location and the location information of the evacuation shelter, and the output is guidance on the optimal evacuation route, allowing the user to reach the shelter safely and quickly.

[0618] Step 7:

[0619] The server notifies the rescue team of the user's current location information obtained in real time and displays it on the rescue team's terminal. The input is the user's location information, and the output is the victim's location information displayed on the rescue team's terminal. The location information is mapped and visually displayed on the rescue team's terminal.

[0620] Step 8:

[0621] The server increases the volume of the beep when the rescue team's device approaches the user. The input is the relative position information between the rescue team and the user, and the output is the volume of the beep. The beep increases when the device approaches within a set range, making it easy to identify the person being rescued.

[0622] Step 9:

[0623] The server collects weather information and information from local governments and analyzes it using a generative AI model. The input is weather information and data from local governments, and the output is the analysis results. Based on the analysis results, the server sends optimal evacuation instructions to the user.

[0624] Step 10:

[0625] The user's device receives the evacuation route and evacuation instructions sent from the server and notifies the user. The input is the evacuation instructions from the server, and the output is the notification to the user. Specifically, the device supports the user in taking appropriate evacuation actions by displaying route guidance on the device screen and providing voice notifications.

[0626] The above is the processing flow of the system that realizes the application example.

[0627] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0628] This invention is a system for effectively managing a user's location information and emotional state during a disaster, and supporting rapid evacuation and rescue operations. This system is implemented using the user's device and a server-side infrastructure. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it enables appropriate responses according to the user's mental state.

[0629] Recording behavioral patterns

[0630] The device records the user's usual behavioral patterns, including the places the user regularly visits, their route, and their location during specific times of the day. The device periodically (e.g., every minute) acquires GPS data and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[0631] Detecting abnormal situations during disasters

[0632] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake or typhoon warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly.

[0633] Providing evacuation shelter information

[0634] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[0635] Utilizing the Emotion Engine

[0636] The device uses a built-in emotion engine to recognize the user's emotions in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The user's emotion data recognized by the emotion engine is sent to a server. The server analyzes this data and evaluates the user's stress level and psychological state during a disaster.

[0637] Customize evacuation routes

[0638] The server takes into account the user's emotional state to customize the optimal evacuation shelter information and evacuation route. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. It also provides additional information and support to users who need more attention.

[0639] Rescue operation support

[0640] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are essential. The server collects location information of victims within a 2,000m radius of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the dedicated terminals carried by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated terminal will increase the volume of its beep, enabling them to identify the person to be rescued and respond quickly.

[0641] Real-time situation analysis and instructions

[0642] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. Using generative AI, the server calculates the safest and quickest evacuation route. Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[0643] The above is a specific embodiment of the system of the present invention. The present invention effectively manages the location information and emotional state of a user, enabling the provision of quick and accurate information and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue workers.

[0644] The processing flow will be explained below.

[0645] Step 1:

[0646] The device periodically (e.g., every minute) acquires GPS data to monitor the user's daily activities. The device records the user's current latitude, longitude, and timestamp.

[0647] Step 2:

[0648] The device sends the acquired GPS data to the server, which includes latitude, longitude, and a timestamp.

[0649] Step 3:

[0650] The server receives the GPS data sent from the device and stores it in a database. The server organizes the data for each user and learns their behavioral patterns.

[0651] Step 4:

[0652] The device analyzes the user's emotions using a built-in emotion engine, which analyzes the user's facial expressions, voice, and heart rate in real time to recognize their emotional state.

[0653] Step 5:

[0654] The device transmits the recognized emotion data, which includes the user's emotional state and associated biometric data, to the server.

[0655] Step 6:

[0656] When a disaster occurs, the server obtains information from the disaster information service in real time. For example, consider the case where an earthquake or typhoon warning is issued.

[0657] Step 7:

[0658] The server checks the user's current location information. If the acquired current location information deviates from the user's daily behavior pattern, it is detected as an anomaly.

[0659] Step 8:

[0660] When an anomaly is detected, the server searches for the nearest shelter from the user's current location, taking into account the user's emotional state to select the most suitable shelter.

[0661] Step 9:

[0662] The server sends the location information of the selected evacuation shelter to the user's device, and provides customized information on evacuation shelters and evacuation routes according to the user's emotional state.

[0663] Step 10:

[0664] The device then notifies the user of the received evacuation shelter information and guides them to the shelter using map display and route guidance functions.

[0665] Step 11:

[0666] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and displays it on a map, allowing rescue teams to immediately grasp where disaster victims are concentrated.

[0667] Step 12:

[0668] The server transmits the location information of the victims to the dedicated terminal of the rescue team, which visually displays the location information.

[0669] Step 13:

[0670] When the rescue team's dedicated device approaches a victim, it activates a louder beep, allowing the rescue team to quickly pinpoint the victim's exact location.

[0671] Step 14:

[0672] As the situation changes every moment, the server collects weather information and information from local governments and analyzes it in real time using generative AI.

[0673] Step 15:

[0674] The server calculates the safest and quickest evacuation route based on the analysis results and sends the optimal evacuation instructions to the user's device.

[0675] Step 16:

[0676] The device notifies the user of the received evacuation instructions and prompts them to take specific evacuation actions, thereby supporting the user's quick and safe evacuation.

[0677] Example 2

[0678] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0679] Conventional evacuation support systems in the event of a disaster have difficulty in quickly providing evacuation shelter information using smartphones and other devices that users use on a daily basis, and have also been unable to take appropriate measures by taking the user's emotional state into consideration. Furthermore, in rescue operations, it has been difficult to effectively display the location information of victims in real time, making it difficult for rescue teams to quickly identify victims. Therefore, the present invention aims to provide a system that manages the user's location information and emotional state in real time and provides quick and appropriate evacuation support and rescue operations in the event of a disaster.

[0680] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0681] In this invention, the server includes a means for recording the user's behavioral patterns, a means for acquiring the user's current location information in the event of a disaster, and a means for recognizing the user's emotional state and acquiring emotion data. This allows the server to provide the user with optimal evacuation routes and evacuation shelter information based on the acquired location information and emotion data, and also enables rescue teams to grasp the locations of disaster victims in real time.

[0682] "User" refers to a person who uses the evacuation support system in the event of a disaster.

[0683] "Behavioral patterns" represent information such as the user's daily movement history, the places they visit, and the time periods they visit.

[0684] "Current location information" refers to location information such as GPS data that indicates the user's current location.

[0685] "Nearest evacuation shelter information" refers to the location information and related details of the evacuation shelter nearest to the current location.

[0686] "Emotional state" refers to a user's mental condition, such as their psychological state or stress level.

[0687] "Emotional data" refers to psychological information obtained from the user's facial expressions, voice, heart rate, etc.

[0688] A "rescue team" refers to a specialized unit or organization that carries out rescue operations for victims during disasters.

[0689] "Weather information" refers to real-time data on weather and climate.

[0690] "Information from local governments" refers to disaster-related notifications and instructions issued by local governments.

[0691] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate optimal evacuation routes and evacuation instructions.

[0692] This invention is a system that effectively manages a user's location information and emotional state during a disaster, supporting rapid evacuation and rescue operations. This system is implemented using the user's device and a server-side infrastructure. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it enables appropriate responses according to the user's mental state.

[0693] Recording behavioral patterns

[0694] The device records the user's daily behavioral patterns. Specifically, this includes the places the user regularly visits, their route, and their location during specific time periods. The device periodically acquires GPS data (e.g., every minute) and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[0695] Example: If a user leaves home at 8am and arrives at the office at 9am every day, their route and time of day will be recorded.

[0696] Detecting abnormal situations during disasters

[0697] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral pattern, it is detected as an anomaly.

[0698] For example, if you are tens of kilometers away from your home during your normal commute, this will be detected as an anomaly.

[0699] Providing evacuation shelter information

[0700] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[0701] Example: If a user is in a location outside their normal behavioral patterns during a disaster, they will be guided to the nearest evacuation shelter.

[0702] Utilizing the Emotion Engine

[0703] The device uses a built-in emotion engine to recognize the user's emotions in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The recognized emotion data is sent to a server, which analyzes this data and evaluates the user's stress level and psychological state.

[0704] Example: If a user feels very high stress during a disaster, that data is analyzed on the server.

[0705] Customize evacuation routes

[0706] The server takes into account the user's emotional state to customize the best evacuation route and information for them. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. Additional information and support is also provided to users who need more attention.

[0707] Example: If the user is experiencing high stress, provide them with a simpler and more direct escape route than usual.

[0708] Rescue operation support

[0709] In the event of a disaster, the server collects the location information of victims within a 2,000m radius of the evacuation center and displays it on a map. Rescue teams can visually confirm the location of victims using dedicated terminals, and as they approach, the terminals will increase the volume of their beeps, enabling faster rescue.

[0710] Example: When rescuers approach a victim, the device will emit an audible alarm to identify their location.

[0711] Real-time situation analysis and instructions

[0712] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes it in real time. Using a generative AI model, the server calculates the safest and quickest evacuation route. The device notifies the user of the results and encourages specific evacuation actions.

[0713] Example: When a disaster occurs, evacuation routes are recalculated based on new wind direction and precipitation information, and evacuees are guided to the new route.

[0714] Prompt Sentence Examples

[0715] Here are some example prompts to input to a generative AI model:

[0716] "Based on the current weather and road conditions, what is the best evacuation route?"

[0717] "Please suggest a safe and quick way to escape when the user is in a stressful situation."

[0718] "When a disaster occurs, monitor the user's location and emotional state in real time and display a route to the nearest evacuation shelter."

[0719] The above is a specific embodiment of the present invention, which effectively manages a user's location information and emotional state, and realizes prompt and accurate information provision and rescue operations in the event of a disaster, thereby contributing to the early rescue of victims and ensuring the safety of rescue workers.

[0720] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0721] Step 1:

[0722] Obtaining location information

[0723] The device periodically (e.g., every minute) acquires GPS data. Specifically, the device records a log message stating "Acquiring current location."

[0724] The input is location data from the device's GPS function, and the output is the acquired current location information (latitude and longitude).

[0725] Step 2:

[0726] Sending data

[0727] The device sends the acquired GPS data to the server. Specifically, the device records a log stating "GPS data has been sent to the server."

[0728] The input is the acquired current location information, and the output is the location information data sent to the server.

[0729] Step 3:

[0730] Data storage

[0731] The server stores the received GPS data in a database. Specifically, the server records a log stating, "GPS data has been stored in the database."

[0732] The input is location information data, and the output is location information data stored in a database.

[0733] Step 4:

[0734] Behavioral pattern analysis

[0735] The server analyzes and learns the user's behavioral patterns based on the saved data. Specifically, the server records a log stating "Behavior pattern analysis completed" and keeps track of the user's daily route and time of day.

[0736] The input is location data pulled from a database, and the output is a model of analyzed behavioral patterns.

[0737] Step 5:

[0738] Obtaining disaster information

[0739] When a disaster occurs, the server obtains information from the disaster information service in real time. Specifically, the server records a log stating, "An earthquake warning has been received."

[0740] The input is disaster information from the disaster information service, and the output is the acquired disaster information.

[0741] Step 6:

[0742] Check your current location

[0743] The server acquires the user's current location information and compares it with their daily behavioral patterns. Specifically, the server records a log stating, "Current location information confirmed. Outside of normal activity range."

[0744] The input is the current location information stored on the server and the user's behavior pattern data, and the output is the detection result of whether it is normal or abnormal.

[0745] Step 7:

[0746] Anomaly detection

[0747] The server detects any deviation from the normal behavioral pattern as an anomaly, and records a log stating "An anomaly has been detected."

[0748] The input is the result of matching current location information with behavioral patterns, and the output is a flag indicating anomaly detection.

[0749] Step 8:

[0750] Search for evacuation shelters

[0751] The server searches for the nearest evacuation shelter from the user's current location. Specifically, the server records a log stating "The nearest evacuation shelter has been searched."

[0752] The input is the user's current location information, and the output is information about the nearest evacuation shelter.

[0753] Step 9:

[0754] Sending evacuation shelter information

[0755] The server sends the location information of the evacuation shelter to the terminal. Specifically, the server records a log stating "The evacuation shelter information has been sent to the terminal."

[0756] The input is the nearest evacuation shelter information, and the output is the evacuation shelter information sent to the terminal.

[0757] Step 10:

[0758] Notification and Guidance

[0759] The device notifies the user of the received evacuation shelter information and guides them to a safe evacuation location through map display and route guidance functions. Specifically, the device records a log stating, "Evacuation shelter information has been notified and a map has been displayed."

[0760] The input is the evacuation shelter information received from the server, and the output is the evacuation shelter information notified to the user and the displayed map.

[0761] Step 11:

[0762] Recognition of emotional states

[0763] The device uses a built-in emotion engine to recognize the user's emotions in real time, and records a log stating, "The device has recognized the user's stressful situation from their facial expression."

[0764] The input is data such as the user's facial expression, voice, and heart rate, and the output is recognized emotion data.

[0765] Step 12:

[0766] Sending emotional data

[0767] The device sends the recognized emotion data to the server. Specifically, the device records a log stating, "Emotion data has been sent to the server."

[0768] The input is the recognized emotion data, and the output is the emotion data sent to the server.

[0769] Step 13:

[0770] Emotional state analysis

[0771] The server analyzes the received emotion data and evaluates the user's stress level and psychological state. Specifically, the server records a log stating, "Emotion data analyzed. Stress level is high."

[0772] The input is the received emotional data, and the output is the analyzed evaluation results of the user's stress level and psychological state.

[0773] Step 14:

[0774] Selection of the optimal evacuation route

[0775] The server selects the optimal evacuation shelter information and evacuation route, taking into account the user's emotional state. Specifically, the server records a log stating, "The optimal evacuation route has been selected, taking into account the user's stress level."

[0776] The input is the evaluation result of the emotional state, and the output is the selected optimal evacuation route.

[0777] Step 15:

[0778] Providing additional information

[0779] The server will provide additional information or support to the user as needed. Specifically, the server will record a log stating, "Additional shelter information has been provided."

[0780] The input is a request for additional information if necessary, and the output is the additional information provided.

[0781] Step 16:

[0782] Collecting location information of victims

[0783] The server collects location information of disaster victims within a 2000m radius of the evacuation center and displays it on a map. Specifically, the server records a log stating, "Victim location information has been displayed on a map."

[0784] The input is the location information of the victims, and the output is a map showing the locations of the victims.

[0785] Step 17:

[0786] Identifying victims

[0787] The dedicated terminal visually displays the victim's location to the rescue team. When the rescue team approaches the victim, the terminal increases the volume of its beep. Specifically, the dedicated terminal records a log stating, "The victim's location has been identified and the beep volume has been increased."

[0788] The input is victim location information, and the output is the identified victim location and an amplified beep.

[0789] Step 18:

[0790] Information collection and analysis

[0791] The server collects weather information and information from local governments and analyzes it in real time. Specifically, the server records a log stating, "The latest weather information has been collected and analyzed."

[0792] The input is meteorological information and local government information, and the output is the latest analyzed situation information.

[0793] Step 19:

[0794] Calculating evacuation routes

[0795] The generative AI model calculates the safest and quickest evacuation route. Specifically, the generative AI model records a log stating, "The optimal evacuation route has been calculated."

[0796] The input is collected and shared disaster information and user emotion data, and the output is the generated optimal evacuation route.

[0797] Step 20:

[0798] Sending evacuation instructions

[0799] Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions. Specifically, the device records a log stating, "Evacuation instructions were received and the user was notified."

[0800] The input is the generated optimal evacuation route, and the output is the evacuation instructions and specific evacuation actions notified to the user.

[0801] (Application example 2)

[0802] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0803] When a disaster occurs, prompt and appropriate evacuation and rescue operations are required, but uniform evacuation instructions that do not take into account users' usual behavioral patterns and emotional states make it difficult to respond appropriately. In particular, there is a risk that evacuation routes may be designated without considering the user's emotional state, or that some evacuees may unknowingly be excluded from rescue efforts. Furthermore, there is a lack of systems that can process large amounts of location information and emotional data in real time to provide optimized evacuation routes and rescue operations. This poses a challenge for improving user safety and achieving prompt and effective disaster response.

[0804] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording the user's behavioral patterns, means for acquiring the user's current location information during a disaster, means for detecting differences between the acquired current location information and the behavioral patterns, means for analyzing the user's emotional state and customizing an evacuation route, and means for visually displaying location information of disaster victims to rescue teams. This makes it possible to provide an optimal evacuation route that takes the user's emotional state into consideration in real time, allowing rescue teams to quickly locate disaster victims and realize safe and efficient rescue operations.

[0805] The "behavior pattern recording means" is a function that collects information on the user's daily actions, places visited, route information, etc., and stores it in a database.

[0806] The "current location information acquisition means" is a function that collects GPS information and other data from the user's device in the event of a disaster and sends it to the server.

[0807] The "abnormality detection means" is a function that determines that an abnormality has occurred if the current location information acquired during a disaster deviates from the normal behavioral patterns.

[0808] The "shelter information providing means" is a function that searches for and notifies the user of the location information of the nearest shelter.

[0809] The "emotional state analysis means" is a function that analyzes the user's emotional state using facial expressions, voice data, heart rate, etc., and evaluates the stress level, etc.

[0810] The "evacuation route customization means" is a function that calculates and provides the optimal evacuation route to the user based on the analysis results of the emotional state.

[0811] The "rescue team display means" is a function that visually displays the location information of victims on the terminals held by rescue teams, thereby supporting rescue operations.

[0812] The "location information notification means" is a function that transmits the user's current location information to a rescue team and carries out rescue operations based on that information.

[0813] "Real-time analysis means" is a function that collects and analyzes the latest weather information and information from local governments according to the disaster situation, and instructs users on the most appropriate evacuation actions.

[0814] A "generative AI model" is an artificial intelligence model that calculates and suggests optimal evacuation routes and rescue operations based on collected data.

[0815] This invention realizes a system that manages users' location information and emotional state during disasters, and supports prompt and optimal evacuation and rescue operations. The system is composed mainly of users' terminals and a server.

[0816] Recording behavioral patterns

[0817] To record the user's usual behavioral patterns, the device periodically acquires location information using a GPS module and sends the data to a server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[0818] Detecting abnormal situations during disasters

[0819] When a disaster occurs, the server obtains information from the disaster information service in real time. When a specific disaster warning is issued, the server checks the user's current location information and detects an anomaly if the obtained current location information deviates from the user's daily behavior pattern.

[0820] Providing evacuation shelter information

[0821] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location and sends the shelter's location information to the user's device. The device then notifies the user of the received evacuation shelter information and uses map display and route guidance functions to guide the user appropriately.

[0822] Utilizing the Emotion Engine

[0823] The device uses a built-in emotion engine to recognize the user's emotional state in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The user's emotional data recognized by the emotion engine is sent to a server, which analyzes this data and evaluates the user's stress level and psychological state during a disaster.

[0824] Customize evacuation routes

[0825] The server takes into account the user's emotional state to customize the optimal evacuation shelter information and evacuation route. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. The server uses a generative AI model to calculate the evacuation route and provide that information to the user.

[0826] Rescue operation support

[0827] The server collects location information of victims within a certain distance from the evacuation shelter and visually displays it on the rescue team's device. This allows the rescue team to immediately grasp the location of the victim. Furthermore, when the rescue team approaches the victim, the device will increase the volume of its beep, supporting rapid rescue operations.

[0828] Real-time situation analysis and instructions

[0829] As the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes it in real time. The server then uses a generative AI model to analyze the information and calculate the safest and quickest evacuation route. A specific example of a prompt is "Please input location information and emotion analysis data and calculate the optimal evacuation route for the user." This allows the generative AI model to provide an appropriate evacuation route and notify the user.

[0830] The above is a specific embodiment for carrying out the present invention. This system effectively manages the user's location information and emotional state, and enables prompt and accurate information provision and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue workers.

[0831] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0832] Step 1:

[0833] Recording user behavior patterns

[0834] The device periodically obtains the user's daily location and route information using a GPS module. The obtained data is sent to a server and stored in a database. The server analyzes the received data to learn the user's behavioral patterns. The input is GPS data, and the output is a record of the behavioral patterns.

[0835] Step 2:

[0836] Obtaining disaster information

[0837] The server retrieves disaster-related data from the disaster information service in real time. The retrieved information includes earthquake, typhoon, and flood warnings. The input is disaster information, and the output is confirmation of the occurrence of a disaster.

[0838] Step 3:

[0839] Comparison of current location information and behavioral patterns

[0840] The server compares the user's current location information obtained from the device when a disaster occurs with their daily behavioral patterns. If an abnormality is detected, a difference is detected. The input is the current location information and behavioral pattern data, and the output is a judgment of whether or not there is an abnormality.

[0841] Step 4:

[0842] Providing evacuation shelter information

[0843] If an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location and sends its location information to the device. The device then notifies the user of the received evacuation shelter information and provides map display and route guidance functions. The input is the current location information and evacuation shelter data, and the output is the evacuation shelter's location information.

[0844] Step 5:

[0845] Emotional state analysis

[0846] The device uses an emotion engine to analyze the user's facial expression, voice, heart rate, etc. in real time to obtain emotional data. The obtained emotional data is sent to the server. The input is the user's facial expression, voice, and heart rate data, and the output is emotional state data.

[0847] Step 6:

[0848] Customize evacuation routes

[0849] The server uses a generative AI model to calculate the optimal evacuation route for the user based on the acquired emotional state data. At this time, the prompt is "Please take location information and emotion analysis data as input and calculate the optimal evacuation route for the user." The input is current location information and emotional state data, and the output is the optimal evacuation route.

[0850] Step 7:

[0851] rescue team support

[0852] The server collects location information of victims within a certain distance from the evacuation shelter and visually displays it on the rescue team's terminal. When the rescue team approaches a victim, the terminal will increase the volume of the beep to help identify the victim. The input is the victim's location information, and the output is a notification to the rescue team's terminal and the beep sound.

[0853] Step 8:

[0854] Real-time situation analysis and instruction

[0855] The server collects weather information and information from local governments in real time and uses a generative AI model to calculate the optimal evacuation route. The collected data is analyzed and specific evacuation instructions are provided to the user. The input is weather information and local government information, and the output is the optimal evacuation route and evacuation instructions.

[0856] 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.

[0857] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0858] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0859] [Third embodiment]

[0860] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0861] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0862] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0863] 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.

[0864] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0865] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0866] 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.

[0867] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0868] 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 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.

[0869] 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.

[0870] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0871] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0872] The present invention provides a system that effectively manages user location information during a disaster and supports rapid evacuation and rescue operations. This system is realized using user terminals and a server-side infrastructure.

[0873] Recording behavioral patterns

[0874] The device records the user's usual behavioral patterns, including the places the user regularly visits, their route, and their location during specific times of the day. The device periodically (e.g., every minute) acquires GPS data and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[0875] Detecting abnormal situations during disasters

[0876] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake or typhoon warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly.

[0877] Providing evacuation shelter information

[0878] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[0879] Rescue operation support

[0880] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are essential. The server collects location information of victims within a 2,000m radius of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the dedicated terminals carried by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated terminal will increase the volume of its beep, enabling them to identify the person to be rescued and respond quickly.

[0881] Real-time situation analysis and instructions

[0882] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. Using generative AI, the server calculates the safest and quickest evacuation route. Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[0883] The above is a specific embodiment of the system of the present invention. This system ensures the safety of users and supports the efficient activities of rescue teams. It provides rapid and accurate information and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue team members.

[0884] The processing flow will be explained below.

[0885] Step 1:

[0886] The device periodically (e.g., every minute) acquires GPS data to monitor the user's daily activities. The device records the user's current latitude, longitude, and timestamp.

[0887] Step 2:

[0888] The device sends the acquired GPS data to the server, which includes latitude, longitude, and a timestamp.

[0889] Step 3:

[0890] The server receives the GPS data sent from the device and stores it in a database. The server organizes the data for each user and learns their behavioral patterns.

[0891] Step 4:

[0892] When a disaster occurs, the server obtains information from the disaster information service in real time. For example, let's assume that an earthquake or typhoon warning is issued.

[0893] Step 5:

[0894] The server checks the user's current location information as current GPS data, compares the acquired current location information with daily behavior patterns, and detects abnormalities.

[0895] Step 6:

[0896] If an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location, obtains the location information of the evacuation shelter, and sends it to the user's device.

[0897] Step 7:

[0898] The device then notifies the user of the received evacuation shelter information and guides them to the shelter using map display and route guidance functions.

[0899] Step 8:

[0900] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and displays it on a map, allowing rescue teams to grasp the locations of disaster victims.

[0901] Step 9:

[0902] The server transmits the location information of the victims to the dedicated terminal of the rescue team, which visually displays the location information.

[0903] Step 10:

[0904] When the rescue team's dedicated device approaches a victim, it activates a louder beep, allowing the rescue team to quickly pinpoint the victim's exact location.

[0905] Step 11:

[0906] As the situation changes every moment, the server collects weather information and information from local governments and analyzes it in real time using generative AI.

[0907] Step 12:

[0908] The server calculates the safest and quickest evacuation route based on the analysis results and sends the optimal evacuation instructions to the user's device.

[0909] Step 13:

[0910] The device notifies the user of the received evacuation instructions and prompts them to take specific evacuation actions, thereby supporting the user's quick and safe evacuation.

[0911] Example 1

[0912] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0913] When a disaster occurs, it is extremely important to quickly and accurately grasp users' location information, provide appropriate evacuation instructions, and carry out efficient rescue operations. However, conventional systems lack the ability to record and analyze users' behavioral patterns in detail, which can delay the detection of abnormalities during a disaster and lead to inappropriate subsequent responses. Another issue is the insufficient ability of rescue teams to quickly identify victims and support rescue operations. Furthermore, there is a need for systems that can comprehensively analyze meteorological information collected in real time and information from local governments to provide users with optimal evacuation routes.

[0914] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0915] In this invention, the server includes a means for recording a user's behavioral patterns, a means for acquiring the user's current location information during a disaster, a means for detecting differences between the acquired current location information and the user's behavioral patterns, a means for providing the user with information on the nearest evacuation shelter when a difference is detected, a means for notifying the user of a route to the evacuation shelter, and a means for collecting and displaying location information on other disaster victims within a certain radius of the evacuation shelter. This allows for detailed analysis of the user's location information during a disaster and early detection of abnormalities. Furthermore, by providing evacuation shelter information promptly and supporting efficient rescue efforts for disaster victims, the safety of the user and rescue teams can be ensured. Furthermore, the system can provide optimal evacuation routes using real-time weather information and information from local governments, more effectively supporting the safe evacuation of disaster victims.

[0916] "Behavioral patterns" are data that show the consistency of location information, travel routes, and places visited in a user's daily life.

[0917] "Location information" refers to a user's real-time location data, typically geographic coordinates obtained from a GPS sensor or similar.

[0918] "Anomaly detection" refers to a process of detecting whether the acquired current location information significantly deviates from the user's normal behavior pattern.

[0919] "Evacuation shelter information" refers to location data and access routes to evacuation destinations that are necessary for users to evacuate safely in the event of a disaster.

[0920] "Route notification" is a process that has the function of instructing the user on the route to the nearest evacuation shelter.

[0921] "Victim location information" refers to data collected on the current locations of victims who are within a certain distance of an evacuation shelter and used to support rescue efforts.

[0922] A "rescue team" refers to an organization or group that carries out rescue and assistance activities for victims when a disaster occurs.

[0923] The "evacuation route" is information indicating the optimal route for a user to safely evacuate in the event of a disaster.

[0924] "Generative AI" refers to artificial intelligence models and algorithms that generate optimal results or suggestions based on input data.

[0925] "Weather information" refers to data relating to weather, including real-time weather data such as temperature, humidity, wind speed, and precipitation.

[0926] "Municipal information" refers to official information provided by local governments, such as emergency information, disaster situations, and evacuation instructions.

[0927] The present invention provides a system that effectively manages user location information during a disaster and supports rapid evacuation and rescue operations. This system is realized using user terminals and a server-side infrastructure.

[0928] Recording behavioral patterns

[0929] The device records the user's daily behavioral patterns. Specifically, it activates the GPS sensor and acquires current location information every minute. The acquired location information is temporarily stored in local memory and then periodically sent to the server using the communication module. The server receives the location data sent from the device and stores it in a database. It analyzes the stored data and applies machine learning algorithms to learn the user's behavioral patterns.

[0930] Specific examples

[0931] The device records the user's commute route from home to work every weekday morning at 8:00 a.m., and the data is stored on the server for about a month. The server analyzes this data and learns the user's commute route.

[0932] Detecting abnormal situations

[0933] When a disaster occurs, the server uses the disaster information service API to obtain real-time information about earthquakes and typhoons. When disaster information is received, it compares the user's behavioral patterns with their current location. If the current location significantly deviates from the normal behavioral pattern, it detects this as an anomaly.

[0934] Specific examples

[0935] The server receives typhoon warnings and flags an anomaly if it detects that a user is in an unusual location outside of the workday.

[0936] Providing evacuation shelter information

[0937] When an abnormality is detected, the server searches for information on the nearest evacuation shelter based on the user's current location and sends that location information to the user's device. The device receives the evacuation shelter information and notifies the user. The notification includes a map display and route guidance functions to support safe evacuation.

[0938] Specific examples

[0939] When the user is in an abnormal area, the server calculates the coordinates of the nearest evacuation shelter (e.g., a community center) and sends them to the device. The device receives this information and notifies the user that "there is a shelter 500 meters from here."

[0940] Rescue operation support

[0941] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and transmits it to the rescue team's dedicated terminal as a map display. The rescue team's terminal displays a map visualizing the location information of disaster victims and has a function that increases the volume of a beep when the rescue team approaches.

[0942] Specific examples

[0943] A map of victims around the evacuation shelter is displayed on the rescue team's device, and when they get within a certain distance, an alert sounds saying "Victims approaching."

[0944] Real-time situation analysis and instructions

[0945] The server collects meteorological information and information from local governments in real time and analyzes this information using generative AI. Based on the analysis results, the optimal evacuation route is displayed on the user's device. The device then displays a map and provides voice guidance to encourage appropriate evacuation behavior.

[0946] Specific examples

[0947] Prompt for generative AI model: "Based on current weather information and behavioral patterns, what is the best route for the user to evacuate?"

[0948] The AI ​​analyzes and determines that "it would be best for the user to walk 500m south, then 300m west towards the evacuation shelter," and sends the result to the user's device.

[0949] The system of the present invention ensures the safety of users and efficiently supports the activities of rescue teams. It provides rapid and accurate information and rescue activities in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue team members.

[0950] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0951] Step 1:

[0952] Recording user behavior patterns

[0953] Input: User location information (GPS data)

[0954] Output: Behavioral pattern data sent to the server

[0955] The device activates the GPS sensor and acquires current location information every minute. The acquired location information is temporarily stored in local memory and sent to the server at regular intervals (e.g., every hour) using the communication module. Specifically, it constantly records the places the user usually visits, the routes they take, and their location during specific time periods.

[0956] Step 2:

[0957] Saving and analyzing behavioral pattern data

[0958] Input: Behavioral pattern data sent from the device

[0959] Output: User behavioral pattern profile

[0960] The server receives the location data sent from the device and stores it in a database. The stored data is analyzed using machine learning algorithms to learn the user's behavioral patterns. Specifically, a profile is created of the user's usual route and time of day.

[0961] Step 3:

[0962] Receiving disaster information and detecting abnormal situations

[0963] Input: Real-time disaster information, user's current location information

[0964] Output: Anomaly detection flag

[0965] The server uses the disaster information service API to obtain real-time information on earthquakes and typhoons. Based on the obtained disaster information, it compares the user's current location with their behavioral pattern profile to detect abnormalities. Specifically, if the user is in a location that deviates from their normal behavioral pattern, it flags it as an abnormality.

[0966] Step 4:

[0967] Providing evacuation shelter information

[0968] Input: Anomaly detection flag, user's current location information

[0969] Output: Location information of evacuation shelter

[0970] If an abnormality is detected, the server searches for information on the nearest evacuation shelter based on the user's current location and sends that location information to the user's device. Specifically, the server compares the coordinates of the evacuation shelter with the user's location information and calculates the optimal evacuation route.

[0971] Step 5:

[0972] User notification and route guidance

[0973] Input: Location of evacuation shelter

[0974] Output: Notification and route guidance to the user

[0975] The device will then send a notification to the user based on the received evacuation shelter information. The notification will include a map display and route guidance to safely guide the user to the evacuation shelter. Specifically, the device will display the route to the evacuation shelter on its screen and begin providing voice guidance.

[0976] Step 6:

[0977] Collecting victim location information and notifying rescue teams

[0978] Input: Location information of victims around the evacuation center

[0979] Output: Sending victim location information to rescue team terminal

[0980] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and transmits it to the rescue team's dedicated terminal as a map display. Specifically, the location information of multiple disaster victims is plotted on the map.

[0981] Step 7:

[0982] Supporting rescue efforts by rescue teams

[0983] Input: Victim location information

[0984] Output: Victim location and rescue

[0985] The rescue team's dedicated device visualizes the location of the victim and displays it on a map. Furthermore, when the rescue team approaches the victim, the device will increase the volume of its beep to alert them. Specifically, the device will display an alert saying "Victim approaching."

[0986] Step 8:

[0987] Real-time situation analysis and optimal route provision

[0988] Input: Weather information, information from local governments, user's current location information

[0989] Output: Optimal evacuation route

[0990] The server collects meteorological information and information from local governments in real time and analyzes it using generative AI. Based on the analysis results, the optimal evacuation route is displayed on the user's device. Specifically, a prompt is entered to the generative AI model, asking, "Based on current weather information and behavioral patterns, please tell me the optimal route for the user to evacuate." The AI ​​generates the analysis results and sends them to the user's device.

[0991] These steps realize a system that ensures user safety during disasters and supports efficient rescue operations.

[0992] (Application example 1)

[0993] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0994] During a disaster, many people become confused, making it difficult to take swift and safe evacuation action. For this reason, there is a need for a system that can grasp the location information of victims in real time and provide optimal evacuation routes. It is also important to provide information that allows rescue teams to efficiently find and rescue victims. Conventional systems lack the technology to address these challenges.

[0995] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0996] In this invention, the server includes means for recording user behavior patterns, means for acquiring user current location information in the event of a disaster, means for detecting a difference between the acquired current location information and the behavior pattern, means for providing the user with information on the nearest evacuation shelter when a difference is detected, means for notifying the user of an abnormality based on location information that deviates from the behavior pattern, and means for providing an optimal evacuation route based on the evacuation shelter information. This makes it possible to quickly ensure the safety of users in the event of a disaster and efficiently support rescue operations.

[0997] The "means for recording user behavior patterns" refers to a method or device for recording the places that a user regularly visits, the route, location information during a specific time period, and the like.

[0998] The "means for acquiring user's current location information in the event of a disaster" refers to a method or device for collecting user's location information in real time when a disaster occurs.

[0999] The "means for detecting differences between acquired current location information and behavioral patterns" refers to a method or device for comparing the user's current location information with pre-recorded behavioral patterns to detect abnormalities.

[1000] The "means for providing the user with information about the nearest evacuation shelter" refers to a method or device for searching for location information of the nearest evacuation shelter based on the user's current location and notifying the user of the location information.

[1001] "Means for notifying an abnormality based on location information that deviates from a behavioral pattern" refers to a method or device for notifying a user of an abnormality when the user's current location deviates from a pre-recorded behavioral pattern.

[1002] The "means for providing an optimal evacuation route based on information about evacuation shelters" refers to a method or device for calculating and providing an optimal evacuation route for a user based on information about the location of evacuation shelters.

[1003] The "means for displaying the user's location information on a terminal held by a rescue team" refers to a method or device for displaying the user's location information in real time on a terminal used by a rescue team.

[1004] "Means for increasing the volume of the beep when the rescue team's terminal approaches the user" refers to a method or device for increasing the volume of the beep when the rescue team's terminal approaches the user, thereby visually and audibly identifying the user's location.

[1005] "Means for displaying user location information in real time and analyzing it based on disaster information" refers to a method or device for collecting and displaying user location information in real time and further analyzing it based on disaster information.

[1006] "Means for collecting weather information and information from local governments" refers to methods and devices for collecting weather information and information from local governments in real time.

[1007] The "means for encouraging the user to take specific evacuation action based on the analysis results" refers to a method or device for analyzing collected information and instructing the user to take specific evacuation action.

[1008] This invention is a system that effectively manages user location information during disasters and supports rapid evacuation and rescue operations. This system is realized using users' terminals and server-side infrastructure.

[1009] Recording behavioral patterns

[1010] The device records the user's daily behavioral patterns, including the places the user usually visits, the route they take, and their location during specific times. The device periodically acquires GPS data and sends it to a server. The server stores the received data in a database and analyzes and records the user's behavioral patterns.

[1011] Detecting abnormal situations during disasters

[1012] The server obtains disaster information in real time from the disaster information service. For example, earthquake or typhoon warnings may be issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly. In this case, the server notifies the user of the anomaly and instructs them on the next course of action.

[1013] Providing evacuation shelter information

[1014] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[1015] Rescue operation support

[1016] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are required. The server collects location information of victims within a certain range of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the devices held by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated device will increase the volume of its beep, allowing them to identify the person to be rescued and respond quickly.

[1017] Real-time situation analysis and instructions

[1018] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. This information is analyzed using generative AI to calculate the safest and fastest evacuation route. The server then sends optimal evacuation instructions based on the analysis results to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[1019] The hardware used to realize this system includes the user's smartphone or smart glasses, the dedicated terminals of the rescue team, and the server that supports these. The software includes an API for acquiring location information, an API for collecting disaster information, and an analysis engine using a generative AI model. As a concrete example, we present an example of a prompt for designing a system that "calculates the optimal evacuation shelter and evacuation route based on the user's current location information and notifies the user in real time if the user's current location information deviates from their normal behavioral pattern."

[1020] Prompt Sentence Examples

[1021] "If a user's current location information deviates from their normal behavioral patterns, please design a system that calculates the optimal evacuation shelter and evacuation route based on that location information and notifies them in real time."

[1022] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1023] Step 1:

[1024] The server records the user's behavioral patterns. In this process, it receives GPS data periodically sent from the device and stores the data in a database. Specifically, it collects the user's current location, places visited, travel route, and location information during specific time periods. The input is the GPS data from the device, and the output is the behavioral pattern data stored in the database.

[1025] Step 2:

[1026] The server obtains the latest disaster information from the disaster information service. The input is a data request from the disaster information service, and the output is the latest disaster information. Specifically, information such as earthquake and typhoon warnings is obtained via API and updated in real time.

[1027] Step 3:

[1028] The server obtains the user's current location information from the device. The input is the real-time location information sent from the device, and the output is the user's current location data. The server periodically checks the user's current location and stores it in a database.

[1029] Step 4:

[1030] The server compares the acquired current location information with the recorded behavioral patterns to detect anomalies. The input is current location data and behavioral pattern data, and the output is the anomaly detection results. Specifically, a generative AI model is used to analyze the degree of deviation and determine whether the behavior deviates from normal behavioral patterns.

[1031] Step 5:

[1032] If an anomaly is detected, the server searches for the nearest evacuation shelter and provides that information to the user. The input is the user's current location information, and the output is the nearest evacuation shelter information. The server searches a database of evacuation shelters and identifies the nearest evacuation shelter to the user.

[1033] Step 6:

[1034] The server calculates the optimal evacuation route and notifies the user. The input is the user's current location and the location information of the evacuation shelter, and the output is guidance on the optimal evacuation route, allowing the user to reach the shelter safely and quickly.

[1035] Step 7:

[1036] The server notifies the rescue team of the user's current location information obtained in real time and displays it on the rescue team's terminal. The input is the user's location information, and the output is the victim's location information displayed on the rescue team's terminal. The location information is mapped and visually displayed on the rescue team's terminal.

[1037] Step 8:

[1038] The server increases the volume of the beep when the rescue team's device approaches the user. The input is the relative position information between the rescue team and the user, and the output is the volume of the beep. The beep increases when the device approaches within a set range, making it easy to identify the person being rescued.

[1039] Step 9:

[1040] The server collects weather information and information from local governments and analyzes it using a generative AI model. The input is weather information and data from local governments, and the output is the analysis results. Based on the analysis results, the server sends optimal evacuation instructions to the user.

[1041] Step 10:

[1042] The user's device receives the evacuation route and evacuation instructions sent from the server and notifies the user. The input is the evacuation instructions from the server, and the output is the notification to the user. Specifically, the device supports the user in taking appropriate evacuation actions by displaying route guidance on the device screen and providing voice notifications.

[1043] The above is the processing flow of the system that realizes the application example.

[1044] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1045] This invention is a system for effectively managing a user's location information and emotional state during a disaster, and supporting rapid evacuation and rescue operations. This system is implemented using the user's device and a server-side infrastructure. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it enables appropriate responses according to the user's mental state.

[1046] Recording behavioral patterns

[1047] The device records the user's usual behavioral patterns, including the places the user regularly visits, their route, and their location during specific times of the day. The device periodically (e.g., every minute) acquires GPS data and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[1048] Detecting abnormal situations during disasters

[1049] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake or typhoon warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly.

[1050] Providing evacuation shelter information

[1051] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[1052] Utilizing the Emotion Engine

[1053] The device uses a built-in emotion engine to recognize the user's emotions in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The user's emotion data recognized by the emotion engine is sent to a server. The server analyzes this data and evaluates the user's stress level and psychological state during a disaster.

[1054] Customize evacuation routes

[1055] The server takes into account the user's emotional state to customize the optimal evacuation shelter information and evacuation route. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. It also provides additional information and support to users who need more attention.

[1056] Rescue operation support

[1057] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are essential. The server collects location information of victims within a 2,000m radius of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the dedicated terminals carried by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated terminal will increase the volume of its beep, enabling them to identify the person to be rescued and respond quickly.

[1058] Real-time situation analysis and instructions

[1059] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. Using generative AI, the server calculates the safest and quickest evacuation route. Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[1060] The above is a specific embodiment of the system of the present invention. The present invention effectively manages the location information and emotional state of a user, enabling the provision of quick and accurate information and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue workers.

[1061] The processing flow will be explained below.

[1062] Step 1:

[1063] The device periodically (e.g., every minute) acquires GPS data to monitor the user's daily activities. The device records the user's current latitude, longitude, and timestamp.

[1064] Step 2:

[1065] The device sends the acquired GPS data to the server, which includes latitude, longitude, and a timestamp.

[1066] Step 3:

[1067] The server receives the GPS data sent from the device and stores it in a database. The server organizes the data for each user and learns their behavioral patterns.

[1068] Step 4:

[1069] The device analyzes the user's emotions using a built-in emotion engine, which analyzes the user's facial expressions, voice, and heart rate in real time to recognize their emotional state.

[1070] Step 5:

[1071] The device transmits the recognized emotion data, which includes the user's emotional state and associated biometric data, to the server.

[1072] Step 6:

[1073] When a disaster occurs, the server obtains information from the disaster information service in real time. For example, consider the case where an earthquake or typhoon warning is issued.

[1074] Step 7:

[1075] The server checks the user's current location information. If the acquired current location information deviates from the user's daily behavior pattern, it is detected as an anomaly.

[1076] Step 8:

[1077] When an anomaly is detected, the server searches for the nearest shelter from the user's current location, taking into account the user's emotional state to select the most suitable shelter.

[1078] Step 9:

[1079] The server sends the location information of the selected evacuation shelter to the user's device, and provides customized information on evacuation shelters and evacuation routes according to the user's emotional state.

[1080] Step 10:

[1081] The device then notifies the user of the received evacuation shelter information and guides them to the shelter using map display and route guidance functions.

[1082] Step 11:

[1083] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and displays it on a map, allowing rescue teams to immediately grasp where disaster victims are concentrated.

[1084] Step 12:

[1085] The server transmits the location information of the victims to the dedicated terminal of the rescue team, which visually displays the location information.

[1086] Step 13:

[1087] When the rescue team's dedicated device approaches a victim, it activates a louder beep, allowing the rescue team to quickly pinpoint the victim's exact location.

[1088] Step 14:

[1089] As the situation changes every moment, the server collects weather information and information from local governments and analyzes it in real time using generative AI.

[1090] Step 15:

[1091] The server calculates the safest and quickest evacuation route based on the analysis results and sends the optimal evacuation instructions to the user's device.

[1092] Step 16:

[1093] The device notifies the user of the received evacuation instructions and prompts them to take specific evacuation actions, thereby supporting the user's quick and safe evacuation.

[1094] Example 2

[1095] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1096] Conventional evacuation support systems in the event of a disaster have difficulty in quickly providing evacuation shelter information using smartphones and other devices that users use on a daily basis, and have also been unable to take appropriate measures by taking the user's emotional state into consideration. Furthermore, in rescue operations, it has been difficult to effectively display the location information of victims in real time, making it difficult for rescue teams to quickly identify victims. Therefore, the present invention aims to provide a system that manages the user's location information and emotional state in real time and provides quick and appropriate evacuation support and rescue operations in the event of a disaster.

[1097] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1098] In this invention, the server includes a means for recording the user's behavioral patterns, a means for acquiring the user's current location information in the event of a disaster, and a means for recognizing the user's emotional state and acquiring emotion data. This allows the server to provide the user with optimal evacuation routes and evacuation shelter information based on the acquired location information and emotion data, and also enables rescue teams to grasp the locations of disaster victims in real time.

[1099] "User" refers to a person who uses the evacuation support system in the event of a disaster.

[1100] "Behavioral patterns" represent information such as the user's daily movement history, the places they visit, and the time periods they visit.

[1101] "Current location information" refers to location information such as GPS data that indicates the user's current location.

[1102] "Nearest evacuation shelter information" refers to the location information and related details of the evacuation shelter nearest to the current location.

[1103] "Emotional state" refers to a user's mental condition, such as their psychological state or stress level.

[1104] "Emotional data" refers to psychological information obtained from the user's facial expressions, voice, heart rate, etc.

[1105] A "rescue team" refers to a specialized unit or organization that carries out rescue operations for victims during disasters.

[1106] "Weather information" refers to real-time data on weather and climate.

[1107] "Information from local governments" refers to disaster-related notifications and instructions issued by local governments.

[1108] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate optimal evacuation routes and evacuation instructions.

[1109] This invention is a system that effectively manages a user's location information and emotional state during a disaster, supporting rapid evacuation and rescue operations. This system is implemented using the user's device and a server-side infrastructure. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it enables appropriate responses according to the user's mental state.

[1110] Recording behavioral patterns

[1111] The device records the user's daily behavioral patterns. Specifically, this includes the places the user regularly visits, their route, and their location during specific time periods. The device periodically acquires GPS data (e.g., every minute) and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[1112] Example: If a user leaves home at 8am and arrives at the office at 9am every day, their route and time of day will be recorded.

[1113] Detecting abnormal situations during disasters

[1114] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral pattern, it is detected as an anomaly.

[1115] For example, if you are tens of kilometers away from your home during your normal commute, this will be detected as an anomaly.

[1116] Providing evacuation shelter information

[1117] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[1118] Example: If a user is in a location outside their normal behavioral patterns during a disaster, they will be guided to the nearest evacuation shelter.

[1119] Utilizing the Emotion Engine

[1120] The device uses a built-in emotion engine to recognize the user's emotions in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The recognized emotion data is sent to a server, which analyzes this data and evaluates the user's stress level and psychological state.

[1121] Example: If a user feels very high stress during a disaster, that data is analyzed on the server.

[1122] Customize evacuation routes

[1123] The server takes into account the user's emotional state to customize the best evacuation route and information for them. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. Additional information and support is also provided to users who need more attention.

[1124] Example: If the user is experiencing high stress, provide them with a simpler and more direct escape route than usual.

[1125] Rescue operation support

[1126] In the event of a disaster, the server collects the location information of victims within a 2,000m radius of the evacuation center and displays it on a map. Rescue teams can visually confirm the location of victims using dedicated terminals, and as they approach, the terminals will increase the volume of their beeps, enabling faster rescue.

[1127] Example: When rescuers approach a victim, the device will emit an audible alarm to identify their location.

[1128] Real-time situation analysis and instructions

[1129] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes it in real time. Using a generative AI model, the server calculates the safest and quickest evacuation route. The device notifies the user of the results and encourages specific evacuation actions.

[1130] Example: When a disaster occurs, evacuation routes are recalculated based on new wind direction and precipitation information, and evacuees are guided to the new route.

[1131] Prompt Sentence Examples

[1132] Here are some example prompts to input to a generative AI model:

[1133] "Based on the current weather and road conditions, what is the best evacuation route?"

[1134] "Please suggest a safe and quick way to escape when the user is in a stressful situation."

[1135] "When a disaster occurs, monitor the user's location and emotional state in real time and display a route to the nearest evacuation shelter."

[1136] The above is a specific embodiment of the present invention, which effectively manages a user's location information and emotional state, and realizes prompt and accurate information provision and rescue operations in the event of a disaster, thereby contributing to the early rescue of victims and ensuring the safety of rescue workers.

[1137] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1138] Step 1:

[1139] Obtaining location information

[1140] The device periodically (e.g., every minute) acquires GPS data. Specifically, the device records a log message stating "Acquiring current location."

[1141] The input is location data from the device's GPS function, and the output is the acquired current location information (latitude and longitude).

[1142] Step 2:

[1143] Sending data

[1144] The device sends the acquired GPS data to the server. Specifically, the device records a log stating "GPS data has been sent to the server."

[1145] The input is the acquired current location information, and the output is the location information data sent to the server.

[1146] Step 3:

[1147] Data storage

[1148] The server stores the received GPS data in a database. Specifically, the server records a log stating, "GPS data has been stored in the database."

[1149] The input is location information data, and the output is location information data stored in a database.

[1150] Step 4:

[1151] Behavioral pattern analysis

[1152] The server analyzes and learns the user's behavioral patterns based on the saved data. Specifically, the server records a log stating "Behavior pattern analysis completed" and keeps track of the user's daily route and time of day.

[1153] The input is location data pulled from a database, and the output is a model of analyzed behavioral patterns.

[1154] Step 5:

[1155] Obtaining disaster information

[1156] When a disaster occurs, the server obtains information from the disaster information service in real time. Specifically, the server records a log stating, "An earthquake warning has been received."

[1157] The input is disaster information from the disaster information service, and the output is the acquired disaster information.

[1158] Step 6:

[1159] Check your current location

[1160] The server acquires the user's current location information and compares it with their daily behavioral patterns. Specifically, the server records a log stating, "Current location information confirmed. Outside of normal activity range."

[1161] The input is the current location information stored on the server and the user's behavior pattern data, and the output is the detection result of whether it is normal or abnormal.

[1162] Step 7:

[1163] Anomaly detection

[1164] The server detects any deviation from the normal behavioral pattern as an anomaly, and records a log stating "An anomaly has been detected."

[1165] The input is the result of matching current location information with behavioral patterns, and the output is a flag indicating anomaly detection.

[1166] Step 8:

[1167] Search for evacuation shelters

[1168] The server searches for the nearest evacuation shelter from the user's current location. Specifically, the server records a log stating "The nearest evacuation shelter has been searched."

[1169] The input is the user's current location information, and the output is information about the nearest evacuation shelter.

[1170] Step 9:

[1171] Sending evacuation shelter information

[1172] The server sends the location information of the evacuation shelter to the terminal. Specifically, the server records a log stating "The evacuation shelter information has been sent to the terminal."

[1173] The input is the nearest evacuation shelter information, and the output is the evacuation shelter information sent to the terminal.

[1174] Step 10:

[1175] Notification and Guidance

[1176] The device notifies the user of the received evacuation shelter information and guides them to a safe evacuation location through map display and route guidance functions. Specifically, the device records a log stating, "Evacuation shelter information has been notified and a map has been displayed."

[1177] The input is the evacuation shelter information received from the server, and the output is the evacuation shelter information notified to the user and the displayed map.

[1178] Step 11:

[1179] Recognition of emotional states

[1180] The device uses a built-in emotion engine to recognize the user's emotions in real time, and records a log stating, "The device has recognized the user's stressful situation from their facial expression."

[1181] The input is data such as the user's facial expression, voice, and heart rate, and the output is recognized emotion data.

[1182] Step 12:

[1183] Sending emotional data

[1184] The device sends the recognized emotion data to the server. Specifically, the device records a log stating, "Emotion data has been sent to the server."

[1185] The input is the recognized emotion data, and the output is the emotion data sent to the server.

[1186] Step 13:

[1187] Emotional state analysis

[1188] The server analyzes the received emotion data and evaluates the user's stress level and psychological state. Specifically, the server records a log stating, "Emotion data analyzed. Stress level is high."

[1189] The input is the received emotional data, and the output is the analyzed evaluation results of the user's stress level and psychological state.

[1190] Step 14:

[1191] Selection of the optimal evacuation route

[1192] The server selects the optimal evacuation shelter information and evacuation route, taking into account the user's emotional state. Specifically, the server records a log stating, "The optimal evacuation route has been selected, taking into account the user's stress level."

[1193] The input is the evaluation result of the emotional state, and the output is the selected optimal evacuation route.

[1194] Step 15:

[1195] Providing additional information

[1196] The server will provide additional information or support to the user as needed. Specifically, the server will record a log stating, "Additional shelter information has been provided."

[1197] The input is a request for additional information if necessary, and the output is the additional information provided.

[1198] Step 16:

[1199] Collecting location information of victims

[1200] The server collects location information of disaster victims within a 2000m radius of the evacuation center and displays it on a map. Specifically, the server records a log stating, "Victim location information has been displayed on a map."

[1201] The input is the location information of the victims, and the output is a map showing the locations of the victims.

[1202] Step 17:

[1203] Identifying victims

[1204] The dedicated terminal visually displays the victim's location to the rescue team. When the rescue team approaches the victim, the terminal increases the volume of its beep. Specifically, the dedicated terminal records a log stating, "The victim's location has been identified and the beep volume has been increased."

[1205] The input is victim location information, and the output is the identified victim location and an amplified beep.

[1206] Step 18:

[1207] Information collection and analysis

[1208] The server collects weather information and information from local governments and analyzes it in real time. Specifically, the server records a log stating, "The latest weather information has been collected and analyzed."

[1209] The input is meteorological information and local government information, and the output is the latest analyzed situation information.

[1210] Step 19:

[1211] Calculating evacuation routes

[1212] The generative AI model calculates the safest and quickest evacuation route. Specifically, the generative AI model records a log stating, "The optimal evacuation route has been calculated."

[1213] The input is collected and shared disaster information and user emotion data, and the output is the generated optimal evacuation route.

[1214] Step 20:

[1215] Sending evacuation instructions

[1216] Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions. Specifically, the device records a log stating, "Evacuation instructions were received and the user was notified."

[1217] The input is the generated optimal evacuation route, and the output is the evacuation instructions and specific evacuation actions notified to the user.

[1218] (Application example 2)

[1219] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1220] When a disaster occurs, prompt and appropriate evacuation and rescue operations are required, but uniform evacuation instructions that do not take into account users' usual behavioral patterns and emotional states make it difficult to respond appropriately. In particular, there is a risk that evacuation routes may be designated without considering the user's emotional state, or that some evacuees may unknowingly be excluded from rescue efforts. Furthermore, there is a lack of systems that can process large amounts of location information and emotional data in real time to provide optimized evacuation routes and rescue operations. This poses a challenge for improving user safety and achieving prompt and effective disaster response.

[1221] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording the user's behavioral patterns, means for acquiring the user's current location information during a disaster, means for detecting differences between the acquired current location information and the behavioral patterns, means for analyzing the user's emotional state and customizing an evacuation route, and means for visually displaying location information of disaster victims to rescue teams. This makes it possible to provide an optimal evacuation route that takes the user's emotional state into consideration in real time, allowing rescue teams to quickly locate disaster victims and realize safe and efficient rescue operations.

[1222] The "behavior pattern recording means" is a function that collects information on the user's daily actions, places visited, route information, etc., and stores it in a database.

[1223] The "current location information acquisition means" is a function that collects GPS information and other data from the user's device in the event of a disaster and sends it to the server.

[1224] The "abnormality detection means" is a function that determines that an abnormality has occurred if the current location information acquired during a disaster deviates from the normal behavioral patterns.

[1225] The "shelter information providing means" is a function that searches for and notifies the user of the location information of the nearest shelter.

[1226] The "emotional state analysis means" is a function that analyzes the user's emotional state using facial expressions, voice data, heart rate, etc., and evaluates the stress level, etc.

[1227] The "evacuation route customization means" is a function that calculates and provides the optimal evacuation route to the user based on the analysis results of the emotional state.

[1228] The "rescue team display means" is a function that visually displays the location information of victims on the terminals held by rescue teams, thereby supporting rescue operations.

[1229] The "location information notification means" is a function that transmits the user's current location information to a rescue team and carries out rescue operations based on that information.

[1230] "Real-time analysis means" is a function that collects and analyzes the latest weather information and information from local governments according to the disaster situation, and instructs users on the most appropriate evacuation actions.

[1231] A "generative AI model" is an artificial intelligence model that calculates and suggests optimal evacuation routes and rescue operations based on collected data.

[1232] This invention realizes a system that manages users' location information and emotional state during disasters, and supports prompt and optimal evacuation and rescue operations. The system is composed mainly of users' terminals and a server.

[1233] Recording behavioral patterns

[1234] To record the user's usual behavioral patterns, the device periodically acquires location information using a GPS module and sends the data to a server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[1235] Detecting abnormal situations during disasters

[1236] When a disaster occurs, the server obtains information from the disaster information service in real time. When a specific disaster warning is issued, the server checks the user's current location information and detects an anomaly if the obtained current location information deviates from the user's daily behavior pattern.

[1237] Providing evacuation shelter information

[1238] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location and sends the shelter's location information to the user's device. The device then notifies the user of the received evacuation shelter information and uses map display and route guidance functions to guide the user appropriately.

[1239] Utilizing the Emotion Engine

[1240] The device uses a built-in emotion engine to recognize the user's emotional state in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The user's emotional data recognized by the emotion engine is sent to a server, which analyzes this data and evaluates the user's stress level and psychological state during a disaster.

[1241] Customize evacuation routes

[1242] The server takes into account the user's emotional state to customize the optimal evacuation shelter information and evacuation route. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. The server uses a generative AI model to calculate the evacuation route and provide that information to the user.

[1243] Rescue operation support

[1244] The server collects location information of victims within a certain distance from the evacuation shelter and visually displays it on the rescue team's device. This allows the rescue team to immediately grasp the location of the victim. Furthermore, when the rescue team approaches the victim, the device will increase the volume of its beep, supporting rapid rescue operations.

[1245] Real-time situation analysis and instructions

[1246] As the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes it in real time. The server then uses a generative AI model to analyze the information and calculate the safest and quickest evacuation route. A specific example of a prompt is "Please input location information and emotion analysis data and calculate the optimal evacuation route for the user." This allows the generative AI model to provide an appropriate evacuation route and notify the user.

[1247] The above is a specific embodiment for carrying out the present invention. This system effectively manages the user's location information and emotional state, and enables prompt and accurate information provision and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue workers.

[1248] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1249] Step 1:

[1250] Recording user behavior patterns

[1251] The device periodically obtains the user's daily location and route information using a GPS module. The obtained data is sent to a server and stored in a database. The server analyzes the received data to learn the user's behavioral patterns. The input is GPS data, and the output is a record of the behavioral patterns.

[1252] Step 2:

[1253] Obtaining disaster information

[1254] The server retrieves disaster-related data from the disaster information service in real time. The retrieved information includes earthquake, typhoon, and flood warnings. The input is disaster information, and the output is confirmation of the occurrence of a disaster.

[1255] Step 3:

[1256] Comparison of current location information and behavioral patterns

[1257] The server compares the user's current location information obtained from the device when a disaster occurs with their daily behavioral patterns. If an abnormality is detected, a difference is detected. The input is the current location information and behavioral pattern data, and the output is a judgment of whether or not there is an abnormality.

[1258] Step 4:

[1259] Providing evacuation shelter information

[1260] If an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location and sends its location information to the device. The device then notifies the user of the received evacuation shelter information and provides map display and route guidance functions. The input is the current location information and evacuation shelter data, and the output is the evacuation shelter's location information.

[1261] Step 5:

[1262] Emotional state analysis

[1263] The device uses an emotion engine to analyze the user's facial expression, voice, heart rate, etc. in real time to obtain emotional data. The obtained emotional data is sent to the server. The input is the user's facial expression, voice, and heart rate data, and the output is emotional state data.

[1264] Step 6:

[1265] Customize evacuation routes

[1266] The server uses a generative AI model to calculate the optimal evacuation route for the user based on the acquired emotional state data. At this time, the prompt is "Please take location information and emotion analysis data as input and calculate the optimal evacuation route for the user." The input is current location information and emotional state data, and the output is the optimal evacuation route.

[1267] Step 7:

[1268] rescue team support

[1269] The server collects location information of victims within a certain distance from the evacuation shelter and visually displays it on the rescue team's terminal. When the rescue team approaches a victim, the terminal will increase the volume of the beep to help identify the victim. The input is the victim's location information, and the output is a notification to the rescue team's terminal and the beep sound.

[1270] Step 8:

[1271] Real-time situation analysis and instruction

[1272] The server collects weather information and information from local governments in real time and uses a generative AI model to calculate the optimal evacuation route. The collected data is analyzed and specific evacuation instructions are provided to the user. The input is weather information and local government information, and the output is the optimal evacuation route and evacuation instructions.

[1273] 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.

[1274] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1275] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1276] [Fourth embodiment]

[1277] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1278] 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.

[1279] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1280] 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.

[1281] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1282] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1283] 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.

[1284] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1285] 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.

[1286] 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 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.

[1287] 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.

[1288] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1289] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1290] The present invention provides a system that effectively manages user location information during a disaster and supports rapid evacuation and rescue operations. This system is realized using user terminals and a server-side infrastructure.

[1291] Recording behavioral patterns

[1292] The device records the user's usual behavioral patterns, including the places the user regularly visits, their route, and their location during specific times of the day. The device periodically (e.g., every minute) acquires GPS data and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[1293] Detecting abnormal situations during disasters

[1294] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake or typhoon warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly.

[1295] Providing evacuation shelter information

[1296] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[1297] Rescue operation support

[1298] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are essential. The server collects location information of victims within a 2,000m radius of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the dedicated terminals carried by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated terminal will increase the volume of its beep, enabling them to identify the person to be rescued and respond quickly.

[1299] Real-time situation analysis and instructions

[1300] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. Using generative AI, the server calculates the safest and quickest evacuation route. Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[1301] The above is a specific embodiment of the system of the present invention. This system ensures the safety of users and supports the efficient activities of rescue teams. It provides rapid and accurate information and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue team members.

[1302] The processing flow will be explained below.

[1303] Step 1:

[1304] The device periodically (e.g., every minute) acquires GPS data to monitor the user's daily activities. The device records the user's current latitude, longitude, and timestamp.

[1305] Step 2:

[1306] The device sends the acquired GPS data to the server, which includes latitude, longitude, and a timestamp.

[1307] Step 3:

[1308] The server receives the GPS data sent from the device and stores it in a database. The server organizes the data for each user and learns their behavioral patterns.

[1309] Step 4:

[1310] When a disaster occurs, the server obtains information from the disaster information service in real time. For example, let's assume that an earthquake or typhoon warning is issued.

[1311] Step 5:

[1312] The server checks the user's current location information as current GPS data, compares the acquired current location information with daily behavior patterns, and detects abnormalities.

[1313] Step 6:

[1314] If an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location, obtains the location information of the evacuation shelter, and sends it to the user's device.

[1315] Step 7:

[1316] The device then notifies the user of the received evacuation shelter information and guides them to the shelter using map display and route guidance functions.

[1317] Step 8:

[1318] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and displays it on a map, allowing rescue teams to grasp the locations of disaster victims.

[1319] Step 9:

[1320] The server transmits the location information of the victims to the dedicated terminal of the rescue team, which visually displays the location information.

[1321] Step 10:

[1322] When the rescue team's dedicated device approaches a victim, it activates a louder beep, allowing the rescue team to quickly pinpoint the victim's exact location.

[1323] Step 11:

[1324] As the situation changes every moment, the server collects weather information and information from local governments and analyzes it in real time using generative AI.

[1325] Step 12:

[1326] The server calculates the safest and quickest evacuation route based on the analysis results and sends the optimal evacuation instructions to the user's device.

[1327] Step 13:

[1328] The device notifies the user of the received evacuation instructions and prompts them to take specific evacuation actions, thereby supporting the user's quick and safe evacuation.

[1329] Example 1

[1330] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1331] When a disaster occurs, it is extremely important to quickly and accurately grasp users' location information, provide appropriate evacuation instructions, and carry out efficient rescue operations. However, conventional systems lack the ability to record and analyze users' behavioral patterns in detail, which can delay the detection of abnormalities during a disaster and lead to inappropriate subsequent responses. Another issue is the insufficient ability of rescue teams to quickly identify victims and support rescue operations. Furthermore, there is a need for systems that can comprehensively analyze meteorological information collected in real time and information from local governments to provide users with optimal evacuation routes.

[1332] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1333] In this invention, the server includes a means for recording a user's behavioral patterns, a means for acquiring the user's current location information during a disaster, a means for detecting differences between the acquired current location information and the user's behavioral patterns, a means for providing the user with information on the nearest evacuation shelter when a difference is detected, a means for notifying the user of a route to the evacuation shelter, and a means for collecting and displaying location information on other disaster victims within a certain radius of the evacuation shelter. This allows for detailed analysis of the user's location information during a disaster and early detection of abnormalities. Furthermore, by providing evacuation shelter information promptly and supporting efficient rescue efforts for disaster victims, the safety of the user and rescue teams can be ensured. Furthermore, the system can provide optimal evacuation routes using real-time weather information and information from local governments, more effectively supporting the safe evacuation of disaster victims.

[1334] "Behavioral patterns" are data that show the consistency of location information, travel routes, and places visited in a user's daily life.

[1335] "Location information" refers to a user's real-time location data, typically geographic coordinates obtained from a GPS sensor or similar.

[1336] "Anomaly detection" refers to a process of detecting whether the acquired current location information significantly deviates from the user's normal behavior pattern.

[1337] "Evacuation shelter information" refers to location data and access routes to evacuation destinations that are necessary for users to evacuate safely in the event of a disaster.

[1338] "Route notification" is a process that has the function of instructing the user on the route to the nearest evacuation shelter.

[1339] "Victim location information" refers to data collected on the current locations of victims who are within a certain distance of an evacuation shelter and used to support rescue efforts.

[1340] A "rescue team" refers to an organization or group that carries out rescue and assistance activities for victims when a disaster occurs.

[1341] The "evacuation route" is information indicating the optimal route for a user to safely evacuate in the event of a disaster.

[1342] "Generative AI" refers to artificial intelligence models and algorithms that generate optimal results or suggestions based on input data.

[1343] "Weather information" refers to data relating to weather, including real-time weather data such as temperature, humidity, wind speed, and precipitation.

[1344] "Municipal information" refers to official information provided by local governments, such as emergency information, disaster situations, and evacuation instructions.

[1345] The present invention provides a system that effectively manages user location information during a disaster and supports rapid evacuation and rescue operations. This system is realized using user terminals and a server-side infrastructure.

[1346] Recording behavioral patterns

[1347] The device records the user's daily behavioral patterns. Specifically, it activates the GPS sensor and acquires current location information every minute. The acquired location information is temporarily stored in local memory and then periodically sent to the server using the communication module. The server receives the location data sent from the device and stores it in a database. It analyzes the stored data and applies machine learning algorithms to learn the user's behavioral patterns.

[1348] Specific examples

[1349] The device records the user's commute route from home to work every weekday morning at 8:00 a.m., and the data is stored on the server for about a month. The server analyzes this data and learns the user's commute route.

[1350] Detecting abnormal situations

[1351] When a disaster occurs, the server uses the disaster information service API to obtain real-time information about earthquakes and typhoons. When disaster information is received, it compares the user's behavioral patterns with their current location. If the current location significantly deviates from the normal behavioral pattern, it detects this as an anomaly.

[1352] Specific examples

[1353] The server receives typhoon warnings and flags an anomaly if it detects that a user is in an unusual location outside of the workday.

[1354] Providing evacuation shelter information

[1355] When an abnormality is detected, the server searches for information on the nearest evacuation shelter based on the user's current location and sends that location information to the user's device. The device receives the evacuation shelter information and notifies the user. The notification includes a map display and route guidance functions to support safe evacuation.

[1356] Specific examples

[1357] When the user is in an abnormal area, the server calculates the coordinates of the nearest evacuation shelter (e.g., a community center) and sends them to the device. The device receives this information and notifies the user that "there is a shelter 500 meters from here."

[1358] Rescue operation support

[1359] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and transmits it to the rescue team's dedicated terminal as a map display. The rescue team's terminal displays a map visualizing the location information of disaster victims and has a function that increases the volume of a beep when the rescue team approaches.

[1360] Specific examples

[1361] A map of victims around the evacuation shelter is displayed on the rescue team's device, and when they get within a certain distance, an alert sounds saying "Victims approaching."

[1362] Real-time situation analysis and instructions

[1363] The server collects meteorological information and information from local governments in real time and analyzes this information using generative AI. Based on the analysis results, the optimal evacuation route is displayed on the user's device. The device then displays a map and provides voice guidance to encourage appropriate evacuation behavior.

[1364] Specific examples

[1365] Prompt for generative AI model: "Based on current weather information and behavioral patterns, what is the best route for the user to evacuate?"

[1366] The AI ​​analyzes and determines that "it would be best for the user to walk 500m south, then 300m west towards the evacuation shelter," and sends the result to the user's device.

[1367] The system of the present invention ensures the safety of users and efficiently supports the activities of rescue teams. It provides rapid and accurate information and rescue activities in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue team members.

[1368] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1369] Step 1:

[1370] Recording user behavior patterns

[1371] Input: User location information (GPS data)

[1372] Output: Behavioral pattern data sent to the server

[1373] The device activates the GPS sensor and acquires current location information every minute. The acquired location information is temporarily stored in local memory and sent to the server at regular intervals (e.g., every hour) using the communication module. Specifically, it constantly records the places the user usually visits, the routes they take, and their location during specific time periods.

[1374] Step 2:

[1375] Saving and analyzing behavioral pattern data

[1376] Input: Behavioral pattern data sent from the device

[1377] Output: User behavioral pattern profile

[1378] The server receives the location data sent from the device and stores it in a database. The stored data is analyzed using machine learning algorithms to learn the user's behavioral patterns. Specifically, a profile is created of the user's usual route and time of day.

[1379] Step 3:

[1380] Receiving disaster information and detecting abnormal situations

[1381] Input: Real-time disaster information, user's current location information

[1382] Output: Anomaly detection flag

[1383] The server uses the disaster information service API to obtain real-time information on earthquakes and typhoons. Based on the obtained disaster information, it compares the user's current location with their behavioral pattern profile to detect abnormalities. Specifically, if the user is in a location that deviates from their normal behavioral pattern, it flags it as an abnormality.

[1384] Step 4:

[1385] Providing evacuation shelter information

[1386] Input: Anomaly detection flag, user's current location information

[1387] Output: Location information of evacuation shelter

[1388] If an abnormality is detected, the server searches for information on the nearest evacuation shelter based on the user's current location and sends that location information to the user's device. Specifically, the server compares the coordinates of the evacuation shelter with the user's location information and calculates the optimal evacuation route.

[1389] Step 5:

[1390] User notification and route guidance

[1391] Input: Location of evacuation shelter

[1392] Output: Notification and route guidance to the user

[1393] The device will then send a notification to the user based on the received evacuation shelter information. The notification will include a map display and route guidance to safely guide the user to the evacuation shelter. Specifically, the device will display the route to the evacuation shelter on its screen and begin providing voice guidance.

[1394] Step 6:

[1395] Collecting victim location information and notifying rescue teams

[1396] Input: Location information of victims around the evacuation center

[1397] Output: Sending victim location information to rescue team terminal

[1398] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and transmits it to the rescue team's dedicated terminal as a map display. Specifically, the location information of multiple disaster victims is plotted on the map.

[1399] Step 7:

[1400] Supporting rescue efforts by rescue teams

[1401] Input: Victim location information

[1402] Output: Victim location and rescue

[1403] The rescue team's dedicated device visualizes the location of the victim and displays it on a map. Furthermore, when the rescue team approaches the victim, the device will increase the volume of its beep to alert them. Specifically, the device will display an alert saying "Victim approaching."

[1404] Step 8:

[1405] Real-time situation analysis and optimal route provision

[1406] Input: Weather information, information from local governments, user's current location information

[1407] Output: Optimal evacuation route

[1408] The server collects meteorological information and information from local governments in real time and analyzes it using generative AI. Based on the analysis results, the optimal evacuation route is displayed on the user's device. Specifically, a prompt is entered to the generative AI model, asking, "Based on current weather information and behavioral patterns, please tell me the optimal route for the user to evacuate." The AI ​​generates the analysis results and sends them to the user's device.

[1409] These steps realize a system that ensures user safety during disasters and supports efficient rescue operations.

[1410] (Application example 1)

[1411] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1412] During a disaster, many people become confused, making it difficult to take swift and safe evacuation action. For this reason, there is a need for a system that can grasp the location information of victims in real time and provide optimal evacuation routes. It is also important to provide information that allows rescue teams to efficiently find and rescue victims. Conventional systems lack the technology to address these challenges.

[1413] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1414] In this invention, the server includes means for recording user behavior patterns, means for acquiring user current location information in the event of a disaster, means for detecting a difference between the acquired current location information and the behavior pattern, means for providing the user with information on the nearest evacuation shelter when a difference is detected, means for notifying the user of an abnormality based on location information that deviates from the behavior pattern, and means for providing an optimal evacuation route based on the evacuation shelter information. This makes it possible to quickly ensure the safety of users in the event of a disaster and efficiently support rescue operations.

[1415] The "means for recording user behavior patterns" refers to a method or device for recording the places that a user regularly visits, the route, location information during a specific time period, and the like.

[1416] The "means for acquiring user's current location information in the event of a disaster" refers to a method or device for collecting user's location information in real time when a disaster occurs.

[1417] The "means for detecting differences between acquired current location information and behavioral patterns" refers to a method or device for comparing the user's current location information with pre-recorded behavioral patterns to detect abnormalities.

[1418] The "means for providing the user with information about the nearest evacuation shelter" refers to a method or device for searching for location information of the nearest evacuation shelter based on the user's current location and notifying the user of the location information.

[1419] "Means for notifying an abnormality based on location information that deviates from a behavioral pattern" refers to a method or device for notifying a user of an abnormality when the user's current location deviates from a pre-recorded behavioral pattern.

[1420] The "means for providing an optimal evacuation route based on information about evacuation shelters" refers to a method or device for calculating and providing an optimal evacuation route for a user based on information about the location of evacuation shelters.

[1421] The "means for displaying the user's location information on a terminal held by a rescue team" refers to a method or device for displaying the user's location information in real time on a terminal used by a rescue team.

[1422] "Means for increasing the volume of the beep when the rescue team's terminal approaches the user" refers to a method or device for increasing the volume of the beep when the rescue team's terminal approaches the user, thereby visually and audibly identifying the user's location.

[1423] "Means for displaying user location information in real time and analyzing it based on disaster information" refers to a method or device for collecting and displaying user location information in real time and further analyzing it based on disaster information.

[1424] "Means for collecting weather information and information from local governments" refers to methods and devices for collecting weather information and information from local governments in real time.

[1425] The "means for encouraging the user to take specific evacuation action based on the analysis results" refers to a method or device for analyzing collected information and instructing the user to take specific evacuation action.

[1426] This invention is a system that effectively manages user location information during disasters and supports rapid evacuation and rescue operations. This system is realized using users' terminals and server-side infrastructure.

[1427] Recording behavioral patterns

[1428] The device records the user's daily behavioral patterns, including the places the user usually visits, the route they take, and their location during specific times. The device periodically acquires GPS data and sends it to a server. The server stores the received data in a database and analyzes and records the user's behavioral patterns.

[1429] Detecting abnormal situations during disasters

[1430] The server obtains disaster information in real time from the disaster information service. For example, earthquake or typhoon warnings may be issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly. In this case, the server notifies the user of the anomaly and instructs them on the next course of action.

[1431] Providing evacuation shelter information

[1432] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[1433] Rescue operation support

[1434] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are required. The server collects location information of victims within a certain range of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the devices held by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated device will increase the volume of its beep, allowing them to identify the person to be rescued and respond quickly.

[1435] Real-time situation analysis and instructions

[1436] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. This information is analyzed using generative AI to calculate the safest and fastest evacuation route. The server then sends optimal evacuation instructions based on the analysis results to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[1437] The hardware used to realize this system includes the user's smartphone or smart glasses, the dedicated terminals of the rescue team, and the server that supports these. The software includes an API for acquiring location information, an API for collecting disaster information, and an analysis engine using a generative AI model. As a concrete example, we present an example of a prompt for designing a system that "calculates the optimal evacuation shelter and evacuation route based on the user's current location information and notifies the user in real time if the user's current location information deviates from their normal behavioral pattern."

[1438] Prompt Sentence Examples

[1439] "If a user's current location information deviates from their normal behavioral patterns, please design a system that calculates the optimal evacuation shelter and evacuation route based on that location information and notifies them in real time."

[1440] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1441] Step 1:

[1442] The server records the user's behavioral patterns. In this process, it receives GPS data periodically sent from the device and stores the data in a database. Specifically, it collects the user's current location, places visited, travel route, and location information during specific time periods. The input is the GPS data from the device, and the output is the behavioral pattern data stored in the database.

[1443] Step 2:

[1444] The server obtains the latest disaster information from the disaster information service. The input is a data request from the disaster information service, and the output is the latest disaster information. Specifically, information such as earthquake and typhoon warnings is obtained via API and updated in real time.

[1445] Step 3:

[1446] The server obtains the user's current location information from the device. The input is the real-time location information sent from the device, and the output is the user's current location data. The server periodically checks the user's current location and stores it in a database.

[1447] Step 4:

[1448] The server compares the acquired current location information with the recorded behavioral patterns to detect anomalies. The input is current location data and behavioral pattern data, and the output is the anomaly detection results. Specifically, a generative AI model is used to analyze the degree of deviation and determine whether the behavior deviates from normal behavioral patterns.

[1449] Step 5:

[1450] If an anomaly is detected, the server searches for the nearest evacuation shelter and provides that information to the user. The input is the user's current location information, and the output is the nearest evacuation shelter information. The server searches a database of evacuation shelters and identifies the nearest evacuation shelter to the user.

[1451] Step 6:

[1452] The server calculates the optimal evacuation route and notifies the user. The input is the user's current location and the location information of the evacuation shelter, and the output is guidance on the optimal evacuation route, allowing the user to reach the shelter safely and quickly.

[1453] Step 7:

[1454] The server notifies the rescue team of the user's current location information obtained in real time and displays it on the rescue team's terminal. The input is the user's location information, and the output is the victim's location information displayed on the rescue team's terminal. The location information is mapped and visually displayed on the rescue team's terminal.

[1455] Step 8:

[1456] The server increases the volume of the beep when the rescue team's device approaches the user. The input is the relative position information between the rescue team and the user, and the output is the volume of the beep. The beep increases when the device approaches within a set range, making it easy to identify the person being rescued.

[1457] Step 9:

[1458] The server collects weather information and information from local governments and analyzes it using a generative AI model. The input is weather information and data from local governments, and the output is the analysis results. Based on the analysis results, the server sends optimal evacuation instructions to the user.

[1459] Step 10:

[1460] The user's device receives the evacuation route and evacuation instructions sent from the server and notifies the user. The input is the evacuation instructions from the server, and the output is the notification to the user. Specifically, the device supports the user in taking appropriate evacuation actions by displaying route guidance on the device screen and providing voice notifications.

[1461] The above is the processing flow of the system that realizes the application example.

[1462] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1463] This invention is a system for effectively managing a user's location information and emotional state during a disaster, and supporting rapid evacuation and rescue operations. This system is implemented using the user's device and a server-side infrastructure. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it enables appropriate responses according to the user's mental state.

[1464] Recording behavioral patterns

[1465] The device records the user's usual behavioral patterns, including the places the user regularly visits, their route, and their location during specific times of the day. The device periodically (e.g., every minute) acquires GPS data and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[1466] Detecting abnormal situations during disasters

[1467] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake or typhoon warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral patterns, it is detected as an anomaly.

[1468] Providing evacuation shelter information

[1469] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[1470] Utilizing the Emotion Engine

[1471] The device uses a built-in emotion engine to recognize the user's emotions in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The user's emotion data recognized by the emotion engine is sent to a server. The server analyzes this data and evaluates the user's stress level and psychological state during a disaster.

[1472] Customize evacuation routes

[1473] The server takes into account the user's emotional state to customize the optimal evacuation shelter information and evacuation route. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. It also provides additional information and support to users who need more attention.

[1474] Rescue operation support

[1475] In the event of a disaster, many people are required to evacuate, so rapid rescue operations are essential. The server collects location information of victims within a 2,000m radius of the evacuation shelter and displays it on a map. This allows rescue teams to immediately grasp where the victims are concentrated. The location information of the victims is visually displayed on the dedicated terminals carried by the rescue teams. Furthermore, when the rescue team approaches a victim, the dedicated terminal will increase the volume of its beep, enabling them to identify the person to be rescued and respond quickly.

[1476] Real-time situation analysis and instructions

[1477] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes this information in real time. Using generative AI, the server calculates the safest and quickest evacuation route. Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions.

[1478] The above is a specific embodiment of the system of the present invention. The present invention effectively manages the location information and emotional state of a user, enabling the provision of quick and accurate information and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety of rescue workers.

[1479] The processing flow will be explained below.

[1480] Step 1:

[1481] The device periodically (e.g., every minute) acquires GPS data to monitor the user's daily activities. The device records the user's current latitude, longitude, and timestamp.

[1482] Step 2:

[1483] The device sends the acquired GPS data to the server, which includes latitude, longitude, and a timestamp.

[1484] Step 3:

[1485] The server receives the GPS data sent from the device and stores it in a database. The server organizes the data for each user and learns their behavioral patterns.

[1486] Step 4:

[1487] The device analyzes the user's emotions using a built-in emotion engine, which analyzes the user's facial expressions, voice, and heart rate in real time to recognize their emotional state.

[1488] Step 5:

[1489] The device transmits the recognized emotion data, which includes the user's emotional state and associated biometric data, to the server.

[1490] Step 6:

[1491] When a disaster occurs, the server obtains information from the disaster information service in real time. For example, consider the case where an earthquake or typhoon warning is issued.

[1492] Step 7:

[1493] The server checks the user's current location information. If the acquired current location information deviates from the user's daily behavior pattern, it is detected as an anomaly.

[1494] Step 8:

[1495] When an anomaly is detected, the server searches for the nearest shelter from the user's current location, taking into account the user's emotional state to select the most suitable shelter.

[1496] Step 9:

[1497] The server sends the location information of the selected evacuation shelter to the user's device, and provides customized information on evacuation shelters and evacuation routes according to the user's emotional state.

[1498] Step 10:

[1499] The device then notifies the user of the received evacuation shelter information and guides them to the shelter using map display and route guidance functions.

[1500] Step 11:

[1501] The server collects location information of disaster victims within a 2,000m radius of the evacuation center and displays it on a map, allowing rescue teams to immediately grasp where disaster victims are concentrated.

[1502] Step 12:

[1503] The server transmits the location information of the victims to the dedicated terminal of the rescue team, which visually displays the location information.

[1504] Step 13:

[1505] When the rescue team's dedicated device approaches a victim, it activates a louder beep, allowing the rescue team to quickly pinpoint the victim's exact location.

[1506] Step 14:

[1507] As the situation changes every moment, the server collects weather information and information from local governments and analyzes it in real time using generative AI.

[1508] Step 15:

[1509] The server calculates the safest and quickest evacuation route based on the analysis results and sends the optimal evacuation instructions to the user's device.

[1510] Step 16:

[1511] The device notifies the user of the received evacuation instructions and prompts them to take specific evacuation actions, thereby supporting the user's quick and safe evacuation.

[1512] Example 2

[1513] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1514] Conventional evacuation support systems in the event of a disaster have difficulty in quickly providing evacuation shelter information using smartphones and other devices that users use on a daily basis, and have also been unable to take appropriate measures by taking the user's emotional state into consideration. Furthermore, in rescue operations, it has been difficult to effectively display the location information of victims in real time, making it difficult for rescue teams to quickly identify victims. Therefore, the present invention aims to provide a system that manages the user's location information and emotional state in real time and provides quick and appropriate evacuation support and rescue operations in the event of a disaster.

[1515] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1516] In this invention, the server includes a means for recording the user's behavioral patterns, a means for acquiring the user's current location information in the event of a disaster, and a means for recognizing the user's emotional state and acquiring emotion data. This allows the server to provide the user with optimal evacuation routes and evacuation shelter information based on the acquired location information and emotion data, and also enables rescue teams to grasp the locations of disaster victims in real time.

[1517] "User" refers to a person who uses the evacuation support system in the event of a disaster.

[1518] "Behavioral patterns" represent information such as the user's daily movement history, the places they visit, and the time periods they visit.

[1519] "Current location information" refers to location information such as GPS data that indicates the user's current location.

[1520] "Nearest evacuation shelter information" refers to the location information and related details of the evacuation shelter nearest to the current location.

[1521] "Emotional state" refers to a user's mental condition, such as their psychological state or stress level.

[1522] "Emotional data" refers to psychological information obtained from the user's facial expressions, voice, heart rate, etc.

[1523] A "rescue team" refers to a specialized unit or organization that carries out rescue operations for victims during disasters.

[1524] "Weather information" refers to real-time data on weather and climate.

[1525] "Information from local governments" refers to disaster-related notifications and instructions issued by local governments.

[1526] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate optimal evacuation routes and evacuation instructions.

[1527] This invention is a system that effectively manages a user's location information and emotional state during a disaster, supporting rapid evacuation and rescue operations. This system is implemented using the user's device and a server-side infrastructure. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it enables appropriate responses according to the user's mental state.

[1528] Recording behavioral patterns

[1529] The device records the user's daily behavioral patterns. Specifically, this includes the places the user regularly visits, their route, and their location during specific time periods. The device periodically acquires GPS data (e.g., every minute) and sends it to the server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[1530] Example: If a user leaves home at 8am and arrives at the office at 9am every day, their route and time of day will be recorded.

[1531] Detecting abnormal situations during disasters

[1532] When a disaster occurs, the server obtains information in real time from the disaster information service. For example, consider the case where an earthquake warning is issued. Based on this information, the server checks the user's current location information. If the obtained current location information deviates from the user's daily behavioral pattern, it is detected as an anomaly.

[1533] For example, if you are tens of kilometers away from your home during your normal commute, this will be detected as an anomaly.

[1534] Providing evacuation shelter information

[1535] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location. The server acquires the location information of the evacuation shelter and sends it to the user's device. The device then notifies the user of the received evacuation shelter information and guides them to a safe evacuation site using map display and route guidance functions.

[1536] Example: If a user is in a location outside their normal behavioral patterns during a disaster, they will be guided to the nearest evacuation shelter.

[1537] Utilizing the Emotion Engine

[1538] The device uses a built-in emotion engine to recognize the user's emotions in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The recognized emotion data is sent to a server, which analyzes this data and evaluates the user's stress level and psychological state.

[1539] Example: If a user feels very high stress during a disaster, that data is analyzed on the server.

[1540] Customize evacuation routes

[1541] The server takes into account the user's emotional state to customize the best evacuation route and information for them. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. Additional information and support is also provided to users who need more attention.

[1542] Example: If the user is experiencing high stress, provide them with a simpler and more direct escape route than usual.

[1543] Rescue operation support

[1544] In the event of a disaster, the server collects the location information of victims within a 2,000m radius of the evacuation center and displays it on a map. Rescue teams can visually confirm the location of victims using dedicated terminals, and as they approach, the terminals will increase the volume of their beeps, enabling faster rescue.

[1545] Example: When rescuers approach a victim, the device will emit an audible alarm to identify their location.

[1546] Real-time situation analysis and instructions

[1547] Because the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes it in real time. Using a generative AI model, the server calculates the safest and quickest evacuation route. The device notifies the user of the results and encourages specific evacuation actions.

[1548] Example: When a disaster occurs, evacuation routes are recalculated based on new wind direction and precipitation information, and evacuees are guided to the new route.

[1549] Prompt Sentence Examples

[1550] Here are some example prompts to input to a generative AI model:

[1551] "Based on the current weather and road conditions, what is the best evacuation route?"

[1552] "Please suggest a safe and quick way to escape when the user is in a stressful situation."

[1553] "When a disaster occurs, monitor the user's location and emotional state in real time and display a route to the nearest evacuation shelter."

[1554] The above is a specific embodiment of the present invention, which effectively manages a user's location information and emotional state, and realizes prompt and accurate information provision and rescue operations in the event of a disaster, thereby contributing to the early rescue of victims and ensuring the safety of rescue workers.

[1555] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1556] Step 1:

[1557] Obtaining location information

[1558] The device periodically (e.g., every minute) acquires GPS data. Specifically, the device records a log message stating "Acquiring current location."

[1559] The input is location data from the device's GPS function, and the output is the acquired current location information (latitude and longitude).

[1560] Step 2:

[1561] Sending data

[1562] The device sends the acquired GPS data to the server. Specifically, the device records a log stating "GPS data has been sent to the server."

[1563] The input is the acquired current location information, and the output is the location information data sent to the server.

[1564] Step 3:

[1565] Data storage

[1566] The server stores the received GPS data in a database. Specifically, the server records a log stating, "GPS data has been stored in the database."

[1567] The input is location information data, and the output is location information data stored in a database.

[1568] Step 4:

[1569] Behavioral pattern analysis

[1570] The server analyzes and learns the user's behavioral patterns based on the saved data. Specifically, the server records a log stating "Behavior pattern analysis completed" and keeps track of the user's daily route and time of day.

[1571] The input is location data pulled from a database, and the output is a model of analyzed behavioral patterns.

[1572] Step 5:

[1573] Obtaining disaster information

[1574] When a disaster occurs, the server obtains information from the disaster information service in real time. Specifically, the server records a log stating, "An earthquake warning has been received."

[1575] The input is disaster information from the disaster information service, and the output is the acquired disaster information.

[1576] Step 6:

[1577] Check your current location

[1578] The server acquires the user's current location information and compares it with their daily behavioral patterns. Specifically, the server records a log stating, "Current location information confirmed. Outside of normal activity range."

[1579] The input is the current location information stored on the server and the user's behavior pattern data, and the output is the detection result of whether it is normal or abnormal.

[1580] Step 7:

[1581] Anomaly detection

[1582] The server detects any deviation from the normal behavioral pattern as an anomaly, and records a log stating "An anomaly has been detected."

[1583] The input is the result of matching current location information with behavioral patterns, and the output is a flag indicating anomaly detection.

[1584] Step 8:

[1585] Search for evacuation shelters

[1586] The server searches for the nearest evacuation shelter from the user's current location. Specifically, the server records a log stating "The nearest evacuation shelter has been searched."

[1587] The input is the user's current location information, and the output is information about the nearest evacuation shelter.

[1588] Step 9:

[1589] Sending evacuation shelter information

[1590] The server sends the location information of the evacuation shelter to the terminal. Specifically, the server records a log stating "The evacuation shelter information has been sent to the terminal."

[1591] The input is the nearest evacuation shelter information, and the output is the evacuation shelter information sent to the terminal.

[1592] Step 10:

[1593] Notification and Guidance

[1594] The device notifies the user of the received evacuation shelter information and guides them to a safe evacuation location through map display and route guidance functions. Specifically, the device records a log stating, "Evacuation shelter information has been notified and a map has been displayed."

[1595] The input is the evacuation shelter information received from the server, and the output is the evacuation shelter information notified to the user and the displayed map.

[1596] Step 11:

[1597] Recognition of emotional states

[1598] The device uses a built-in emotion engine to recognize the user's emotions in real time, and records a log stating, "The device has recognized the user's stressful situation from their facial expression."

[1599] The input is data such as the user's facial expression, voice, and heart rate, and the output is recognized emotion data.

[1600] Step 12:

[1601] Sending emotional data

[1602] The device sends the recognized emotion data to the server. Specifically, the device records a log stating, "Emotion data has been sent to the server."

[1603] The input is the recognized emotion data, and the output is the emotion data sent to the server.

[1604] Step 13:

[1605] Emotional state analysis

[1606] The server analyzes the received emotion data and evaluates the user's stress level and psychological state. Specifically, the server records a log stating, "Emotion data analyzed. Stress level is high."

[1607] The input is the received emotional data, and the output is the analyzed evaluation results of the user's stress level and psychological state.

[1608] Step 14:

[1609] Selection of the optimal evacuation route

[1610] The server selects the optimal evacuation shelter information and evacuation route, taking into account the user's emotional state. Specifically, the server records a log stating, "The optimal evacuation route has been selected, taking into account the user's stress level."

[1611] The input is the evaluation result of the emotional state, and the output is the selected optimal evacuation route.

[1612] Step 15:

[1613] Providing additional information

[1614] The server will provide additional information or support to the user as needed. Specifically, the server will record a log stating, "Additional shelter information has been provided."

[1615] The input is a request for additional information if necessary, and the output is the additional information provided.

[1616] Step 16:

[1617] Collecting location information of victims

[1618] The server collects location information of disaster victims within a 2000m radius of the evacuation center and displays it on a map. Specifically, the server records a log stating, "Victim location information has been displayed on a map."

[1619] The input is the location information of the victims, and the output is a map showing the locations of the victims.

[1620] Step 17:

[1621] Identifying victims

[1622] The dedicated terminal visually displays the victim's location to the rescue team. When the rescue team approaches the victim, the terminal increases the volume of its beep. Specifically, the dedicated terminal records a log stating, "The victim's location has been identified and the beep volume has been increased."

[1623] The input is victim location information, and the output is the identified victim location and an amplified beep.

[1624] Step 18:

[1625] Information collection and analysis

[1626] The server collects weather information and information from local governments and analyzes it in real time. Specifically, the server records a log stating, "The latest weather information has been collected and analyzed."

[1627] The input is meteorological information and local government information, and the output is the latest analyzed situation information.

[1628] Step 19:

[1629] Calculating evacuation routes

[1630] The generative AI model calculates the safest and quickest evacuation route. Specifically, the generative AI model records a log stating, "The optimal evacuation route has been calculated."

[1631] The input is collected and shared disaster information and user emotion data, and the output is the generated optimal evacuation route.

[1632] Step 20:

[1633] Sending evacuation instructions

[1634] Based on the analysis results, the server sends optimal evacuation instructions to the user's device. The device then notifies the user of this information and encourages them to take specific evacuation actions. Specifically, the device records a log stating, "Evacuation instructions were received and the user was notified."

[1635] The input is the generated optimal evacuation route, and the output is the evacuation instructions and specific evacuation actions notified to the user.

[1636] (Application example 2)

[1637] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1638] When a disaster occurs, prompt and appropriate evacuation and rescue operations are required, but uniform evacuation instructions that do not take into account users' usual behavioral patterns and emotional states make it difficult to respond appropriately. In particular, there is a risk that evacuation routes may be designated without considering the user's emotional state, or that some evacuees may unknowingly be excluded from rescue efforts. Furthermore, there is a lack of systems that can process large amounts of location information and emotional data in real time to provide optimized evacuation routes and rescue operations. This poses a challenge for improving user safety and achieving prompt and effective disaster response.

[1639] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording the user's behavioral patterns, means for acquiring the user's current location information during a disaster, means for detecting differences between the acquired current location information and the behavioral patterns, means for analyzing the user's emotional state and customizing an evacuation route, and means for visually displaying location information of disaster victims to rescue teams. This makes it possible to provide an optimal evacuation route that takes the user's emotional state into consideration in real time, allowing rescue teams to quickly locate disaster victims and realize safe and efficient rescue operations.

[1640] The "behavior pattern recording means" is a function that collects information on the user's daily actions, places visited, route information, etc., and stores it in a database.

[1641] The "current location information acquisition means" is a function that collects GPS information and other data from the user's device in the event of a disaster and sends it to the server.

[1642] The "abnormality detection means" is a function that determines that an abnormality has occurred if the current location information acquired during a disaster deviates from the normal behavioral patterns.

[1643] The "shelter information providing means" is a function that searches for and notifies the user of the location information of the nearest shelter.

[1644] The "emotional state analysis means" is a function that analyzes the user's emotional state using facial expressions, voice data, heart rate, etc., and evaluates the stress level, etc.

[1645] The "evacuation route customization means" is a function that calculates and provides the optimal evacuation route to the user based on the analysis results of the emotional state.

[1646] The "rescue team display means" is a function that visually displays the location information of victims on the terminals held by rescue teams, thereby supporting rescue operations.

[1647] The "location information notification means" is a function that transmits the user's current location information to a rescue team and carries out rescue operations based on that information.

[1648] "Real-time analysis means" is a function that collects and analyzes the latest weather information and information from local governments according to the disaster situation, and instructs users on the most appropriate evacuation actions.

[1649] A "generative AI model" is an artificial intelligence model that calculates and suggests optimal evacuation routes and rescue operations based on collected data.

[1650] This invention realizes a system that manages users' location information and emotional state during disasters, and supports prompt and optimal evacuation and rescue operations. The system is composed mainly of users' terminals and a server.

[1651] Recording behavioral patterns

[1652] To record the user's usual behavioral patterns, the device periodically acquires location information using a GPS module and sends the data to a server. The server stores the received data in a database and analyzes and learns the user's behavioral patterns.

[1653] Detecting abnormal situations during disasters

[1654] When a disaster occurs, the server obtains information from the disaster information service in real time. When a specific disaster warning is issued, the server checks the user's current location information and detects an anomaly if the obtained current location information deviates from the user's daily behavior pattern.

[1655] Providing evacuation shelter information

[1656] When an abnormality is detected, the server searches for the nearest evacuation shelter from the user's current location and sends the shelter's location information to the user's device. The device then notifies the user of the received evacuation shelter information and uses map display and route guidance functions to guide the user appropriately.

[1657] Utilizing the Emotion Engine

[1658] The device uses a built-in emotion engine to recognize the user's emotional state in real time. This is achieved by analyzing the user's facial expressions, voice, heart rate, etc. The user's emotional data recognized by the emotion engine is sent to a server, which analyzes this data and evaluates the user's stress level and psychological state during a disaster.

[1659] Customize evacuation routes

[1660] The server takes into account the user's emotional state to customize the optimal evacuation shelter information and evacuation route. For example, if the user is feeling highly stressed, it will select the quickest and least stressful route. The server uses a generative AI model to calculate the evacuation route and provide that information to the user.

[1661] Rescue operation support

[1662] The server collects location information of victims within a certain distance from the evacuation shelter and visually displays it on the rescue team's device. This allows the rescue team to immediately grasp the location of the victim. Furthermore, when the rescue team approaches the victim, the device will increase the volume of its beep, supporting rapid rescue operations.

[1663] Real-time situation analysis and instructions

[1664] As the situation during a disaster changes constantly, the server collects weather information and information from local governments and analyzes it in real time. The server then uses a generative AI model to analyze the information and calculate the safest and quickest evacuation route. A specific example of a prompt is "Please input location information and emotion analysis data and calculate the optimal evacuation route for the user." This allows the generative AI model to provide an appropriate evacuation route and notify the user.

[1665] The above is a specific embodiment for carrying out the present invention. This system effectively manages the user's location information and emotional state, and enables prompt and accurate information provision and rescue operations in the event of a disaster, contributing to the early rescue of victims and ensuring the safety o...

Claims

1. means for recording user behavior patterns; A means for acquiring user's current location information in the event of a disaster; A means for detecting a difference between the acquired current location information and a behavioral pattern; means for providing the user with information about the nearest evacuation shelter when a difference is detected; A system including:

2. means for notifying a rescue team of the user's current location; a means for displaying the user's location information on a terminal carried by a rescue team; means for increasing the volume of the beep when the rescue team terminal approaches the user; The system of claim 1 further comprising:

3. A means of collecting weather information and information from local governments, A means of analyzing the collected information and providing users with the optimal evacuation route; The system of claim 1 further comprising:

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