System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving an image captured by a user; means for analyzing the image to evaluate furniture placement and seismic risk; means for recommending a seismic countermeasure based on the evaluation; and means for notifying the user of the recommended seismic countermeasure.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] Japan is a region prone to frequent natural disasters, with damage caused by earthquakes and heavy rains being particularly severe. Furthermore, when a disaster occurs, appropriate information and countermeasures are often lacking, resulting in insufficient evacuation and safety measures for residents. For this reason, an integrated disaster prevention system is needed to reduce disaster risk and ensure rapid response. However, few conventional disaster prevention systems offer multiple integrated functions, requiring users to implement countermeasures individually. Therefore, the objective of this invention is to integrate four main functions—earthquake-resistant furniture placement, provision of hazard maps, real-time disaster prediction and evacuation instructions, and automatic safety confirmation—to support users in responding appropriately to disasters and contribute to the protection of lives. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving images taken by a user, analyzing the images, and evaluating furniture placement and earthquake risk, and a means for recommending earthquake-resistance measures based on the evaluation results and notifying the user of the recommendations. The system also includes a means for receiving the user's location information, generating hazard maps and evacuation maps based on the location, and providing these to the user. The system further includes a means for collecting meteorological data and damage information in real time, analyzing the data, and predicting the occurrence of a disaster, generating evacuation instructions based on the prediction results, and notifying the user of these instructions. Finally, the system includes a means for receiving biometric data from a smart device, analyzing the data, evaluating the user's safety, generating safety information based on the evaluation results, and notifying other designated users. This system, integrating these four main functions, is designed to enable users to respond quickly and appropriately in the event of a disaster.

[0006] "Users" refer to individuals and organizations that use this system, and are entities that receive information that enables them to take appropriate measures in the event of a disaster.

[0007] "Means for receiving" refers to the functions and methods for incorporating data provided by users (e.g., images, location information, biometric data, etc.) into the system.

[0008] "Means for analysis" refers to the functions or methods for analyzing received data and drawing conclusions or judgments based on a specific purpose (e.g., furniture placement, earthquake risk assessment, or weather data analysis).

[0009] "Means for evaluation" refers to a function or method for evaluating an object (for example, furniture or the safety of a user) based on certain criteria, using the results of the analysis.

[0010] "Means of notification" refers to the methods or functions for notifying users of the results of the analysis or evaluation, and includes, for example, in-app notifications, emails, push notifications, etc.

[0011] "Location information" refers to data that indicates a user's current location, and is information that is primarily obtained by a GPS device.

[0012] A "hazard map" is a map that visually shows areas at risk of natural disasters, providing users with information to guide them in taking safe actions.

[0013] An "evacuation map" is a map that shows routes and evacuation locations for users to safely evacuate in the event of a disaster.

[0014] "Weather data" refers to information about weather (e.g., temperature, precipitation, wind speed, etc.), often collected in real time.

[0015] "Disaster information" refers to data that indicates the extent of damage when a disaster occurs, and is information used to respond to disasters.

[0016] "Biometric data" refers to data that indicates the user's physical condition, including heart rate, body temperature, blood pressure, etc.

[0017] "Safety" refers to the status of the user indicating whether they are safe or not in the event of a disaster, and is determined based on biometric data and the like. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention is a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, real-time disaster predictions and evacuation instructions, and automatic safety confirmation during disasters. This system transmits and receives data between a server, a terminal, and users, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[0040] Analysis of furniture layout and earthquake resistance measures

[0041] This function begins when the user uploads photos of their home from their device to the server. The server then passes the photos to an AI module, which analyzes furniture placement and the risk of furniture tipping over. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and helps them take the necessary measures.

[0042] Examples:

[0043] A user uploads a photo of their living room from their device to the server. The server uses AI to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[0044] Hazard and evacuation map information mapping

[0045] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[0046] Examples:

[0047] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[0048] Disaster prediction and evacuation instructions based on real-time data

[0049] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[0050] Examples:

[0051] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[0052] Automatic safety confirmation

[0053] With this function, the terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows the user's safety to be quickly confirmed.

[0054] Examples:

[0055] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[0056] As described above, the system of the present invention provides an integrated set of functions to help users respond quickly and appropriately in the event of a disaster, thereby contributing to reducing disaster risks and protecting lives.

[0057] The processing flow will be explained below.

[0058] Analysis of furniture layout and earthquake resistance measures

[0059] Step 1:

[0060] Users take photos of their home using a smartphone or tablet and upload the photos from the device to the server via the app.

[0061] Step 2:

[0062] The server passes the received photo data to an AI module, which then uses image recognition technology to identify the location, type, height, etc. of the furniture.

[0063] Step 3:

[0064] The server refers to past earthquake data and furniture characteristic data to evaluate the risk of the recognized furniture tipping over.

[0065] Step 4:

[0066] Based on the evaluation results, the server will propose specific earthquake-resistant measures, such as fastening furniture and rearranging it.

[0067] Step 5:

[0068] The server transmits the recommended earthquake-resistance measures to the terminal, which then displays them to the user.

[0069] Hazard and evacuation map information mapping

[0070] Step 1:

[0071] The user allows the app to obtain location information, and the device obtains the current location information from GPS.

[0072] Step 2:

[0073] The location information acquired by the device is sent to the server.

[0074] Step 3:

[0075] The server integrates various hazard map data obtained from national and local governments based on location information, and generates hazard maps and evacuation maps for the target area.

[0076] Step 4:

[0077] The server sends the generated hazard map and evacuation map to the terminal.

[0078] Step 5:

[0079] The device displays hazard maps and evacuation maps to the user and provides evacuation route guidance as needed.

[0080] Disaster prediction and evacuation instructions based on real-time data

[0081] Step 1:

[0082] The server periodically collects real-time data such as weather data, seismograph data, and river water level data.

[0083] Step 2:

[0084] The server passes the collected data to an AI module, which analyzes it to assess the possibility of a disaster occurring.

[0085] Step 3:

[0086] If the server determines that there is a high possibility of a disaster occurring, it generates appropriate evacuation instructions.

[0087] Step 4:

[0088] The server generates evacuation instructions and sends them to the terminal along with information on the optimal evacuation route.

[0089] Step 5:

[0090] The device immediately notifies the user of evacuation instructions and evacuation route information received, encouraging evacuation action.

[0091] Automatic safety confirmation

[0092] Step 1:

[0093] The device periodically collects biometric data such as heart rate and body temperature from the user's smart device (e.g., smartwatch).

[0094] Step 2:

[0095] The terminal transmits the collected biometric data to the server.

[0096] Step 3:

[0097] The server analyzes the biometric data and assesses the user's safety.

[0098] Step 4:

[0099] The server generates safety information based on the safety evaluation results and notifies other users (e.g., family and friends) designated in advance.

[0100] Step 5:

[0101] Family and friends (designated users) receive safety notifications from the server and confirm that the user is safe.

[0102] The above is the specific processing flow for each function. This system enables users to respond quickly and appropriately in the event of a disaster, thereby managing risks and protecting lives.

[0103] Example 1

[0104] 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."

[0105] In recent years, the frequent occurrence of natural disasters has created a need for safety measures in individual homes and local communities. However, current disaster prevention systems are overly focused on responding to disasters after they occur, and therefore lack advance measures and real-time information provision. Furthermore, they do not provide specific measures or instructions tailored to each user's situation, making it difficult for users to take optimal actions. Furthermore, safety confirmation during disasters is not carried out quickly and accurately. Therefore, there is a need for the development of a system that can provide consistent support, from advance measures to real-time information provision, evacuation instructions, and safety confirmation.

[0106] 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.

[0107] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a hazard map and an evacuation map based on the location information, means for providing the generated hazard map and evacuation map to the user, means for collecting meteorological data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for collecting biometric data from a smart device, means for analyzing the collected biometric data to evaluate the user's safety, and means for notifying the user of the evaluation results. This allows the user to receive a series of support, including advance earthquake-resistance measures, real-time disaster information, evacuation instructions, and even safety confirmation.

[0108] "Means for receiving images taken by a user" refers to a device or software that has the function of transmitting image data taken by a user to a server via a communication means and receiving the data on the server side.

[0109] "Means for analyzing the image and assessing furniture placement and seismic risk" refers to an algorithm or AI model that analyzes the received image data and assesses the furniture placement and seismic risk depicted in the image.

[0110] The "means for recommending earthquake-resistance measures based on the evaluation results" refers to a function that recommends appropriate earthquake-resistance measures to the user based on the evaluation results of furniture layout and earthquake risk.

[0111] The "means for notifying the user of the recommended earthquake-resistance measures" refers to a function for transmitting information on earthquake-resistance measures generated by the server to the user's terminal and notifying the user of the information.

[0112] "Means for receiving user location information" refers to a function that sends data from GPS or other location information acquisition means to a server and receives that data in order to determine the user's current location.

[0113] "Means for generating hazard maps and evacuation maps based on the location information" refers to algorithms or software that generate hazard maps showing disaster risks and evacuation maps showing safe evacuation routes based on the user's location information.

[0114] "Means for providing the generated hazard map and evacuation map to the user" refers to a function for transmitting the generated hazard map and evacuation map to the user's terminal so that the user can view them.

[0115] "Means of collecting meteorological data and disaster information in real time" refers to means of linking with external data sources and APIs to obtain meteorological data and information on disaster occurrence in real time.

[0116] "Means for analyzing the collected data and predicting the occurrence of disasters" refers to algorithms or AI models that analyze meteorological data and disaster information obtained in real time and predict the possibility of future disasters.

[0117] The "means for generating evacuation instructions based on the prediction results" refers to a function for generating information instructing the user on specific evacuation actions based on the prediction results of the occurrence of a disaster.

[0118] The "means for notifying the user of the generated evacuation instructions" refers to a function for transmitting the generated evacuation instructions to the user's terminal and notifying the user of the information.

[0119] "Means for collecting biometric data from smart devices" refers to the ability to obtain a user's biometric data from smart devices such as smartwatches and fitness trackers.

[0120] "Means for analyzing the collected biometric data and assessing the user's safety" refers to algorithms or software for analyzing the acquired biometric data and assessing the user's health condition and safety.

[0121] The "means for notifying the evaluation results" refers to a function for notifying designated other users (e.g., family members or friends) of the safety information obtained by the analysis through a means for notifying the user.

[0122] MODE FOR CARRYING OUT THE INVENTION

[0123] This invention is a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, real-time disaster predictions and evacuation instructions, and automatic safety confirmation during disasters. This system transmits and receives data between a server, a terminal, and users, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[0124] Analysis of furniture layout and earthquake resistance measures

[0125] This function begins when the user uploads photos of their home from their device to the server. The server then passes the received photos to an AI module, which analyzes furniture placement and the risk of it falling over. The AI ​​module can use an "image analysis algorithm" that is commonly used in image analysis services. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and helps them take the necessary measures.

[0126] Examples:

[0127] A user uploads a photo of their living room from their device to the server. The server uses an AI module to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[0128] Example prompt sentence:

[0129] User: "I'll send you a picture of my living room. Can you confirm if this room needs earthquake protection?"

[0130] Hazard and evacuation map information mapping

[0131] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[0132] Examples:

[0133] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[0134] Example prompt sentence:

[0135] User: "What is the nearest evacuation shelter from my current location?"

[0136] Disaster prediction and evacuation instructions based on real-time data

[0137] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The weather data and damage information used by the server can be obtained from a "weather observation system" or "data provider API." The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[0138] Examples:

[0139] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[0140] Example prompt sentence:

[0141] User: "Are there any upcoming weather forecasts and necessary evacuation warnings for this area?"

[0142] Automatic safety confirmation

[0143] With this function, the terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Using an AI-based "biometric data analysis algorithm," a quick and accurate evaluation is possible. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows for quick confirmation of the user's safety.

[0144] Examples:

[0145] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[0146] Example prompt sentence:

[0147] User: "Check my current physical condition based on my heart rate data."

[0148] In this way, the system of the present invention provides comprehensive information and support to enable users to respond to disasters quickly and appropriately. The system provides each function in an integrated manner to ensure the safety and security of users.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Analysis of furniture layout and earthquake resistance measures

[0151] Step 1:

[0152] Input: The user takes a photo of their home on their device.

[0153] How it works: A user uses the camera app on their smartphone to take a photo of their living room.

[0154] Output: Captured image data.

[0155] Step 2:

[0156] Input: Captured image data.

[0157] How it works: The user uses a smartphone app to upload this image data to a server.

[0158] Output: Image data sent to the server.

[0159] Step 3:

[0160] Input: Image data sent to the server.

[0161] How it works: The server stores the received image data in Google Cloud Storage and passes the URL to the image analysis service.

[0162] Output: URL of the image data.

[0163] Step 4:

[0164] Input: Image data URL.

[0165] How it works: The server passes the URL to the image analysis service, which performs image analysis to evaluate furniture placement and the associated seismic risk.

[0166] Output: Analysis results (furniture placement and seismic risk information).

[0167] Step 5:

[0168] Input: Analysis results (furniture layout and seismic risk information).

[0169] Operation: The server generates appropriate earthquake resistance measures based on the analysis results.

[0170] Output: Seismic countermeasure recommendations.

[0171] Step 6:

[0172] Input: Seismic resilience recommendations.

[0173] Operation: The server sends the generated earthquake resistance countermeasure information to the terminal.

[0174] Output: Earthquake countermeasure information sent to the device.

[0175] Step 7:

[0176] Input: Earthquake prevention information sent to the device.

[0177] Operation: The device displays earthquake-resistance countermeasure information on the user interface and notifies the user.

[0178] Output: Seismic countermeasure information notified to the user.

[0179] Hazard and evacuation map information mapping

[0180] Step 1:

[0181] Input: The user's current location.

[0182] How it works: A user enables the GPS function on their smartphone and presses the "Send current location" button in a system app.

[0183] Output: Location data sent to the server.

[0184] Step 2:

[0185] Input: Location data sent to the server.

[0186] How it works: The server retrieves location information and uses the Google Maps API to aggregate and process hazard map data for the area.

[0187] Output: Organized hazard map and evacuation map data.

[0188] Step 3:

[0189] Input: Organized hazard and evacuation map data.

[0190] Operation: The server sends the consolidated hazard and evacuation maps to the terminal.

[0191] Output: Hazard and evacuation maps sent to the device.

[0192] Step 4:

[0193] Input: Hazard and evacuation maps sent to the device.

[0194] How it works: The device displays map data to the user and guides them to a safe evacuation route.

[0195] Output: Hazard and evacuation maps displayed to the user.

[0196] Disaster prediction and evacuation instructions based on real-time data

[0197] Step 1:

[0198] Input: Real-time weather data and disaster information.

[0199] How it works: The server collects data in real time from the Japan Meteorological Agency API and other data providers.

[0200] Output: Weather data and disaster information collected on the server.

[0201] Step 2:

[0202] Input: Weather data and disaster information collected on the server.

[0203] How it works: The server inputs this data into an AI model to predict the occurrence of disasters.

[0204] Output: Disaster occurrence prediction results.

[0205] Step 3:

[0206] Input: Disaster occurrence prediction results.

[0207] Operation: The server generates evacuation instructions based on the prediction results.

[0208] Output: Evacuation order information.

[0209] Step 4:

[0210] Input: Evacuation order information.

[0211] Operation: The server sends the generated evacuation instructions to the device.

[0212] Output: Evacuation order information sent to the device.

[0213] Step 5:

[0214] Input: Evacuation order information sent to the device.

[0215] How it works: The device displays a pop-up notification of evacuation instructions, urging the user to take immediate action.

[0216] Output: Evacuation instructions notified to the user.

[0217] Automatic safety confirmation

[0218] Step 1:

[0219] Input: Biometric data from a smart device.

[0220] How it works: The device collects biometric data from the smartwatch using Bluetooth or other means.

[0221] Output: Biometric data collected on the device.

[0222] Step 2:

[0223] Input: Biometric data collected on the device.

[0224] Operation: The device sends the collected data to the server.

[0225] Output: Biometric data sent to the server.

[0226] Step 3:

[0227] Input: Biometric data sent to the server.

[0228] How it works: The server analyzes biometric data and assesses the user's safety.

[0229] Output: Safety assessment results.

[0230] Step 4:

[0231] Input: Safety assessment results.

[0232] Operation: The server generates safety information based on the evaluation results.

[0233] Output: Generated safety information.

[0234] Step 5:

[0235] Input: Generated safety information.

[0236] Operation: The server notifies the generated safety information to other users (e.g., family and friends) who have registered in advance.

[0237] Output: Notified safety information.

[0238] The above is the specific processing flow of this system's program. Each step works together to achieve comprehensive disaster prevention measures and safety confirmation.

[0239] (Application example 1)

[0240] 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."

[0241] Conventional disaster response support systems provide individual functions, such as furniture placement, earthquake-resistance measures, hazard map provision, evacuation instructions, and automatic safety confirmation, making it difficult for users to use them in a centralized manner. It is also difficult for autonomous vehicles to respond in real time to situations that change over time. As a result, optimal information cannot be provided to users to take safe action quickly in the event of a disaster, which can lead to delayed responses. Furthermore, there is a lack of disaster response systems that effectively utilize vehicle location information and biometric data.

[0242] 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.

[0243] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a hazard map and an evacuation map based on the location information, means for providing the generated hazard map and evacuation map to the user, means for collecting meteorological data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for a terminal to transmit vehicle location information to the server and display evacuation information on a vehicle display, means for collecting user biometric data and notifying other users of the user's safety, and means for analyzing the biometric data and safety information and evaluating safety. This enables centralized and real-time provision of information to users so that they can respond quickly and appropriately in the event of a disaster.

[0244] The "means for receiving images" refers to a system or component for transmitting images taken by a user from a terminal to a server and receiving the images.

[0245] "Means for analyzing images" means a system or algorithm that uses an AI module or image processing technology to analyze the information contained in the received images and evaluate specific patterns or risks.

[0246] "Means for assessing furniture placement and seismic risk" refers to a system or process for recognizing the location and placement of furniture based on the results of image analysis and assessing the associated seismic risk.

[0247] The "means for recommending earthquake-resistance measures" is a system or component for presenting optimal measures to users based on the evaluation results.

[0248] "Means for notifying users" refers to a system or application that sends recommended measures and important information to users' terminals and notifies them.

[0249] The "means for receiving location information" refers to a system or component that allows the terminal to obtain current location information (such as GPS data) and transmit it to the server.

[0250] "Means for generating hazard maps and evacuation maps" refers to a system or software that integrates the latest disaster information based on location information to generate appropriate evacuation routes and hazard maps.

[0251] "Means for collecting meteorological data and disaster information in real time" refers to a system or sensor for collecting current meteorological data and disaster information in real time and transmitting them to a server.

[0252] "Means for predicting disaster occurrence" refers to a system or algorithm that analyzes collected data and uses AI or other tools to predict the probability of disaster occurrence and its impact.

[0253] The "means for generating evacuation instructions" is a system or component for creating messages or notifications to instruct users on appropriate evacuation actions based on the prediction results.

[0254] The "means for displaying evacuation information on the vehicle display" is an interface for displaying the evacuation information sent from the server on the vehicle monitor or infotainment system.

[0255] A "means for collecting biometric data" is a system or sensor for collecting data from a smart device (such as a smartwatch) that measures a user's health status or vital signs.

[0256] "Means for notifying other users of safety status" refers to a system or application that analyzes collected biometric data and notifies designated other users (family or friends) of the results of a safety assessment.

[0257] A "means for assessing safety" is a system or algorithm for analyzing collected biometric data and assessing whether a user is safe.

[0258] This invention is an integrated system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, disaster predictions and evacuation instructions based on real-time data, and automatic safety confirmation in the event of a disaster. This system transmits and receives data between four parties: a server, terminals, vehicles, and users, and provides appropriate information and notifications.

[0259] Analysis of furniture layout and earthquake resistance measures

[0260] The system begins when a user takes a picture of their home using a mobile device and uploads it to a server. The server then passes the received image to an AI module, which analyzes furniture placement and the risk of it falling over. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and supports them in taking the necessary measures. For example, if a tall bookshelf is determined to be at high risk of falling over during an earthquake, the device will suggest measures such as "We recommend a kit for securing the bookshelf to the wall."

[0261] Provision of hazard maps and evacuation map information

[0262] The device sends the user's location information to the server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and displays it on the vehicle's display and the user's smartphone. The user can refer to this information to take safe evacuation action.

[0263] Disaster prediction and evacuation instructions based on real-time data

[0264] The server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The device immediately notifies the user and provides guidance on appropriate evacuation routes and evacuation locations. For example, an evacuation instruction may be sent stating, "Evacuation is required in this area immediately," and the route to the nearest evacuation location will be displayed.

[0265] Automatic safety confirmation

[0266] The terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety information during the disaster. This allows the user's safety to be quickly confirmed. For example, if the server receives the user's heart rate data and determines that it is within the normal range, it generates safety information stating "User A is safe" and notifies the family.

[0267] Technical details

[0268] The server includes an AI module, a hazard map generation system, a real-time data collection system, and a notification system. The hardware used includes GPS sensors, smartphones, smartwatches, and autonomous vehicle navigation systems. The software includes Python, image analysis algorithms, and an HTTP communication library (requests).

[0269] Specific examples

[0270] When a user is in a car during heavy rain, the smartphone app acquires location information from the vehicle's GPS sensor and sends it to a server. The server analyzes disaster predictions and evacuation information, and sends appropriate evacuation routes to the vehicle's navigation system. As a result, the navigation screen displays safe evacuation routes updated in real time.

[0271] Example prompts for generative AI models

[0272] "Based on my current location (35.6895, 139.6917), please retrieve the latest evacuation information and hazard map, and generate the optimal evacuation route."

[0273] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0274] Step 1:

[0275] Users take pictures of their homes and upload them to a server using a device (such as a smartphone or tablet). These images become input data for analyzing the furniture layout in their homes.

[0276] Step 2:

[0277] The server receives the images and passes them to the AI ​​module. The AI ​​module analyzes the received images and evaluates the furniture placement and risk of tipping over. As a result of the analysis, it may determine, for example, that a tall bookshelf is at high risk of tipping over during an earthquake. This analysis result becomes the output data.

[0278] Step 3:

[0279] Based on the analysis results, the server recommends appropriate earthquake-resistance measures. For example, it generates a message such as, "We recommend a kit for fixing bookshelves to the wall." This recommended measure becomes the output data.

[0280] Step 4:

[0281] The device receives the recommended measures sent from the server and notifies the user, who can then check the notification and take the necessary measures.

[0282] Step 5:

[0283] Users send location information from their devices to the server, which then becomes the input data for generating the latest hazard and evacuation maps based on their current location information.

[0284] Step 6:

[0285] The server generates the latest hazard and evacuation maps based on the received location information. The generation process integrates the latest data obtained from national and local governments. The generated maps are the output data.

[0286] Step 7:

[0287] The server sends the generated hazard map and evacuation map to the terminal, which displays them on its screen so that the user can check the safe evacuation route.

[0288] Step 8:

[0289] The server collects meteorological data and damage information in real time, which serves as input data for disaster prediction.

[0290] Step 9:

[0291] The server passes the collected data to an AI module, which then predicts the occurrence of disasters. For example, heavy rain is predicted and a high probability of flooding is determined. This prediction result becomes the output data.

[0292] Step 10:

[0293] The server generates evacuation instructions based on the prediction results. For example, it may generate instructions such as "This area requires immediate evacuation." This evacuation instruction becomes the output data.

[0294] Step 11:

[0295] The device receives evacuation instructions sent from the server and immediately notifies the user, who can then check the notification and take appropriate evacuation action.

[0296] Step 12:

[0297] The terminal collects biometric data from the user's smart device (e.g., smartwatch), which serves as input data for assessing the user's health status.

[0298] Step 13:

[0299] The terminal sends the collected biometric data to the server, which analyzes the data and evaluates the user's safety. The evaluation results are output data.

[0300] Step 14:

[0301] Based on the evaluation results, the server notifies other designated users (family and friends) of the user's safety. For example, the server sends an email to the user's family saying, "User A is safe."

[0302] Step 15:

[0303] The device sends the vehicle's location information to a server, which then generates a safe evacuation route based on the location information and displays it on the vehicle's display. The user can then check the safe evacuation route through the navigation system.

[0304] 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.

[0305] This invention is a system that combines a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, disaster predictions and evacuation instructions based on real-time data, and automatic safety confirmation in the event of a disaster, with an emotion engine that recognizes the user's emotions and takes appropriate action based on those emotions. This system transmits and receives data between four parties: a server, a terminal, the emotion engine, and the user, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[0306] Analysis of furniture layout and earthquake resistance measures

[0307] This function begins when the user uploads photos of their home from their device to the server. The server then passes the photos to an AI module, which analyzes furniture placement and the risk of it falling over. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends them to the device. The device then notifies the user and helps them take the necessary measures.

[0308] Examples:

[0309] A user uploads a photo of their living room from their device to the server. The server uses AI to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[0310] Hazard and evacuation map information mapping

[0311] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[0312] Examples:

[0313] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[0314] Disaster prediction and evacuation instructions based on real-time data

[0315] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[0316] Examples:

[0317] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[0318] Automatic safety confirmation

[0319] With this function, the terminal collects biometric data such as heart rate and body temperature from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows the user's safety to be quickly confirmed.

[0320] Examples:

[0321] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[0322] Emotion recognition by emotion engine

[0323] The emotion engine acquires image and voice data of the user and recognizes the user's emotions based on that data. This allows the system to understand the user's emotional state during a disaster and provide appropriate support.

[0324] Examples:

[0325] When a user talks to the device or takes a selfie, the device sends this data to the server. The server's emotion engine analyzes the received data, and if it determines that the user is feeling anxious or scared, the server sends a notification to the device recommending that the user contact a mental health care professional or counseling service. The device notifies the user and helps them get the support they need.

[0326] Evacuation support using an emotion engine

[0327] Based on the emotions recognized, the emotion engine can suggest appropriate measures to reduce stress during evacuation. It can also select and notify the appropriate contact points and evacuation locations depending on the situation.

[0328] Examples:

[0329] If the emotion engine detects strong anxiety or stress in the user while evacuating, the server will use that information to determine that "support staff is needed nearby." The server will send a notification to the support staff at the evacuation site and quickly arrange for support for the user. Additionally, if the emotion engine detects that the user is in a calm emotional state, the server will send a notification that "safety is secured at the current evacuation site," helping the user to feel at ease.

[0330] The above is a specific embodiment of the invention that combines an emotion engine. This system enables users to respond quickly and appropriately in the event of a disaster, managing risks and protecting lives, while also providing appropriate support according to the user's emotional state.

[0331] The processing flow will be explained below.

[0332] Analysis of furniture layout and earthquake resistance measures

[0333] Step 1:

[0334] Users take photos of their home using a smartphone or tablet and upload the photos from the device to the server via the app.

[0335] Step 2:

[0336] The server passes the received photo data to an AI module, which then uses image recognition technology to identify the location, type, height, etc. of the furniture.

[0337] Step 3:

[0338] The server refers to past earthquake data and furniture characteristic data to evaluate the risk of the recognized furniture tipping over.

[0339] Step 4:

[0340] Based on the evaluation results, the server will propose specific earthquake-resistant measures, such as fastening furniture and rearranging it.

[0341] Step 5:

[0342] The server transmits the recommended earthquake-resistance measures to the terminal, which then displays them to the user.

[0343] Hazard and evacuation map information mapping

[0344] Step 1:

[0345] The user allows the app to obtain location information, and the device obtains the current location information from GPS.

[0346] Step 2:

[0347] The location information acquired by the device is sent to the server.

[0348] Step 3:

[0349] The server integrates various hazard map data obtained from national and local governments based on location information, and generates hazard maps and evacuation maps for the target area.

[0350] Step 4:

[0351] The server sends the generated hazard map and evacuation map to the terminal.

[0352] Step 5:

[0353] The device displays hazard maps and evacuation maps to the user and provides evacuation route guidance as needed.

[0354] Disaster prediction and evacuation instructions based on real-time data

[0355] Step 1:

[0356] The server periodically collects real-time data such as weather data, seismograph data, and river water level data.

[0357] Step 2:

[0358] The server passes the collected data to an AI module, which analyzes it and evaluates the prediction of disaster occurrence.

[0359] Step 3:

[0360] The server generates evacuation instructions based on the predicted disaster occurrence results.

[0361] Step 4:

[0362] The server generates evacuation instructions and sends them to the terminal along with information on the optimal evacuation route.

[0363] Step 5:

[0364] The device immediately notifies the user of the evacuation instructions and evacuation route information it receives, urging the user to take evacuation action.

[0365] Automatic safety confirmation

[0366] Step 1:

[0367] The device periodically collects biometric data such as heart rate and body temperature from the user's smart device (e.g., smartwatch).

[0368] Step 2:

[0369] The terminal transmits the collected biometric data to the server.

[0370] Step 3:

[0371] The server analyzes the biometric data and assesses the user's safety.

[0372] Step 4:

[0373] The server generates safety information based on the safety evaluation results and notifies other users (e.g., family and friends) designated in advance.

[0374] Step 5:

[0375] The designated user receives the safety notification from the server and confirms that the user is safe.

[0376] Emotion recognition by emotion engine

[0377] Step 1:

[0378] The user talks to the device and takes a selfie, and the device sends this data to the server.

[0379] Step 2:

[0380] The server uses an emotion engine to analyze the received image and audio data and identify the user's emotional state.

[0381] Step 3:

[0382] The server recommends necessary mental health care and counseling services based on the emotional state identified by the emotion engine.

[0383] Step 4:

[0384] The server notifies the device of recommendations for mental health care and counseling.

[0385] Step 5:

[0386] The device displays the received recommendations to the user, helping them get the support they need.

[0387] Evacuation support using an emotion engine

[0388] Step 1:

[0389] While the user is evacuating, the emotion engine detects strong anxiety or stress, and the device sends this information to the server.

[0390] Step 2:

[0391] The server uses this information to determine the user's condition and whether nearby support staff is needed.

[0392] Step 3:

[0393] If the server determines that a service is needed, it will notify the nearest support staff and arrange for support for the user.

[0394] Step 4:

[0395] The server sends information about the user's evacuation location and, if necessary, other safe evacuation location information to the terminal.

[0396] Step 5:

[0397] The device displays the received evacuation site information to the user, helping the user to feel at ease.

[0398] Example 2

[0399] 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."

[0400] In the event of a disaster, the provision of information for users to take prompt and appropriate evacuation actions was insufficient.In addition, the system was unable to confirm the user's safety or provide support based on the user's emotional state, making it impossible to reduce stress and difficulties during a disaster.

[0401] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving digital images taken by a user, means for analyzing the digital images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a risk map and an evacuation route map based on the location information, means for providing the generated risk map and evacuation route map to the user, means for collecting environmental data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for receiving the user's biometric data and evaluating the user's safety, means for notifying other users of safety information based on the evaluation results, means for analyzing the user's emotional data to recognize the user's emotional state, and means for recommending appropriate support based on the emotional state. This enables users to receive appropriate information and support in the event of a disaster and take evacuation action quickly and safely. It will also be possible to check the user's safety and provide support according to their emotional state, helping to reduce stress and difficulties during disasters.

[0402] "User" refers to an individual or organization that uses the disaster information provision system.

[0403] "Server" refers to the central system that collects, analyzes, manages data, and provides information to users.

[0404] A "terminal" is a device that a user uses to communicate with a server, and includes mobile devices such as smartphones and tablets.

[0405] "Digital image" refers to image data that a user takes using a terminal and uploads to a server.

[0406] "Seismic risk" refers to a parameter that evaluates the degree of damage or danger that the placement of furniture and equipment could cause during an earthquake.

[0407] "Evaluation Results" refers to the analysis data generated after the AI ​​module analyzes the digital image.

[0408] "Earthquake-resistant measures" refers to specific earthquake countermeasure methods proposed to users based on the evaluation results.

[0409] "Location information" refers to the user's current geographical location data obtained using the device's GPS function, etc.

[0410] "Risk map" refers to map data generated based on a user's location information that visually shows the risk of earthquakes and other disasters.

[0411] "Evacuation route map" refers to map data that shows the route from the user's current location to the optimal evacuation site.

[0412] "Environmental Data" means meteorological and other real-time data relating to the environment.

[0413] "Disaster information" refers to information regarding the extent of damage and the extent of impact when a disaster occurs.

[0414] "Disaster occurrence prediction" refers to the AI ​​module predicting the probability of disaster occurrence and its impact based on collected environmental data and damage information.

[0415] "Evacuation instructions" refers to evacuation instructions and recommendations generated by the server based on disaster predictions and issued to users.

[0416] "Biometric data" refers to physical information such as a user's heart rate and body temperature obtained from a smart device or other device.

[0417] "Safety information" refers to information used to evaluate the user's safety based on collected biometric data and to notify other designated users as necessary.

[0418] "Emotional data" refers to data relating to the emotional state of a user that is analyzed based on image and voice data.

[0419] "Emotional state" refers to the user's current mental state as determined by analyzing emotion data.

[0420] This invention is a system that provides users with quick and appropriate evacuation measures during disasters, and provides support based on the user's safety confirmation and emotional state. This system operates in cooperation with a server, terminals, and various data analysis modules.

[0421] First, the user uploads digital images of their home room to the server via their device. The device is equipped with a camera and has the functionality to securely transmit images taken by the user to the server. The server receives the images and uses AI modules (e.g., TensorFlow and PyTorch) to analyze furniture placement and tip-over risk. Based on the analysis results, the server generates earthquake-resistance measures and notifies the device. This allows the user to receive specific instructions for implementing earthquake measures.

[0422] Next, if the user allows location sharing, the device sends GPS data to the server. The server uses this location information to generate an updated risk map and evacuation route map. The risk map is created by integrating information obtained from national and local government databases. The generated map is sent to the device, and the user receives visual guidance on appropriate evacuation actions.

[0423] Furthermore, the server collects environmental data (such as weather information) and damage information in real time and uses AI to predict the occurrence of disasters. If the server determines that there is a high risk of a disaster occurring, it generates evacuation instructions and sends them to the device. The device immediately notifies the user, allowing them to quickly move to the designated evacuation route or evacuation location.

[0424] The terminal also collects biometric data (heart rate, body temperature, etc.) from smart devices (e.g., smartwatches) and sends it to a server. The server analyzes this data and evaluates the user's safety. The evaluation results are notified to other users (e.g., family and friends) designated in advance. This makes it possible to quickly check the user's safety information.

[0425] Finally, the user's emotional data (image and voice data) is also sent to the server via the device. The emotion engine uses this data to analyze the user's emotional state and recommend appropriate support. For example, if the user is experiencing extreme anxiety or fear, the server will notify them to contact a mental health care professional and provide guidance on receiving appropriate counseling services.

[0426] Examples:

[0427] The user takes a photo of their living room onto their device and presses the "Send" button. The device sends the photo data to the server, which analyzes it and determines that "tall bookshelves have a high risk of tipping over." The server generates a countermeasure, such as "Use a kit to secure the bookshelf to the wall," and sends it to the device. The user's device receives a notification, and the user confirms the details.

[0428] Example prompt sentence:

[0429] "I would like to use this system to analyze the earthquake resistance measures of my home and find out what measures are necessary. Please upload a photo of your living room and analyze what risks there are."

[0430] This system analyzes various data and provides users with appropriate information, minimizing risks in the event of a disaster and providing safety and security.

[0431] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0432] Step 1: User takes and uploads a digital image

[0433] Specific operation: The user launches the application on the device, takes a photo of a room such as the living room, or selects an existing photo, and then presses the upload button to send the image data to the server.

[0434] Input: A digital image taken or selected by the user.

[0435] Output: Digital image data sent to the server.

[0436] Step 2: The device sends the digital image data to the server.

[0437] Specific operation: The terminal receives user input and sends image data to the server using a secure protocol (e.g., HTTPS).

[0438] Input: A digital image taken by the user.

[0439] Output: Digital image data sent to the server.

[0440] Step 3: The server receives the digital image and begins analysis.

[0441] Specific operation: The server receives the uploaded image data and passes it to an AI module (e.g., TensorFlow or PyTorch).

[0442] Input: Digital image data sent from the device.

[0443] Output: Image data being analyzed.

[0444] Step 4: The AI ​​module analyzes furniture placement and seismic risk

[0445] How it works: The AI ​​module processes image data, identifies the location and type of furniture, and evaluates the earthquake risk. For example, if a tall bookshelf is not secured, it will determine that there is a high risk of it falling over.

[0446] Input: Digital image data passed by the server.

[0447] Output: Analysis results (furniture placement and seismic risk assessment).

[0448] Step 5: The server generates earthquake-resistance measures based on the analysis results and sends them to the device.

[0449] Specific operation: The server generates earthquake-resistance measures based on the analysis results of the AI ​​module. For example, it creates a specific countermeasure proposal such as "We recommend a kit for fixing bookshelves to the wall" and sends it to the terminal.

[0450] Input: Analysis results from the AI ​​module.

[0451] Output: Generated earthquake countermeasures and notification data to the terminal.

[0452] Step 6: The device notifies the user of earthquake countermeasure information

[0453] Specific operation: The device displays the notification data received from the server and informs the user of the proposed solution. The user can tap the notification to view more information.

[0454] Input: Earthquake countermeasure notification data sent from the server.

[0455] Output: The seismic action notification that is displayed to the user.

[0456] Step 7: The user allows location sharing

[0457] Specific behavior: The user allows location sharing in the application settings.

[0458] Input: User action (allow location sharing).

[0459] Output: Permission settings for the system to obtain location information.

[0460] Step 8: The device sends its location to the server

[0461] Specific operation: The device acquires GPS data in real time and sends it to the server.

[0462] Input: GPS data acquired by the device.

[0463] Output: The location data sent to the server.

[0464] Step 9: The server generates a risk map and evacuation route map based on the location information.

[0465] Specific operation: The server generates the latest risk maps and evacuation route maps from national and local government databases based on the user's location information.

[0466] Input: Location data and map data from government agencies.

[0467] Output: Generated risk map and evacuation route map.

[0468] Step 10: The server sends the generated risk map and evacuation route map to the terminal.

[0469] Specific operation: The server sends the generated map data to the terminal.

[0470] Input: Generated risk map and evacuation route map.

[0471] Output: Risk map and evacuation route map data sent to the terminal.

[0472] Step 11: The device displays map information to the user

[0473] Specific operation: The device displays the received map information so that the user can check it.

[0474] Input: Map data sent from the server.

[0475] Output: Risk map and evacuation route diagram displayed to the user.

[0476] Step 12: Server collects real-time environmental data

[0477] What it does: The server collects real-time environmental data from the Japan Meteorological Agency and other public data sources.

[0478] Input: Environmental data from the Japan Meteorological Agency and data providers.

[0479] Output: Collected real-time environmental data.

[0480] Step 13: The server makes a disaster prediction

[0481] Specific operation: Environmental data collected by the server is input into an AI model to evaluate and predict disaster risks.

[0482] Input: Collected environmental data.

[0483] Output: Disaster prediction results data.

[0484] Step 14: The server generates evacuation instructions based on the risk of disaster occurrence.

[0485] Specific operation: The server generates appropriate evacuation instructions based on the prediction results of the AI ​​model.

[0486] Input: Disaster prediction result data.

[0487] Output: Generated evacuation order data.

[0488] Step 15: The server sends the generated evacuation instructions to the terminal.

[0489] Specific operation: The server sends evacuation instruction data to the terminal.

[0490] Input: Generated evacuation order data.

[0491] Output: Evacuation instruction data sent to the terminal.

[0492] Step 16: The device notifies the user of the evacuation order

[0493] Specific operation: The device immediately notifies the user of the evacuation instruction data it receives.

[0494] Input: Evacuation order data sent from the server.

[0495] Output: Evacuation instructions displayed to the user.

[0496] Step 17: The terminal collects biometric data from the smart device

[0497] Specific operation: The terminal collects biometric data such as heart rate and body temperature from smart devices such as smartwatches.

[0498] Input: Biometric data from smart device.

[0499] output: The collected biometric data.

[0500] Step 18: The device sends the biometric data to the server.

[0501] Specific operation: The terminal sends the collected biometric data to the server.

[0502] Input: Biometric data collected from smart devices.

[0503] Output: Biometric data sent to the server.

[0504] Step 19: The server analyzes the biometric data and evaluates the user's safety.

[0505] Specific operation: The server analyzes the biometric data and evaluates the user's health condition. If it is within the normal range, it is deemed "safe."

[0506] Input: Biometric data sent from the device.

[0507] Output: Safety assessment results.

[0508] Step 20: The server notifies the safety information based on the evaluation result.

[0509] Specific operation: The server notifies other designated users (e.g., family members) of the safety information based on the safety evaluation results.

[0510] Input: Safety assessment results.

[0511] Output: Safety information sent to other users.

[0512] Step 21: The device acquires the user's emotion data and sends it to the server.

[0513] Specific operation: The user talks to the device or takes a selfie. The device sends this emotional data to the server.

[0514] Input: Emotional image and audio data.

[0515] Output: Emotion data sent to the server.

[0516] Step 22: The server analyzes the emotion data and recognizes the emotional state.

[0517] Specific operation: The emotion engine on the server analyzes the received emotion data and recognizes the user's emotional state (e.g., anxiety, fear).

[0518] Input: Emotion data sent from the device.

[0519] Output: Perceived emotional state.

[0520] Step 23: The server recommends appropriate assistance based on the emotional state.

[0521] Specific operation: Based on the recognized emotional state, the server generates a notification recommending appropriate support measures (e.g., contacting a mental health professional) and sends it to the device.

[0522] Input: Perceived emotional state.

[0523] Output: The generated help recommendation notification.

[0524] Step 24: The device displays a support recommendation notification to the user.

[0525] Specific operation: The device displays the support recommendation notification received from the server to the user so that appropriate support can be provided.

[0526] Input: Help recommendation notification sent from the server.

[0527] Output: Help recommendation notification displayed to the user.

[0528] (Application example 2)

[0529] 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."

[0530] During disasters, safe evacuation, accurate information provision, and rapid safety confirmation are important. However, conventional systems do not take into account real-time weather data or the user's emotional state, making it difficult to provide optimal evacuation instructions and support. Food delivery services also require rapid safety confirmation and appropriate support based on the user's emotional state during disasters, but there has been a lack of systems that comprehensively address these needs. Therefore, a new system is needed to ensure safety during disasters and provide support based on the user's emotional state.

[0531] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0532] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake resistance measures, means for collecting meteorological data and biometric data, means for analyzing the collected meteorological data to predict the occurrence of a disaster, means for analyzing the user's image data to evaluate the user's emotional state and provide appropriate support, and means for transmitting safety information to others registered by the user. This makes it possible to ensure safety and provide appropriate support based on the user's emotional state and biometric data even in the event of a disaster.

[0533] "Images taken by a user" refers to still image or video data taken by a user using a terminal.

[0534] "Furniture arrangement" is information that indicates the position and arrangement of furniture in a room.

[0535] "Seismic risk" is an index used to evaluate the risk of furniture tipping over or moving in the event of an earthquake.

[0536] "Earthquake-resistant measures" are measures and procedures to minimize damage to furniture and structures in the event of an earthquake.

[0537] "Weather data" refers to data that includes information about weather forecasts and current weather conditions.

[0538] "Biometric data" refers to data relating to the user's physical condition, such as heart rate and body temperature.

[0539] "Disaster prediction" is the process of analyzing the possibility of disasters occurring based on meteorological data and other environmental information.

[0540] "Emotional state" refers to the psychological state of the user that can be detected from facial expressions, tone of voice, etc.

[0541] "Safety information" is information for confirming the safety of a user in the event of a disaster or emergency.

[0542] A "hazard map" is a map that shows the potential damage that may occur in the event of a natural disaster.

[0543] An "evacuation map" is a map that shows safe evacuation routes and evacuation locations in the event of a disaster.

[0544] An "evacuation instruction" is a message that instructs the user to evacuate to a safe place when a disaster occurs.

[0545] This invention is a system for ensuring the safety of users in the event of a disaster and providing appropriate support promptly. This system has the function of receiving and analyzing images taken by the user and evaluating furniture placement and earthquake resistance risks. It can also collect meteorological data and biometric data to predict the occurrence of disasters. Furthermore, it uses an emotion engine to evaluate the user's emotional state and provide appropriate support based on that. It can also send safety information to others registered by the user.

[0546] Hardware and Software

[0547] Hardware: Smartphones, smartwatches

[0548] Software: Applications (Android / iOS), Google Maps API, Twilio SMS API, emotion engine (e.g., Affectiva SDK)

[0549] Program processing explanation

[0550] 1. Receiving and analyzing images taken by the user:

[0551] Users take pictures of their rooms with their smartphones and upload them to the server, which then uses AI modules to analyze the images and assess furniture placement and seismic risks. If necessary, the server recommends earthquake-resistance measures and notifies the user.

[0552] 2. Meteorological and biometric data collection:

[0553] Weather and biometric data are collected in real time from smartphones and smartwatches. The server analyzes the weather data and predicts the occurrence of disasters. At the same time, it analyzes the biometric data and evaluates the user's safety.

[0554] 3. Emotional assessment and support:

[0555] The system sends the user's image and voice data to the emotion engine to evaluate their emotional state, and if the user is in a stressful state, generates a notification to provide appropriate assistance.

[0556] 4. Provision of hazard maps and evacuation maps:

[0557] The server receives the user's location information and generates updated hazard and evacuation maps based on that location. These maps are provided to the user, guiding them to safe evacuation routes.

[0558] 5. Sending safety information:

[0559] The server sends safety information based on the user's biometric data to other people registered by the user, allowing the user's safety to be quickly confirmed.

[0560] Specific examples

[0561] A user takes a photo of their living room with their smartphone and uploads it to the server. The server uses AI to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then recommends a countermeasure, such as "We recommend a kit to secure the bookshelf to the wall," and sends a notification to the smartphone.

[0562] At the same time, the server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and notifies the smartphone. The smartphone then notifies the user of this information and displays a route to the nearest evacuation site.

[0563] Additionally, if a user is wearing a smartwatch, biometric data such as heart rate data is collected in real time. The server analyzes the biometric data, and if there are no abnormalities, it generates safety information stating "User A is safe" and notifies the user's family. The family receives this information and confirms that the user is safe.

[0564] Prompt Sentence Examples

[0565] The task is to obtain data from a weather forecast API and send evacuation instructions via SMS if heavy rain is predicted. It also analyzes the user's image data using an emotion engine and sends a support message if the user is in a stressful state. Specific examples of prompts are as follows:

[0566] plaintext

[0567] Prerequisites:

[0568] 1. A delivery staff member in Tokyo uses a smartphone and a smartwatch.

[0569] 2. Use weather forecast data and emotion engines to provide guidance and support during disasters.

[0570] task:

[0571] 1. Get data from the weather forecast API and send evacuation instructions via SMS if heavy rain is predicted.

[0572] 2. Analyze the user's image data with an emotion engine and send a supportive message if the user is in a stressful state.

[0573] question:

[0574] 1. Configure the API to retrieve weather forecast data.

[0575] 2. Provide an example of code to send important evacuation instructions via SMS.

[0576] 3. Give an example of the process of analyzing image data with an emotion engine.

[0577] As described above, the system of the present invention can be implemented. This system ensures safety and provides appropriate support based on the user's emotional state and biometric data even during a disaster.

[0578] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0579] Step 1:

[0580] The server receives images of the room taken by the user. The user takes an image of the room with their smartphone and uploads this image data to the server. The input is the image data taken with the user's smartphone, and the output is the image data stored on the server.

[0581] Step 2:

[0582] The server analyzes the received image data and evaluates furniture placement and seismic risk. It uses an AI module to perform image analysis and identify the location and placement of furniture that is prone to tipping over. The input is the image data received in step 1, and the output is the furniture placement information and seismic risk assessment as the analysis results.

[0583] Step 3:

[0584] The server recommends appropriate earthquake-resistance measures based on the analysis results. For example, it generates a message such as "We recommend a kit for fixing bookshelves to the wall." The input is the analysis results from step 2, and the output is the recommended measures.

[0585] Step 4:

[0586] The server notifies the user of the recommended countermeasure information. It sends the recommended message to the user's smartphone and displays it to the user. The input is the recommended countermeasure information generated in step 3, and the output is the message displayed on the user's smartphone.

[0587] Step 5:

[0588] The device collects weather data and biometric data and sends them to the server. The weather data is obtained from a weather forecast API, and the biometric data is collected from the smartwatch. The input is the weather data API response and the biometric data from the smartwatch, and the output is the weather data and biometric data sent to the server.

[0589] Step 6:

[0590] The server analyzes the collected weather data and predicts the occurrence of disasters. It analyzes the weather data and evaluates the risk of heavy rain and earthquakes. The input is the weather data from step 5, and the output is the predicted disaster occurrence results.

[0591] Step 7:

[0592] The server sends evacuation instructions to the user based on the disaster prediction results. For example, it generates a message saying, "Evacuation is required in this area immediately," and sends it to the user's smartphone. The input is the prediction result in step 6, and the output is the evacuation instruction message displayed on the user's smartphone.

[0593] Step 8:

[0594] The server sends the user's image data to the emotion engine to evaluate their emotional state. The engine analyzes the image and audio data sent by the user and determines whether they are under stress. The input is the user's image and audio data, and the output is the evaluation result of their emotional state.

[0595] Step 9:

[0596] The server generates a message offering appropriate support based on the evaluation result of the emotional state and notifies the user. For example, it generates a message such as "If the user is feeling stressed, we recommend counseling services." The input is the evaluation result of the emotional state in step 8, and the output is the support message displayed on the user's smartphone.

[0597] Step 10:

[0598] The server sends safety information to other people registered by the user. It analyzes biometric data and notifies the user of the results of its safety assessment via email or message. The input is the analysis result of the biometric data, and the output is a safety information message sent to the registered devices of other people.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] [Second embodiment]

[0603] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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."

[0615] This invention is a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, real-time disaster predictions and evacuation instructions, and automatic safety confirmation during disasters. This system transmits and receives data between a server, a terminal, and users, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[0616] Analysis of furniture layout and earthquake resistance measures

[0617] This function begins when the user uploads photos of their home from their device to the server. The server then passes the photos to an AI module, which analyzes furniture placement and the risk of furniture tipping over. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and helps them take the necessary measures.

[0618] Examples:

[0619] A user uploads a photo of their living room from their device to the server. The server uses AI to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[0620] Hazard and evacuation map information mapping

[0621] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[0622] Examples:

[0623] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[0624] Disaster prediction and evacuation instructions based on real-time data

[0625] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[0626] Examples:

[0627] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[0628] Automatic safety confirmation

[0629] With this function, the terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows the user's safety to be quickly confirmed.

[0630] Examples:

[0631] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[0632] As described above, the system of the present invention provides an integrated set of functions to help users respond quickly and appropriately in the event of a disaster, thereby contributing to reducing disaster risks and protecting lives.

[0633] The processing flow will be explained below.

[0634] Analysis of furniture layout and earthquake resistance measures

[0635] Step 1:

[0636] Users take photos of their home using a smartphone or tablet and upload the photos from the device to the server via the app.

[0637] Step 2:

[0638] The server passes the received photo data to an AI module, which then uses image recognition technology to identify the location, type, height, etc. of the furniture.

[0639] Step 3:

[0640] The server refers to past earthquake data and furniture characteristic data to evaluate the risk of the recognized furniture tipping over.

[0641] Step 4:

[0642] Based on the evaluation results, the server will propose specific earthquake-resistant measures, such as fastening furniture and rearranging it.

[0643] Step 5:

[0644] The server transmits the recommended earthquake-resistance measures to the terminal, which then displays them to the user.

[0645] Hazard and evacuation map information mapping

[0646] Step 1:

[0647] The user allows the app to obtain location information, and the device obtains the current location information from GPS.

[0648] Step 2:

[0649] The location information acquired by the device is sent to the server.

[0650] Step 3:

[0651] The server integrates various hazard map data obtained from national and local governments based on location information, and generates hazard maps and evacuation maps for the target area.

[0652] Step 4:

[0653] The server sends the generated hazard map and evacuation map to the terminal.

[0654] Step 5:

[0655] The device displays hazard maps and evacuation maps to the user and provides evacuation route guidance as needed.

[0656] Disaster prediction and evacuation instructions based on real-time data

[0657] Step 1:

[0658] The server periodically collects real-time data such as weather data, seismograph data, and river water level data.

[0659] Step 2:

[0660] The server passes the collected data to an AI module, which analyzes it to assess the possibility of a disaster occurring.

[0661] Step 3:

[0662] If the server determines that there is a high possibility of a disaster occurring, it generates appropriate evacuation instructions.

[0663] Step 4:

[0664] The server generates evacuation instructions and sends them to the terminal along with information on the optimal evacuation route.

[0665] Step 5:

[0666] The device immediately notifies the user of evacuation instructions and evacuation route information received, encouraging evacuation action.

[0667] Automatic safety confirmation

[0668] Step 1:

[0669] The device periodically collects biometric data such as heart rate and body temperature from the user's smart device (e.g., smartwatch).

[0670] Step 2:

[0671] The terminal transmits the collected biometric data to the server.

[0672] Step 3:

[0673] The server analyzes the biometric data and assesses the user's safety.

[0674] Step 4:

[0675] The server generates safety information based on the safety evaluation results and notifies other users (e.g., family and friends) designated in advance.

[0676] Step 5:

[0677] Family and friends (designated users) receive safety notifications from the server and confirm that the user is safe.

[0678] The above is the specific processing flow for each function. This system enables users to respond quickly and appropriately in the event of a disaster, thereby managing risks and protecting lives.

[0679] Example 1

[0680] 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."

[0681] In recent years, the frequent occurrence of natural disasters has created a need for safety measures in individual homes and local communities. However, current disaster prevention systems are overly focused on responding to disasters after they occur, and therefore lack advance measures and real-time information provision. Furthermore, they do not provide specific measures or instructions tailored to each user's situation, making it difficult for users to take optimal actions. Furthermore, safety confirmation during disasters is not carried out quickly and accurately. Therefore, there is a need for the development of a system that can provide consistent support, from advance measures to real-time information provision, evacuation instructions, and safety confirmation.

[0682] 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.

[0683] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a hazard map and an evacuation map based on the location information, means for providing the generated hazard map and evacuation map to the user, means for collecting meteorological data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for collecting biometric data from a smart device, means for analyzing the collected biometric data to evaluate the user's safety, and means for notifying the user of the evaluation results. This allows the user to receive a series of support, including advance earthquake-resistance measures, real-time disaster information, evacuation instructions, and even safety confirmation.

[0684] "Means for receiving images taken by a user" refers to a device or software that has the function of transmitting image data taken by a user to a server via a communication means and receiving the data on the server side.

[0685] "Means for analyzing the image and assessing furniture placement and seismic risk" refers to an algorithm or AI model that analyzes the received image data and assesses the furniture placement and seismic risk depicted in the image.

[0686] The "means for recommending earthquake-resistance measures based on the evaluation results" refers to a function that recommends appropriate earthquake-resistance measures to the user based on the evaluation results of furniture layout and earthquake risk.

[0687] The "means for notifying the user of the recommended earthquake-resistance measures" refers to a function for transmitting information on earthquake-resistance measures generated by the server to the user's terminal and notifying the user of the information.

[0688] "Means for receiving user location information" refers to a function that sends data from GPS or other location information acquisition means to a server and receives that data in order to determine the user's current location.

[0689] "Means for generating hazard maps and evacuation maps based on the location information" refers to algorithms or software that generate hazard maps showing disaster risks and evacuation maps showing safe evacuation routes based on the user's location information.

[0690] "Means for providing the generated hazard map and evacuation map to the user" refers to a function for transmitting the generated hazard map and evacuation map to the user's terminal so that the user can view them.

[0691] "Means of collecting meteorological data and disaster information in real time" refers to means of linking with external data sources and APIs to obtain meteorological data and information on disaster occurrence in real time.

[0692] "Means for analyzing the collected data and predicting the occurrence of disasters" refers to algorithms or AI models that analyze meteorological data and disaster information obtained in real time and predict the possibility of future disasters.

[0693] The "means for generating evacuation instructions based on the prediction results" refers to a function for generating information instructing the user on specific evacuation actions based on the prediction results of the occurrence of a disaster.

[0694] The "means for notifying the user of the generated evacuation instructions" refers to a function for transmitting the generated evacuation instructions to the user's terminal and notifying the user of the information.

[0695] "Means for collecting biometric data from smart devices" refers to the ability to obtain a user's biometric data from smart devices such as smartwatches and fitness trackers.

[0696] "Means for analyzing the collected biometric data and assessing the user's safety" refers to algorithms or software for analyzing the acquired biometric data and assessing the user's health condition and safety.

[0697] The "means for notifying the evaluation results" refers to a function for notifying designated other users (e.g., family members or friends) of the safety information obtained by the analysis through a means for notifying the user.

[0698] MODE FOR CARRYING OUT THE INVENTION

[0699] This invention is a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, real-time disaster predictions and evacuation instructions, and automatic safety confirmation during disasters. This system transmits and receives data between a server, a terminal, and users, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[0700] Analysis of furniture layout and earthquake resistance measures

[0701] This function begins when the user uploads photos of their home from their device to the server. The server then passes the received photos to an AI module, which analyzes furniture placement and the risk of it falling over. The AI ​​module can use an "image analysis algorithm" that is commonly used in image analysis services. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and helps them take the necessary measures.

[0702] Examples:

[0703] A user uploads a photo of their living room from their device to the server. The server uses an AI module to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[0704] Example prompt sentence:

[0705] User: "I'll send you a picture of my living room. Can you confirm if this room needs earthquake protection?"

[0706] Hazard and evacuation map information mapping

[0707] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[0708] Examples:

[0709] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[0710] Example prompt sentence:

[0711] User: "What is the nearest evacuation shelter from my current location?"

[0712] Disaster prediction and evacuation instructions based on real-time data

[0713] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The weather data and damage information used by the server can be obtained from a "weather observation system" or "data provider API." The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[0714] Examples:

[0715] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[0716] Example prompt sentence:

[0717] User: "Are there any upcoming weather forecasts and necessary evacuation warnings for this area?"

[0718] Automatic safety confirmation

[0719] With this function, the terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Using an AI-based "biometric data analysis algorithm," a quick and accurate evaluation is possible. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows for quick confirmation of the user's safety.

[0720] Examples:

[0721] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[0722] Example prompt sentence:

[0723] User: "Check my current physical condition based on my heart rate data."

[0724] In this way, the system of the present invention provides comprehensive information and support to enable users to respond to disasters quickly and appropriately. The system provides each function in an integrated manner to ensure the safety and security of users.

[0725] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0726] Analysis of furniture layout and earthquake resistance measures

[0727] Step 1:

[0728] Input: The user takes a photo of their home on their device.

[0729] How it works: A user uses the camera app on their smartphone to take a photo of their living room.

[0730] Output: Captured image data.

[0731] Step 2:

[0732] Input: Captured image data.

[0733] How it works: The user uses a smartphone app to upload this image data to a server.

[0734] Output: Image data sent to the server.

[0735] Step 3:

[0736] Input: Image data sent to the server.

[0737] How it works: The server stores the received image data in Google Cloud Storage and passes the URL to the image analysis service.

[0738] Output: URL of the image data.

[0739] Step 4:

[0740] Input: Image data URL.

[0741] How it works: The server passes the URL to the image analysis service, which performs image analysis to evaluate furniture placement and the associated seismic risk.

[0742] Output: Analysis results (furniture placement and seismic risk information).

[0743] Step 5:

[0744] Input: Analysis results (furniture layout and seismic risk information).

[0745] Operation: The server generates appropriate earthquake resistance measures based on the analysis results.

[0746] Output: Seismic countermeasure recommendations.

[0747] Step 6:

[0748] Input: Seismic resilience recommendations.

[0749] Operation: The server sends the generated earthquake resistance countermeasure information to the terminal.

[0750] Output: Earthquake countermeasure information sent to the device.

[0751] Step 7:

[0752] Input: Earthquake prevention information sent to the device.

[0753] Operation: The device displays earthquake-resistance countermeasure information on the user interface and notifies the user.

[0754] Output: Seismic countermeasure information notified to the user.

[0755] Hazard and evacuation map information mapping

[0756] Step 1:

[0757] Input: The user's current location.

[0758] How it works: A user enables the GPS function on their smartphone and presses the "Send current location" button in a system app.

[0759] Output: Location data sent to the server.

[0760] Step 2:

[0761] Input: Location data sent to the server.

[0762] How it works: The server retrieves location information and uses the Google Maps API to aggregate and process hazard map data for the area.

[0763] Output: Organized hazard map and evacuation map data.

[0764] Step 3:

[0765] Input: Organized hazard and evacuation map data.

[0766] Operation: The server sends the consolidated hazard and evacuation maps to the terminal.

[0767] Output: Hazard and evacuation maps sent to the device.

[0768] Step 4:

[0769] Input: Hazard and evacuation maps sent to the device.

[0770] How it works: The device displays map data to the user and guides them to a safe evacuation route.

[0771] Output: Hazard and evacuation maps displayed to the user.

[0772] Disaster prediction and evacuation instructions based on real-time data

[0773] Step 1:

[0774] Input: Real-time weather data and disaster information.

[0775] How it works: The server collects data in real time from the Japan Meteorological Agency API and other data providers.

[0776] Output: Weather data and disaster information collected on the server.

[0777] Step 2:

[0778] Input: Weather data and disaster information collected on the server.

[0779] How it works: The server inputs this data into an AI model to predict the occurrence of disasters.

[0780] Output: Disaster occurrence prediction results.

[0781] Step 3:

[0782] Input: Disaster occurrence prediction results.

[0783] Operation: The server generates evacuation instructions based on the prediction results.

[0784] Output: Evacuation order information.

[0785] Step 4:

[0786] Input: Evacuation order information.

[0787] Operation: The server sends the generated evacuation instructions to the device.

[0788] Output: Evacuation order information sent to the device.

[0789] Step 5:

[0790] Input: Evacuation order information sent to the device.

[0791] How it works: The device displays a pop-up notification of evacuation instructions, urging the user to take immediate action.

[0792] Output: Evacuation instructions notified to the user.

[0793] Automatic safety confirmation

[0794] Step 1:

[0795] Input: Biometric data from a smart device.

[0796] How it works: The device collects biometric data from the smartwatch using Bluetooth or other means.

[0797] Output: Biometric data collected on the device.

[0798] Step 2:

[0799] Input: Biometric data collected on the device.

[0800] Operation: The device sends the collected data to the server.

[0801] Output: Biometric data sent to the server.

[0802] Step 3:

[0803] Input: Biometric data sent to the server.

[0804] How it works: The server analyzes biometric data and assesses the user's safety.

[0805] Output: Safety assessment results.

[0806] Step 4:

[0807] Input: Safety assessment results.

[0808] Operation: The server generates safety information based on the evaluation results.

[0809] Output: Generated safety information.

[0810] Step 5:

[0811] Input: Generated safety information.

[0812] Operation: The server notifies the generated safety information to other users (e.g., family and friends) who have registered in advance.

[0813] Output: Notified safety information.

[0814] The above is the specific processing flow of this system's program. Each step works together to achieve comprehensive disaster prevention measures and safety confirmation.

[0815] (Application example 1)

[0816] 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."

[0817] Conventional disaster response support systems provide individual functions, such as furniture placement, earthquake-resistance measures, hazard map provision, evacuation instructions, and automatic safety confirmation, making it difficult for users to use them in a centralized manner. It is also difficult for autonomous vehicles to respond in real time to situations that change over time. As a result, optimal information cannot be provided to users to take safe action quickly in the event of a disaster, which can lead to delayed responses. Furthermore, there is a lack of disaster response systems that effectively utilize vehicle location information and biometric data.

[0818] 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.

[0819] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a hazard map and an evacuation map based on the location information, means for providing the generated hazard map and evacuation map to the user, means for collecting meteorological data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for a terminal to transmit vehicle location information to the server and display evacuation information on a vehicle display, means for collecting user biometric data and notifying other users of the user's safety, and means for analyzing the biometric data and safety information and evaluating safety. This enables centralized and real-time provision of information to users so that they can respond quickly and appropriately in the event of a disaster.

[0820] The "means for receiving images" refers to a system or component for transmitting images taken by a user from a terminal to a server and receiving the images.

[0821] "Means for analyzing images" means a system or algorithm that uses an AI module or image processing technology to analyze the information contained in the received images and evaluate specific patterns or risks.

[0822] "Means for assessing furniture placement and seismic risk" refers to a system or process for recognizing the location and placement of furniture based on the results of image analysis and assessing the associated seismic risk.

[0823] The "means for recommending earthquake-resistance measures" is a system or component for presenting optimal measures to users based on the evaluation results.

[0824] "Means for notifying users" refers to a system or application that sends recommended measures and important information to users' terminals and notifies them.

[0825] The "means for receiving location information" refers to a system or component that allows the terminal to obtain current location information (such as GPS data) and transmit it to the server.

[0826] "Means for generating hazard maps and evacuation maps" refers to a system or software that integrates the latest disaster information based on location information to generate appropriate evacuation routes and hazard maps.

[0827] "Means for collecting meteorological data and disaster information in real time" refers to a system or sensor for collecting current meteorological data and disaster information in real time and transmitting them to a server.

[0828] "Means for predicting disaster occurrence" refers to a system or algorithm that analyzes collected data and uses AI or other tools to predict the probability of disaster occurrence and its impact.

[0829] The "means for generating evacuation instructions" is a system or component for creating messages or notifications to instruct users on appropriate evacuation actions based on the prediction results.

[0830] The "means for displaying evacuation information on the vehicle display" is an interface for displaying the evacuation information sent from the server on the vehicle monitor or infotainment system.

[0831] A "means for collecting biometric data" is a system or sensor for collecting data from a smart device (such as a smartwatch) that measures a user's health status or vital signs.

[0832] "Means for notifying other users of safety status" refers to a system or application that analyzes collected biometric data and notifies designated other users (family or friends) of the results of a safety assessment.

[0833] A "means for assessing safety" is a system or algorithm for analyzing collected biometric data and assessing whether a user is safe.

[0834] This invention is an integrated system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, disaster predictions and evacuation instructions based on real-time data, and automatic safety confirmation in the event of a disaster. This system transmits and receives data between four parties: a server, terminals, vehicles, and users, and provides appropriate information and notifications.

[0835] Analysis of furniture layout and earthquake resistance measures

[0836] The system begins when a user takes a picture of their home using a mobile device and uploads it to a server. The server then passes the received image to an AI module, which analyzes furniture placement and the risk of it falling over. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and supports them in taking the necessary measures. For example, if a tall bookshelf is determined to be at high risk of falling over during an earthquake, the device will suggest measures such as "We recommend a kit for securing the bookshelf to the wall."

[0837] Provision of hazard maps and evacuation map information

[0838] The device sends the user's location information to the server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and displays it on the vehicle's display and the user's smartphone. The user can refer to this information to take safe evacuation action.

[0839] Disaster prediction and evacuation instructions based on real-time data

[0840] The server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The device immediately notifies the user and provides guidance on appropriate evacuation routes and evacuation locations. For example, an evacuation instruction may be sent stating, "Evacuation is required in this area immediately," and the route to the nearest evacuation location will be displayed.

[0841] Automatic safety confirmation

[0842] The terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety information during the disaster. This allows the user's safety to be quickly confirmed. For example, if the server receives the user's heart rate data and determines that it is within the normal range, it generates safety information stating "User A is safe" and notifies the family.

[0843] Technical details

[0844] The server includes an AI module, a hazard map generation system, a real-time data collection system, and a notification system. The hardware used includes GPS sensors, smartphones, smartwatches, and autonomous vehicle navigation systems. The software includes Python, image analysis algorithms, and an HTTP communication library (requests).

[0845] Specific examples

[0846] When a user is in a car during heavy rain, the smartphone app acquires location information from the vehicle's GPS sensor and sends it to a server. The server analyzes disaster predictions and evacuation information, and sends appropriate evacuation routes to the vehicle's navigation system. As a result, the navigation screen displays safe evacuation routes updated in real time.

[0847] Example prompts for generative AI models

[0848] "Based on my current location (35.6895, 139.6917), please retrieve the latest evacuation information and hazard map, and generate the optimal evacuation route."

[0849] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0850] Step 1:

[0851] Users take pictures of their homes and upload them to a server using a device (such as a smartphone or tablet). These images become input data for analyzing the furniture layout in their homes.

[0852] Step 2:

[0853] The server receives the images and passes them to the AI ​​module. The AI ​​module analyzes the received images and evaluates the furniture placement and risk of tipping over. As a result of the analysis, it may determine, for example, that a tall bookshelf is at high risk of tipping over during an earthquake. This analysis result becomes the output data.

[0854] Step 3:

[0855] Based on the analysis results, the server recommends appropriate earthquake-resistance measures. For example, it generates a message such as, "We recommend a kit for fixing bookshelves to the wall." This recommended measure becomes the output data.

[0856] Step 4:

[0857] The device receives the recommended measures sent from the server and notifies the user, who can then check the notification and take the necessary measures.

[0858] Step 5:

[0859] Users send location information from their devices to the server, which then becomes the input data for generating the latest hazard and evacuation maps based on their current location information.

[0860] Step 6:

[0861] The server generates the latest hazard and evacuation maps based on the received location information. The generation process integrates the latest data obtained from national and local governments. The generated maps are the output data.

[0862] Step 7:

[0863] The server sends the generated hazard map and evacuation map to the terminal, which displays them on its screen so that the user can check the safe evacuation route.

[0864] Step 8:

[0865] The server collects meteorological data and damage information in real time, which serves as input data for disaster prediction.

[0866] Step 9:

[0867] The server passes the collected data to an AI module, which then predicts the occurrence of disasters. For example, heavy rain is predicted and a high probability of flooding is determined. This prediction result becomes the output data.

[0868] Step 10:

[0869] The server generates evacuation instructions based on the prediction results. For example, it may generate instructions such as "This area requires immediate evacuation." This evacuation instruction becomes the output data.

[0870] Step 11:

[0871] The device receives evacuation instructions sent from the server and immediately notifies the user, who can then check the notification and take appropriate evacuation action.

[0872] Step 12:

[0873] The terminal collects biometric data from the user's smart device (e.g., smartwatch), which serves as input data for assessing the user's health status.

[0874] Step 13:

[0875] The terminal sends the collected biometric data to the server, which analyzes the data and evaluates the user's safety. The evaluation results are output data.

[0876] Step 14:

[0877] Based on the evaluation results, the server notifies other designated users (family and friends) of the user's safety. For example, the server sends an email to the user's family saying, "User A is safe."

[0878] Step 15:

[0879] The device sends the vehicle's location information to a server, which then generates a safe evacuation route based on the location information and displays it on the vehicle's display. The user can then check the safe evacuation route through the navigation system.

[0880] 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.

[0881] This invention is a system that combines a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, disaster predictions and evacuation instructions based on real-time data, and automatic safety confirmation in the event of a disaster, with an emotion engine that recognizes the user's emotions and takes appropriate action based on those emotions. This system transmits and receives data between four parties: a server, a terminal, the emotion engine, and the user, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[0882] Analysis of furniture layout and earthquake resistance measures

[0883] This function begins when the user uploads photos of their home from their device to the server. The server then passes the photos to an AI module, which analyzes furniture placement and the risk of it falling over. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends them to the device. The device then notifies the user and helps them take the necessary measures.

[0884] Examples:

[0885] A user uploads a photo of their living room from their device to the server. The server uses AI to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[0886] Hazard and evacuation map information mapping

[0887] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[0888] Examples:

[0889] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[0890] Disaster prediction and evacuation instructions based on real-time data

[0891] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[0892] Examples:

[0893] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[0894] Automatic safety confirmation

[0895] With this function, the terminal collects biometric data such as heart rate and body temperature from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows the user's safety to be quickly confirmed.

[0896] Examples:

[0897] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[0898] Emotion recognition by emotion engine

[0899] The emotion engine acquires image and voice data of the user and recognizes the user's emotions based on that data. This allows the system to understand the user's emotional state during a disaster and provide appropriate support.

[0900] Examples:

[0901] When a user talks to the device or takes a selfie, the device sends this data to the server. The server's emotion engine analyzes the received data, and if it determines that the user is feeling anxious or scared, the server sends a notification to the device recommending that the user contact a mental health care professional or counseling service. The device notifies the user and helps them get the support they need.

[0902] Evacuation support using an emotion engine

[0903] Based on the emotions recognized, the emotion engine can suggest appropriate measures to reduce stress during evacuation. It can also select and notify the appropriate contact points and evacuation locations depending on the situation.

[0904] Examples:

[0905] If the emotion engine detects strong anxiety or stress in the user while evacuating, the server will use that information to determine that "support staff is needed nearby." The server will send a notification to the support staff at the evacuation site and quickly arrange for support for the user. Additionally, if the emotion engine detects that the user is in a calm emotional state, the server will send a notification that "safety is secured at the current evacuation site," helping the user to feel at ease.

[0906] The above is a specific embodiment of the invention that combines an emotion engine. This system enables users to respond quickly and appropriately in the event of a disaster, managing risks and protecting lives, while also providing appropriate support according to the user's emotional state.

[0907] The processing flow will be explained below.

[0908] Analysis of furniture layout and earthquake resistance measures

[0909] Step 1:

[0910] Users take photos of their home using a smartphone or tablet and upload the photos from the device to the server via the app.

[0911] Step 2:

[0912] The server passes the received photo data to an AI module, which then uses image recognition technology to identify the location, type, height, etc. of the furniture.

[0913] Step 3:

[0914] The server refers to past earthquake data and furniture characteristic data to evaluate the risk of the recognized furniture tipping over.

[0915] Step 4:

[0916] Based on the evaluation results, the server will propose specific earthquake-resistant measures, such as fastening furniture and rearranging it.

[0917] Step 5:

[0918] The server transmits the recommended earthquake-resistance measures to the terminal, which then displays them to the user.

[0919] Hazard and evacuation map information mapping

[0920] Step 1:

[0921] The user allows the app to obtain location information, and the device obtains the current location information from GPS.

[0922] Step 2:

[0923] The location information acquired by the device is sent to the server.

[0924] Step 3:

[0925] The server integrates various hazard map data obtained from national and local governments based on location information, and generates hazard maps and evacuation maps for the target area.

[0926] Step 4:

[0927] The server sends the generated hazard map and evacuation map to the terminal.

[0928] Step 5:

[0929] The device displays hazard maps and evacuation maps to the user and provides evacuation route guidance as needed.

[0930] Disaster prediction and evacuation instructions based on real-time data

[0931] Step 1:

[0932] The server periodically collects real-time data such as weather data, seismograph data, and river water level data.

[0933] Step 2:

[0934] The server passes the collected data to an AI module, which analyzes it and evaluates the prediction of disaster occurrence.

[0935] Step 3:

[0936] The server generates evacuation instructions based on the predicted disaster occurrence results.

[0937] Step 4:

[0938] The server generates evacuation instructions and sends them to the terminal along with information on the optimal evacuation route.

[0939] Step 5:

[0940] The device immediately notifies the user of the evacuation instructions and evacuation route information it receives, urging the user to take evacuation action.

[0941] Automatic safety confirmation

[0942] Step 1:

[0943] The device periodically collects biometric data such as heart rate and body temperature from the user's smart device (e.g., smartwatch).

[0944] Step 2:

[0945] The terminal transmits the collected biometric data to the server.

[0946] Step 3:

[0947] The server analyzes the biometric data and assesses the user's safety.

[0948] Step 4:

[0949] The server generates safety information based on the safety evaluation results and notifies other users (e.g., family and friends) designated in advance.

[0950] Step 5:

[0951] The designated user receives the safety notification from the server and confirms that the user is safe.

[0952] Emotion recognition by emotion engine

[0953] Step 1:

[0954] The user talks to the device and takes a selfie, and the device sends this data to the server.

[0955] Step 2:

[0956] The server uses an emotion engine to analyze the received image and audio data and identify the user's emotional state.

[0957] Step 3:

[0958] The server recommends necessary mental health care and counseling services based on the emotional state identified by the emotion engine.

[0959] Step 4:

[0960] The server notifies the device of recommendations for mental health care and counseling.

[0961] Step 5:

[0962] The device displays the received recommendations to the user, helping them get the support they need.

[0963] Evacuation support using an emotion engine

[0964] Step 1:

[0965] While the user is evacuating, the emotion engine detects strong anxiety or stress, and the device sends this information to the server.

[0966] Step 2:

[0967] The server uses this information to determine the user's condition and whether nearby support staff is needed.

[0968] Step 3:

[0969] If the server determines that a service is needed, it will notify the nearest support staff and arrange for support for the user.

[0970] Step 4:

[0971] The server sends information about the user's evacuation location and, if necessary, other safe evacuation location information to the terminal.

[0972] Step 5:

[0973] The device displays the received evacuation site information to the user, helping the user to feel at ease.

[0974] Example 2

[0975] 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."

[0976] In the event of a disaster, the provision of information for users to take prompt and appropriate evacuation actions was insufficient.In addition, the system was unable to confirm the user's safety or provide support based on the user's emotional state, making it impossible to reduce stress and difficulties during a disaster.

[0977] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving digital images taken by a user, means for analyzing the digital images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a risk map and an evacuation route map based on the location information, means for providing the generated risk map and evacuation route map to the user, means for collecting environmental data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for receiving the user's biometric data and evaluating the user's safety, means for notifying other users of safety information based on the evaluation results, means for analyzing the user's emotional data to recognize the user's emotional state, and means for recommending appropriate support based on the emotional state. This enables users to receive appropriate information and support in the event of a disaster and take evacuation action quickly and safely. It will also be possible to check the user's safety and provide support according to their emotional state, helping to reduce stress and difficulties during disasters.

[0978] "User" refers to an individual or organization that uses the disaster information provision system.

[0979] "Server" refers to the central system that collects, analyzes, manages data, and provides information to users.

[0980] A "terminal" is a device that a user uses to communicate with a server, and includes mobile devices such as smartphones and tablets.

[0981] "Digital image" refers to image data that a user takes using a terminal and uploads to a server.

[0982] "Seismic risk" refers to a parameter that evaluates the degree of damage or danger that the placement of furniture and equipment could cause during an earthquake.

[0983] "Evaluation Results" refers to the analysis data generated after the AI ​​module analyzes the digital image.

[0984] "Earthquake-resistant measures" refers to specific earthquake countermeasure methods proposed to users based on the evaluation results.

[0985] "Location information" refers to the user's current geographical location data obtained using the device's GPS function, etc.

[0986] "Risk map" refers to map data generated based on a user's location information that visually shows the risk of earthquakes and other disasters.

[0987] "Evacuation route map" refers to map data that shows the route from the user's current location to the optimal evacuation site.

[0988] "Environmental Data" means meteorological and other real-time data relating to the environment.

[0989] "Disaster information" refers to information regarding the extent of damage and the extent of impact when a disaster occurs.

[0990] "Disaster occurrence prediction" refers to the AI ​​module predicting the probability of disaster occurrence and its impact based on collected environmental data and damage information.

[0991] "Evacuation instructions" refers to evacuation instructions and recommendations generated by the server based on disaster predictions and issued to users.

[0992] "Biometric data" refers to physical information such as a user's heart rate and body temperature obtained from a smart device or other device.

[0993] "Safety information" refers to information used to evaluate the user's safety based on collected biometric data and to notify other designated users as necessary.

[0994] "Emotional data" refers to data relating to the emotional state of a user that is analyzed based on image and voice data.

[0995] "Emotional state" refers to the user's current mental state as determined by analyzing emotion data.

[0996] This invention is a system that provides users with quick and appropriate evacuation measures during disasters, and provides support based on the user's safety confirmation and emotional state. This system operates in cooperation with a server, terminals, and various data analysis modules.

[0997] First, the user uploads digital images of their home room to the server via their device. The device is equipped with a camera and has the functionality to securely transmit images taken by the user to the server. The server receives the images and uses AI modules (e.g., TensorFlow and PyTorch) to analyze furniture placement and tip-over risk. Based on the analysis results, the server generates earthquake-resistance measures and notifies the device. This allows the user to receive specific instructions for implementing earthquake measures.

[0998] Next, if the user allows location sharing, the device sends GPS data to the server. The server uses this location information to generate an updated risk map and evacuation route map. The risk map is created by integrating information obtained from national and local government databases. The generated map is sent to the device, and the user receives visual guidance on appropriate evacuation actions.

[0999] Furthermore, the server collects environmental data (such as weather information) and damage information in real time and uses AI to predict the occurrence of disasters. If the server determines that there is a high risk of a disaster occurring, it generates evacuation instructions and sends them to the device. The device immediately notifies the user, allowing them to quickly move to the designated evacuation route or evacuation location.

[1000] The terminal also collects biometric data (heart rate, body temperature, etc.) from smart devices (e.g., smartwatches) and sends it to a server. The server analyzes this data and evaluates the user's safety. The evaluation results are notified to other users (e.g., family and friends) designated in advance. This makes it possible to quickly check the user's safety information.

[1001] Finally, the user's emotional data (image and voice data) is also sent to the server via the device. The emotion engine uses this data to analyze the user's emotional state and recommend appropriate support. For example, if the user is experiencing extreme anxiety or fear, the server will notify them to contact a mental health care professional and provide guidance on receiving appropriate counseling services.

[1002] Examples:

[1003] The user takes a photo of their living room onto their device and presses the "Send" button. The device sends the photo data to the server, which analyzes it and determines that "tall bookshelves have a high risk of tipping over." The server generates a countermeasure, such as "Use a kit to secure the bookshelf to the wall," and sends it to the device. The user's device receives a notification, and the user confirms the details.

[1004] Example prompt sentence:

[1005] "I would like to use this system to analyze the earthquake resistance measures of my home and find out what measures are necessary. Please upload a photo of your living room and analyze what risks there are."

[1006] This system analyzes various data and provides users with appropriate information, minimizing risks in the event of a disaster and providing safety and security.

[1007] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1008] Step 1: User takes and uploads a digital image

[1009] Specific operation: The user launches the application on the device, takes a photo of a room such as the living room, or selects an existing photo, and then presses the upload button to send the image data to the server.

[1010] Input: A digital image taken or selected by the user.

[1011] Output: Digital image data sent to the server.

[1012] Step 2: The device sends the digital image data to the server.

[1013] Specific operation: The terminal receives user input and sends image data to the server using a secure protocol (e.g., HTTPS).

[1014] Input: A digital image taken by the user.

[1015] Output: Digital image data sent to the server.

[1016] Step 3: The server receives the digital image and begins analysis.

[1017] Specific operation: The server receives the uploaded image data and passes it to an AI module (e.g., TensorFlow or PyTorch).

[1018] Input: Digital image data sent from the device.

[1019] Output: Image data being analyzed.

[1020] Step 4: The AI ​​module analyzes furniture placement and seismic risk

[1021] How it works: The AI ​​module processes image data, identifies the location and type of furniture, and evaluates the earthquake risk. For example, if a tall bookshelf is not secured, it will determine that there is a high risk of it falling over.

[1022] Input: Digital image data passed by the server.

[1023] Output: Analysis results (furniture placement and seismic risk assessment).

[1024] Step 5: The server generates earthquake-resistance measures based on the analysis results and sends them to the device.

[1025] Specific operation: The server generates earthquake-resistance measures based on the analysis results of the AI ​​module. For example, it creates a specific countermeasure proposal such as "We recommend a kit for fixing bookshelves to the wall" and sends it to the terminal.

[1026] Input: Analysis results from the AI ​​module.

[1027] Output: Generated earthquake countermeasures and notification data to the terminal.

[1028] Step 6: The device notifies the user of earthquake countermeasure information

[1029] Specific operation: The device displays the notification data received from the server and informs the user of the proposed solution. The user can tap the notification to view more information.

[1030] Input: Earthquake countermeasure notification data sent from the server.

[1031] Output: The seismic action notification that is displayed to the user.

[1032] Step 7: The user allows location sharing

[1033] Specific behavior: The user allows location sharing in the application settings.

[1034] Input: User action (allow location sharing).

[1035] Output: Permission settings for the system to obtain location information.

[1036] Step 8: The device sends its location to the server

[1037] Specific operation: The device acquires GPS data in real time and sends it to the server.

[1038] Input: GPS data acquired by the device.

[1039] Output: The location data sent to the server.

[1040] Step 9: The server generates a risk map and evacuation route map based on the location information.

[1041] Specific operation: The server generates the latest risk maps and evacuation route maps from national and local government databases based on the user's location information.

[1042] Input: Location data and map data from government agencies.

[1043] Output: Generated risk map and evacuation route map.

[1044] Step 10: The server sends the generated risk map and evacuation route map to the terminal.

[1045] Specific operation: The server sends the generated map data to the terminal.

[1046] Input: Generated risk map and evacuation route map.

[1047] Output: Risk map and evacuation route map data sent to the terminal.

[1048] Step 11: The device displays map information to the user

[1049] Specific operation: The device displays the received map information so that the user can check it.

[1050] Input: Map data sent from the server.

[1051] Output: Risk map and evacuation route diagram displayed to the user.

[1052] Step 12: Server collects real-time environmental data

[1053] What it does: The server collects real-time environmental data from the Japan Meteorological Agency and other public data sources.

[1054] Input: Environmental data from the Japan Meteorological Agency and data providers.

[1055] Output: Collected real-time environmental data.

[1056] Step 13: The server makes a disaster prediction

[1057] Specific operation: Environmental data collected by the server is input into an AI model to evaluate and predict disaster risks.

[1058] Input: Collected environmental data.

[1059] Output: Disaster prediction results data.

[1060] Step 14: The server generates evacuation instructions based on the risk of disaster occurrence.

[1061] Specific operation: The server generates appropriate evacuation instructions based on the prediction results of the AI ​​model.

[1062] Input: Disaster prediction result data.

[1063] Output: Generated evacuation order data.

[1064] Step 15: The server sends the generated evacuation instructions to the terminal.

[1065] Specific operation: The server sends evacuation instruction data to the terminal.

[1066] Input: Generated evacuation order data.

[1067] Output: Evacuation instruction data sent to the terminal.

[1068] Step 16: The device notifies the user of the evacuation order

[1069] Specific operation: The device immediately notifies the user of the evacuation instruction data it receives.

[1070] Input: Evacuation order data sent from the server.

[1071] Output: Evacuation instructions displayed to the user.

[1072] Step 17: The terminal collects biometric data from the smart device

[1073] Specific operation: The terminal collects biometric data such as heart rate and body temperature from smart devices such as smartwatches.

[1074] Input: Biometric data from smart device.

[1075] output: The collected biometric data.

[1076] Step 18: The device sends the biometric data to the server.

[1077] Specific operation: The terminal sends the collected biometric data to the server.

[1078] Input: Biometric data collected from smart devices.

[1079] Output: Biometric data sent to the server.

[1080] Step 19: The server analyzes the biometric data and evaluates the user's safety.

[1081] Specific operation: The server analyzes the biometric data and evaluates the user's health condition. If it is within the normal range, it is deemed "safe."

[1082] Input: Biometric data sent from the device.

[1083] Output: Safety assessment results.

[1084] Step 20: The server notifies the safety information based on the evaluation result.

[1085] Specific operation: The server notifies other designated users (e.g., family members) of the safety information based on the safety evaluation results.

[1086] Input: Safety assessment results.

[1087] Output: Safety information sent to other users.

[1088] Step 21: The device acquires the user's emotion data and sends it to the server.

[1089] Specific operation: The user talks to the device or takes a selfie. The device sends this emotional data to the server.

[1090] Input: Emotional image and audio data.

[1091] Output: Emotion data sent to the server.

[1092] Step 22: The server analyzes the emotion data and recognizes the emotional state.

[1093] Specific operation: The emotion engine on the server analyzes the received emotion data and recognizes the user's emotional state (e.g., anxiety, fear).

[1094] Input: Emotion data sent from the device.

[1095] Output: Perceived emotional state.

[1096] Step 23: The server recommends appropriate assistance based on the emotional state.

[1097] Specific operation: Based on the recognized emotional state, the server generates a notification recommending appropriate support measures (e.g., contacting a mental health professional) and sends it to the device.

[1098] Input: Perceived emotional state.

[1099] Output: The generated help recommendation notification.

[1100] Step 24: The device displays a support recommendation notification to the user.

[1101] Specific operation: The device displays the support recommendation notification received from the server to the user so that appropriate support can be provided.

[1102] Input: Help recommendation notification sent from the server.

[1103] Output: Help recommendation notification displayed to the user.

[1104] (Application example 2)

[1105] 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."

[1106] During disasters, safe evacuation, accurate information provision, and rapid safety confirmation are important. However, conventional systems do not take into account real-time weather data or the user's emotional state, making it difficult to provide optimal evacuation instructions and support. Food delivery services also require rapid safety confirmation and appropriate support based on the user's emotional state during disasters, but there has been a lack of systems that comprehensively address these needs. Therefore, a new system is needed to ensure safety during disasters and provide support based on the user's emotional state.

[1107] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1108] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake resistance measures, means for collecting meteorological data and biometric data, means for analyzing the collected meteorological data to predict the occurrence of a disaster, means for analyzing the user's image data to evaluate the user's emotional state and provide appropriate support, and means for transmitting safety information to others registered by the user. This makes it possible to ensure safety and provide appropriate support based on the user's emotional state and biometric data even in the event of a disaster.

[1109] "Images taken by a user" refers to still image or video data taken by a user using a terminal.

[1110] "Furniture arrangement" is information that indicates the position and arrangement of furniture in a room.

[1111] "Seismic risk" is an index used to evaluate the risk of furniture tipping over or moving in the event of an earthquake.

[1112] "Earthquake-resistant measures" are measures and procedures to minimize damage to furniture and structures in the event of an earthquake.

[1113] "Weather data" refers to data that includes information about weather forecasts and current weather conditions.

[1114] "Biometric data" refers to data relating to the user's physical condition, such as heart rate and body temperature.

[1115] "Disaster prediction" is the process of analyzing the possibility of disasters occurring based on meteorological data and other environmental information.

[1116] "Emotional state" refers to the psychological state of the user that can be detected from facial expressions, tone of voice, etc.

[1117] "Safety information" is information for confirming the safety of a user in the event of a disaster or emergency.

[1118] A "hazard map" is a map that shows the potential damage that may occur in the event of a natural disaster.

[1119] An "evacuation map" is a map that shows safe evacuation routes and evacuation locations in the event of a disaster.

[1120] An "evacuation instruction" is a message that instructs the user to evacuate to a safe place when a disaster occurs.

[1121] This invention is a system for ensuring the safety of users in the event of a disaster and providing appropriate support promptly. This system has the function of receiving and analyzing images taken by the user and evaluating furniture placement and earthquake resistance risks. It can also collect meteorological data and biometric data to predict the occurrence of disasters. Furthermore, it uses an emotion engine to evaluate the user's emotional state and provide appropriate support based on that. It can also send safety information to others registered by the user.

[1122] Hardware and Software

[1123] Hardware: Smartphones, smartwatches

[1124] Software: Applications (Android / iOS), Google Maps API, Twilio SMS API, emotion engine (e.g., Affectiva SDK)

[1125] Program processing explanation

[1126] 1. Receiving and analyzing images taken by the user:

[1127] Users take pictures of their rooms with their smartphones and upload them to the server, which then uses AI modules to analyze the images and assess furniture placement and seismic risks. If necessary, the server recommends earthquake-resistance measures and notifies the user.

[1128] 2. Meteorological and biometric data collection:

[1129] Weather and biometric data are collected in real time from smartphones and smartwatches. The server analyzes the weather data and predicts the occurrence of disasters. At the same time, it analyzes the biometric data and evaluates the user's safety.

[1130] 3. Emotional assessment and support:

[1131] The system sends the user's image and voice data to the emotion engine to evaluate their emotional state, and if the user is in a stressful state, generates a notification to provide appropriate assistance.

[1132] 4. Provision of hazard maps and evacuation maps:

[1133] The server receives the user's location information and generates updated hazard and evacuation maps based on that location. These maps are provided to the user, guiding them to safe evacuation routes.

[1134] 5. Sending safety information:

[1135] The server sends safety information based on the user's biometric data to other people registered by the user, allowing the user's safety to be quickly confirmed.

[1136] Specific examples

[1137] A user takes a photo of their living room with their smartphone and uploads it to the server. The server uses AI to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then recommends a countermeasure, such as "We recommend a kit to secure the bookshelf to the wall," and sends a notification to the smartphone.

[1138] At the same time, the server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and notifies the smartphone. The smartphone then notifies the user of this information and displays a route to the nearest evacuation site.

[1139] Additionally, if a user is wearing a smartwatch, biometric data such as heart rate data is collected in real time. The server analyzes the biometric data, and if there are no abnormalities, it generates safety information stating "User A is safe" and notifies the user's family. The family receives this information and confirms that the user is safe.

[1140] Prompt Sentence Examples

[1141] The task is to obtain data from a weather forecast API and send evacuation instructions via SMS if heavy rain is predicted. It also analyzes the user's image data using an emotion engine and sends a support message if the user is in a stressful state. Specific examples of prompts are as follows:

[1142] plaintext

[1143] Prerequisites:

[1144] 1. A delivery staff member in Tokyo uses a smartphone and a smartwatch.

[1145] 2. Use weather forecast data and emotion engines to provide guidance and support during disasters.

[1146] task:

[1147] 1. Get data from the weather forecast API and send evacuation instructions via SMS if heavy rain is predicted.

[1148] 2. Analyze the user's image data with an emotion engine and send a supportive message if the user is in a stressful state.

[1149] question:

[1150] 1. Configure the API to retrieve weather forecast data.

[1151] 2. Provide an example of code to send important evacuation instructions via SMS.

[1152] 3. Give an example of the process of analyzing image data with an emotion engine.

[1153] As described above, the system of the present invention can be implemented. This system ensures safety and provides appropriate support based on the user's emotional state and biometric data even during a disaster.

[1154] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1155] Step 1:

[1156] The server receives images of the room taken by the user. The user takes an image of the room with their smartphone and uploads this image data to the server. The input is the image data taken with the user's smartphone, and the output is the image data stored on the server.

[1157] Step 2:

[1158] The server analyzes the received image data and evaluates furniture placement and seismic risk. It uses an AI module to perform image analysis and identify the location and placement of furniture that is prone to tipping over. The input is the image data received in step 1, and the output is the furniture placement information and seismic risk assessment as the analysis results.

[1159] Step 3:

[1160] The server recommends appropriate earthquake-resistance measures based on the analysis results. For example, it generates a message such as "We recommend a kit for fixing bookshelves to the wall." The input is the analysis results from step 2, and the output is the recommended measures.

[1161] Step 4:

[1162] The server notifies the user of the recommended countermeasure information. It sends the recommended message to the user's smartphone and displays it to the user. The input is the recommended countermeasure information generated in step 3, and the output is the message displayed on the user's smartphone.

[1163] Step 5:

[1164] The device collects weather data and biometric data and sends them to the server. The weather data is obtained from a weather forecast API, and the biometric data is collected from the smartwatch. The input is the weather data API response and the biometric data from the smartwatch, and the output is the weather data and biometric data sent to the server.

[1165] Step 6:

[1166] The server analyzes the collected weather data and predicts the occurrence of disasters. It analyzes the weather data and evaluates the risk of heavy rain and earthquakes. The input is the weather data from step 5, and the output is the predicted disaster occurrence results.

[1167] Step 7:

[1168] The server sends evacuation instructions to the user based on the disaster prediction results. For example, it generates a message saying, "Evacuation is required in this area immediately," and sends it to the user's smartphone. The input is the prediction result in step 6, and the output is the evacuation instruction message displayed on the user's smartphone.

[1169] Step 8:

[1170] The server sends the user's image data to the emotion engine to evaluate their emotional state. The engine analyzes the image and audio data sent by the user and determines whether they are under stress. The input is the user's image and audio data, and the output is the evaluation result of their emotional state.

[1171] Step 9:

[1172] The server generates a message offering appropriate support based on the evaluation result of the emotional state and notifies the user. For example, it generates a message such as "If the user is feeling stressed, we recommend counseling services." The input is the evaluation result of the emotional state in step 8, and the output is the support message displayed on the user's smartphone.

[1173] Step 10:

[1174] The server sends safety information to other people registered by the user. It analyzes biometric data and notifies the user of the results of its safety assessment via email or message. The input is the analysis result of the biometric data, and the output is a safety information message sent to the registered devices of other people.

[1175] 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.

[1176] 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.

[1177] 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.

[1178] [Third embodiment]

[1179] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1180] 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.

[1181] 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).

[1182] 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.

[1183] 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.

[1184] 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).

[1185] 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.

[1186] 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.

[1187] 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.

[1188] 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.

[1189] 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.

[1190] 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."

[1191] This invention is a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, real-time disaster predictions and evacuation instructions, and automatic safety confirmation during disasters. This system transmits and receives data between a server, a terminal, and users, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[1192] Analysis of furniture layout and earthquake resistance measures

[1193] This function begins when the user uploads photos of their home from their device to the server. The server then passes the photos to an AI module, which analyzes furniture placement and the risk of furniture tipping over. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and helps them take the necessary measures.

[1194] Examples:

[1195] A user uploads a photo of their living room from their device to the server. The server uses AI to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[1196] Hazard and evacuation map information mapping

[1197] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[1198] Examples:

[1199] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[1200] Disaster prediction and evacuation instructions based on real-time data

[1201] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[1202] Examples:

[1203] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[1204] Automatic safety confirmation

[1205] With this function, the terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows the user's safety to be quickly confirmed.

[1206] Examples:

[1207] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[1208] As described above, the system of the present invention provides an integrated set of functions to help users respond quickly and appropriately in the event of a disaster, thereby contributing to reducing disaster risks and protecting lives.

[1209] The processing flow will be explained below.

[1210] Analysis of furniture layout and earthquake resistance measures

[1211] Step 1:

[1212] Users take photos of their home using a smartphone or tablet and upload the photos from the device to the server via the app.

[1213] Step 2:

[1214] The server passes the received photo data to an AI module, which then uses image recognition technology to identify the location, type, height, etc. of the furniture.

[1215] Step 3:

[1216] The server refers to past earthquake data and furniture characteristic data to evaluate the risk of the recognized furniture tipping over.

[1217] Step 4:

[1218] Based on the evaluation results, the server will propose specific earthquake-resistant measures, such as fastening furniture and rearranging it.

[1219] Step 5:

[1220] The server transmits the recommended earthquake-resistance measures to the terminal, which then displays them to the user.

[1221] Hazard and evacuation map information mapping

[1222] Step 1:

[1223] The user allows the app to obtain location information, and the device obtains the current location information from GPS.

[1224] Step 2:

[1225] The location information acquired by the device is sent to the server.

[1226] Step 3:

[1227] The server integrates various hazard map data obtained from national and local governments based on location information, and generates hazard maps and evacuation maps for the target area.

[1228] Step 4:

[1229] The server sends the generated hazard map and evacuation map to the terminal.

[1230] Step 5:

[1231] The device displays hazard maps and evacuation maps to the user and provides evacuation route guidance as needed.

[1232] Disaster prediction and evacuation instructions based on real-time data

[1233] Step 1:

[1234] The server periodically collects real-time data such as weather data, seismograph data, and river water level data.

[1235] Step 2:

[1236] The server passes the collected data to an AI module, which analyzes it to assess the possibility of a disaster occurring.

[1237] Step 3:

[1238] If the server determines that there is a high possibility of a disaster occurring, it generates appropriate evacuation instructions.

[1239] Step 4:

[1240] The server generates evacuation instructions and sends them to the terminal along with information on the optimal evacuation route.

[1241] Step 5:

[1242] The device immediately notifies the user of evacuation instructions and evacuation route information received, encouraging evacuation action.

[1243] Automatic safety confirmation

[1244] Step 1:

[1245] The device periodically collects biometric data such as heart rate and body temperature from the user's smart device (e.g., smartwatch).

[1246] Step 2:

[1247] The terminal transmits the collected biometric data to the server.

[1248] Step 3:

[1249] The server analyzes the biometric data and assesses the user's safety.

[1250] Step 4:

[1251] The server generates safety information based on the safety evaluation results and notifies other users (e.g., family and friends) designated in advance.

[1252] Step 5:

[1253] Family and friends (designated users) receive safety notifications from the server and confirm that the user is safe.

[1254] The above is the specific processing flow for each function. This system enables users to respond quickly and appropriately in the event of a disaster, thereby managing risks and protecting lives.

[1255] Example 1

[1256] 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."

[1257] In recent years, the frequent occurrence of natural disasters has created a need for safety measures in individual homes and local communities. However, current disaster prevention systems are overly focused on responding to disasters after they occur, and therefore lack advance measures and real-time information provision. Furthermore, they do not provide specific measures or instructions tailored to each user's situation, making it difficult for users to take optimal actions. Furthermore, safety confirmation during disasters is not carried out quickly and accurately. Therefore, there is a need for the development of a system that can provide consistent support, from advance measures to real-time information provision, evacuation instructions, and safety confirmation.

[1258] 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.

[1259] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a hazard map and an evacuation map based on the location information, means for providing the generated hazard map and evacuation map to the user, means for collecting meteorological data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for collecting biometric data from a smart device, means for analyzing the collected biometric data to evaluate the user's safety, and means for notifying the user of the evaluation results. This allows the user to receive a series of support, including advance earthquake-resistance measures, real-time disaster information, evacuation instructions, and even safety confirmation.

[1260] "Means for receiving images taken by a user" refers to a device or software that has the function of transmitting image data taken by a user to a server via a communication means and receiving the data on the server side.

[1261] "Means for analyzing the image and assessing furniture placement and seismic risk" refers to an algorithm or AI model that analyzes the received image data and assesses the furniture placement and seismic risk depicted in the image.

[1262] The "means for recommending earthquake-resistance measures based on the evaluation results" refers to a function that recommends appropriate earthquake-resistance measures to the user based on the evaluation results of furniture layout and earthquake risk.

[1263] The "means for notifying the user of the recommended earthquake-resistance measures" refers to a function for transmitting information on earthquake-resistance measures generated by the server to the user's terminal and notifying the user of the information.

[1264] "Means for receiving user location information" refers to a function that sends data from GPS or other location information acquisition means to a server and receives that data in order to determine the user's current location.

[1265] "Means for generating hazard maps and evacuation maps based on the location information" refers to algorithms or software that generate hazard maps showing disaster risks and evacuation maps showing safe evacuation routes based on the user's location information.

[1266] "Means for providing the generated hazard map and evacuation map to the user" refers to a function for transmitting the generated hazard map and evacuation map to the user's terminal so that the user can view them.

[1267] "Means of collecting meteorological data and disaster information in real time" refers to means of linking with external data sources and APIs to obtain meteorological data and information on disaster occurrence in real time.

[1268] "Means for analyzing the collected data and predicting the occurrence of disasters" refers to algorithms or AI models that analyze meteorological data and disaster information obtained in real time and predict the possibility of future disasters.

[1269] The "means for generating evacuation instructions based on the prediction results" refers to a function for generating information instructing the user on specific evacuation actions based on the prediction results of the occurrence of a disaster.

[1270] The "means for notifying the user of the generated evacuation instructions" refers to a function for transmitting the generated evacuation instructions to the user's terminal and notifying the user of the information.

[1271] "Means for collecting biometric data from smart devices" refers to the ability to obtain a user's biometric data from smart devices such as smartwatches and fitness trackers.

[1272] "Means for analyzing the collected biometric data and assessing the user's safety" refers to algorithms or software for analyzing the acquired biometric data and assessing the user's health condition and safety.

[1273] The "means for notifying the evaluation results" refers to a function for notifying designated other users (e.g., family members or friends) of the safety information obtained by the analysis through a means for notifying the user.

[1274] MODE FOR CARRYING OUT THE INVENTION

[1275] This invention is a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, real-time disaster predictions and evacuation instructions, and automatic safety confirmation during disasters. This system transmits and receives data between a server, a terminal, and users, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[1276] Analysis of furniture layout and earthquake resistance measures

[1277] This function begins when the user uploads photos of their home from their device to the server. The server then passes the received photos to an AI module, which analyzes furniture placement and the risk of it falling over. The AI ​​module can use an "image analysis algorithm" that is commonly used in image analysis services. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and helps them take the necessary measures.

[1278] Examples:

[1279] A user uploads a photo of their living room from their device to the server. The server uses an AI module to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[1280] Example prompt sentence:

[1281] User: "I'll send you a picture of my living room. Can you confirm if this room needs earthquake protection?"

[1282] Hazard and evacuation map information mapping

[1283] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[1284] Examples:

[1285] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[1286] Example prompt sentence:

[1287] User: "What is the nearest evacuation shelter from my current location?"

[1288] Disaster prediction and evacuation instructions based on real-time data

[1289] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The weather data and damage information used by the server can be obtained from a "weather observation system" or "data provider API." The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[1290] Examples:

[1291] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[1292] Example prompt sentence:

[1293] User: "Are there any upcoming weather forecasts and necessary evacuation warnings for this area?"

[1294] Automatic safety confirmation

[1295] With this function, the terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Using an AI-based "biometric data analysis algorithm," a quick and accurate evaluation is possible. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows for quick confirmation of the user's safety.

[1296] Examples:

[1297] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[1298] Example prompt sentence:

[1299] User: "Check my current physical condition based on my heart rate data."

[1300] In this way, the system of the present invention provides comprehensive information and support to enable users to respond to disasters quickly and appropriately. The system provides each function in an integrated manner to ensure the safety and security of users.

[1301] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1302] Analysis of furniture layout and earthquake resistance measures

[1303] Step 1:

[1304] Input: The user takes a photo of their home on their device.

[1305] How it works: A user uses the camera app on their smartphone to take a photo of their living room.

[1306] Output: Captured image data.

[1307] Step 2:

[1308] Input: Captured image data.

[1309] How it works: The user uses a smartphone app to upload this image data to a server.

[1310] Output: Image data sent to the server.

[1311] Step 3:

[1312] Input: Image data sent to the server.

[1313] How it works: The server stores the received image data in Google Cloud Storage and passes the URL to the image analysis service.

[1314] Output: URL of the image data.

[1315] Step 4:

[1316] Input: Image data URL.

[1317] How it works: The server passes the URL to the image analysis service, which performs image analysis to evaluate furniture placement and the associated seismic risk.

[1318] Output: Analysis results (furniture placement and seismic risk information).

[1319] Step 5:

[1320] Input: Analysis results (furniture layout and seismic risk information).

[1321] Operation: The server generates appropriate earthquake resistance measures based on the analysis results.

[1322] Output: Seismic countermeasure recommendations.

[1323] Step 6:

[1324] Input: Seismic resilience recommendations.

[1325] Operation: The server sends the generated earthquake resistance countermeasure information to the terminal.

[1326] Output: Earthquake countermeasure information sent to the device.

[1327] Step 7:

[1328] Input: Earthquake prevention information sent to the device.

[1329] Operation: The device displays earthquake-resistance countermeasure information on the user interface and notifies the user.

[1330] Output: Seismic countermeasure information notified to the user.

[1331] Hazard and evacuation map information mapping

[1332] Step 1:

[1333] Input: The user's current location.

[1334] How it works: A user enables the GPS function on their smartphone and presses the "Send current location" button in a system app.

[1335] Output: Location data sent to the server.

[1336] Step 2:

[1337] Input: Location data sent to the server.

[1338] How it works: The server retrieves location information and uses the Google Maps API to aggregate and process hazard map data for the area.

[1339] Output: Organized hazard map and evacuation map data.

[1340] Step 3:

[1341] Input: Organized hazard and evacuation map data.

[1342] Operation: The server sends the consolidated hazard and evacuation maps to the terminal.

[1343] Output: Hazard and evacuation maps sent to the device.

[1344] Step 4:

[1345] Input: Hazard and evacuation maps sent to the device.

[1346] How it works: The device displays map data to the user and guides them to a safe evacuation route.

[1347] Output: Hazard and evacuation maps displayed to the user.

[1348] Disaster prediction and evacuation instructions based on real-time data

[1349] Step 1:

[1350] Input: Real-time weather data and disaster information.

[1351] How it works: The server collects data in real time from the Japan Meteorological Agency API and other data providers.

[1352] Output: Weather data and disaster information collected on the server.

[1353] Step 2:

[1354] Input: Weather data and disaster information collected on the server.

[1355] How it works: The server inputs this data into an AI model to predict the occurrence of disasters.

[1356] Output: Disaster occurrence prediction results.

[1357] Step 3:

[1358] Input: Disaster occurrence prediction results.

[1359] Operation: The server generates evacuation instructions based on the prediction results.

[1360] Output: Evacuation order information.

[1361] Step 4:

[1362] Input: Evacuation order information.

[1363] Operation: The server sends the generated evacuation instructions to the device.

[1364] Output: Evacuation order information sent to the device.

[1365] Step 5:

[1366] Input: Evacuation order information sent to the device.

[1367] How it works: The device displays a pop-up notification of evacuation instructions, urging the user to take immediate action.

[1368] Output: Evacuation instructions notified to the user.

[1369] Automatic safety confirmation

[1370] Step 1:

[1371] Input: Biometric data from a smart device.

[1372] How it works: The device collects biometric data from the smartwatch using Bluetooth or other means.

[1373] Output: Biometric data collected on the device.

[1374] Step 2:

[1375] Input: Biometric data collected on the device.

[1376] Operation: The device sends the collected data to the server.

[1377] Output: Biometric data sent to the server.

[1378] Step 3:

[1379] Input: Biometric data sent to the server.

[1380] How it works: The server analyzes biometric data and assesses the user's safety.

[1381] Output: Safety assessment results.

[1382] Step 4:

[1383] Input: Safety assessment results.

[1384] Operation: The server generates safety information based on the evaluation results.

[1385] Output: Generated safety information.

[1386] Step 5:

[1387] Input: Generated safety information.

[1388] Operation: The server notifies the generated safety information to other users (e.g., family and friends) who have registered in advance.

[1389] Output: Notified safety information.

[1390] The above is the specific processing flow of this system's program. Each step works together to achieve comprehensive disaster prevention measures and safety confirmation.

[1391] (Application example 1)

[1392] 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."

[1393] Conventional disaster response support systems provide individual functions, such as furniture placement, earthquake-resistance measures, hazard map provision, evacuation instructions, and automatic safety confirmation, making it difficult for users to use them in a centralized manner. It is also difficult for autonomous vehicles to respond in real time to situations that change over time. As a result, optimal information cannot be provided to users to take safe action quickly in the event of a disaster, which can lead to delayed responses. Furthermore, there is a lack of disaster response systems that effectively utilize vehicle location information and biometric data.

[1394] 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.

[1395] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a hazard map and an evacuation map based on the location information, means for providing the generated hazard map and evacuation map to the user, means for collecting meteorological data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for a terminal to transmit vehicle location information to the server and display evacuation information on a vehicle display, means for collecting user biometric data and notifying other users of the user's safety, and means for analyzing the biometric data and safety information and evaluating safety. This enables centralized and real-time provision of information to users so that they can respond quickly and appropriately in the event of a disaster.

[1396] The "means for receiving images" refers to a system or component for transmitting images taken by a user from a terminal to a server and receiving the images.

[1397] "Means for analyzing images" means a system or algorithm that uses an AI module or image processing technology to analyze the information contained in the received images and evaluate specific patterns or risks.

[1398] "Means for assessing furniture placement and seismic risk" refers to a system or process for recognizing the location and placement of furniture based on the results of image analysis and assessing the associated seismic risk.

[1399] The "means for recommending earthquake-resistance measures" is a system or component for presenting optimal measures to users based on the evaluation results.

[1400] "Means for notifying users" refers to a system or application that sends recommended measures and important information to users' terminals and notifies them.

[1401] The "means for receiving location information" refers to a system or component that allows the terminal to obtain current location information (such as GPS data) and transmit it to the server.

[1402] "Means for generating hazard maps and evacuation maps" refers to a system or software that integrates the latest disaster information based on location information to generate appropriate evacuation routes and hazard maps.

[1403] "Means for collecting meteorological data and disaster information in real time" refers to a system or sensor for collecting current meteorological data and disaster information in real time and transmitting them to a server.

[1404] "Means for predicting disaster occurrence" refers to a system or algorithm that analyzes collected data and uses AI or other tools to predict the probability of disaster occurrence and its impact.

[1405] The "means for generating evacuation instructions" is a system or component for creating messages or notifications to instruct users on appropriate evacuation actions based on the prediction results.

[1406] The "means for displaying evacuation information on the vehicle display" is an interface for displaying the evacuation information sent from the server on the vehicle monitor or infotainment system.

[1407] A "means for collecting biometric data" is a system or sensor for collecting data from a smart device (such as a smartwatch) that measures a user's health status or vital signs.

[1408] "Means for notifying other users of safety status" refers to a system or application that analyzes collected biometric data and notifies designated other users (family or friends) of the results of a safety assessment.

[1409] A "means for assessing safety" is a system or algorithm for analyzing collected biometric data and assessing whether a user is safe.

[1410] This invention is an integrated system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, disaster predictions and evacuation instructions based on real-time data, and automatic safety confirmation in the event of a disaster. This system transmits and receives data between four parties: a server, terminals, vehicles, and users, and provides appropriate information and notifications.

[1411] Analysis of furniture layout and earthquake resistance measures

[1412] The system begins when a user takes a picture of their home using a mobile device and uploads it to a server. The server then passes the received image to an AI module, which analyzes furniture placement and the risk of it falling over. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and supports them in taking the necessary measures. For example, if a tall bookshelf is determined to be at high risk of falling over during an earthquake, the device will suggest measures such as "We recommend a kit for securing the bookshelf to the wall."

[1413] Provision of hazard maps and evacuation map information

[1414] The device sends the user's location information to the server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and displays it on the vehicle's display and the user's smartphone. The user can refer to this information to take safe evacuation action.

[1415] Disaster prediction and evacuation instructions based on real-time data

[1416] The server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The device immediately notifies the user and provides guidance on appropriate evacuation routes and evacuation locations. For example, an evacuation instruction may be sent stating, "Evacuation is required in this area immediately," and the route to the nearest evacuation location will be displayed.

[1417] Automatic safety confirmation

[1418] The terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety information during the disaster. This allows the user's safety to be quickly confirmed. For example, if the server receives the user's heart rate data and determines that it is within the normal range, it generates safety information stating "User A is safe" and notifies the family.

[1419] Technical details

[1420] The server includes an AI module, a hazard map generation system, a real-time data collection system, and a notification system. The hardware used includes GPS sensors, smartphones, smartwatches, and autonomous vehicle navigation systems. The software includes Python, image analysis algorithms, and an HTTP communication library (requests).

[1421] Specific examples

[1422] When a user is in a car during heavy rain, the smartphone app acquires location information from the vehicle's GPS sensor and sends it to a server. The server analyzes disaster predictions and evacuation information, and sends appropriate evacuation routes to the vehicle's navigation system. As a result, the navigation screen displays safe evacuation routes updated in real time.

[1423] Example prompts for generative AI models

[1424] "Based on my current location (35.6895, 139.6917), please retrieve the latest evacuation information and hazard map, and generate the optimal evacuation route."

[1425] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1426] Step 1:

[1427] Users take pictures of their homes and upload them to a server using a device (such as a smartphone or tablet). These images become input data for analyzing the furniture layout in their homes.

[1428] Step 2:

[1429] The server receives the images and passes them to the AI ​​module. The AI ​​module analyzes the received images and evaluates the furniture placement and risk of tipping over. As a result of the analysis, it may determine, for example, that a tall bookshelf is at high risk of tipping over during an earthquake. This analysis result becomes the output data.

[1430] Step 3:

[1431] Based on the analysis results, the server recommends appropriate earthquake-resistance measures. For example, it generates a message such as, "We recommend a kit for fixing bookshelves to the wall." This recommended measure becomes the output data.

[1432] Step 4:

[1433] The device receives the recommended measures sent from the server and notifies the user, who can then check the notification and take the necessary measures.

[1434] Step 5:

[1435] Users send location information from their devices to the server, which then becomes the input data for generating the latest hazard and evacuation maps based on their current location information.

[1436] Step 6:

[1437] The server generates the latest hazard and evacuation maps based on the received location information. The generation process integrates the latest data obtained from national and local governments. The generated maps are the output data.

[1438] Step 7:

[1439] The server sends the generated hazard map and evacuation map to the terminal, which displays them on its screen so that the user can check the safe evacuation route.

[1440] Step 8:

[1441] The server collects meteorological data and damage information in real time, which serves as input data for disaster prediction.

[1442] Step 9:

[1443] The server passes the collected data to an AI module, which then predicts the occurrence of disasters. For example, heavy rain is predicted and a high probability of flooding is determined. This prediction result becomes the output data.

[1444] Step 10:

[1445] The server generates evacuation instructions based on the prediction results. For example, it may generate instructions such as "This area requires immediate evacuation." This evacuation instruction becomes the output data.

[1446] Step 11:

[1447] The device receives evacuation instructions sent from the server and immediately notifies the user, who can then check the notification and take appropriate evacuation action.

[1448] Step 12:

[1449] The terminal collects biometric data from the user's smart device (e.g., smartwatch), which serves as input data for assessing the user's health status.

[1450] Step 13:

[1451] The terminal sends the collected biometric data to the server, which analyzes the data and evaluates the user's safety. The evaluation results are output data.

[1452] Step 14:

[1453] Based on the evaluation results, the server notifies other designated users (family and friends) of the user's safety. For example, the server sends an email to the user's family saying, "User A is safe."

[1454] Step 15:

[1455] The device sends the vehicle's location information to a server, which then generates a safe evacuation route based on the location information and displays it on the vehicle's display. The user can then check the safe evacuation route through the navigation system.

[1456] 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.

[1457] This invention is a system that combines a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, disaster predictions and evacuation instructions based on real-time data, and automatic safety confirmation in the event of a disaster, with an emotion engine that recognizes the user's emotions and takes appropriate action based on those emotions. This system transmits and receives data between four parties: a server, a terminal, the emotion engine, and the user, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[1458] Analysis of furniture layout and earthquake resistance measures

[1459] This function begins when the user uploads photos of their home from their device to the server. The server then passes the photos to an AI module, which analyzes furniture placement and the risk of it falling over. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends them to the device. The device then notifies the user and helps them take the necessary measures.

[1460] Examples:

[1461] A user uploads a photo of their living room from their device to the server. The server uses AI to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[1462] Hazard and evacuation map information mapping

[1463] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[1464] Examples:

[1465] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[1466] Disaster prediction and evacuation instructions based on real-time data

[1467] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[1468] Examples:

[1469] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[1470] Automatic safety confirmation

[1471] With this function, the terminal collects biometric data such as heart rate and body temperature from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows the user's safety to be quickly confirmed.

[1472] Examples:

[1473] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[1474] Emotion recognition by emotion engine

[1475] The emotion engine acquires image and voice data of the user and recognizes the user's emotions based on that data. This allows the system to understand the user's emotional state during a disaster and provide appropriate support.

[1476] Examples:

[1477] When a user talks to the device or takes a selfie, the device sends this data to the server. The server's emotion engine analyzes the received data, and if it determines that the user is feeling anxious or scared, the server sends a notification to the device recommending that the user contact a mental health care professional or counseling service. The device notifies the user and helps them get the support they need.

[1478] Evacuation support using an emotion engine

[1479] Based on the emotions recognized, the emotion engine can suggest appropriate measures to reduce stress during evacuation. It can also select and notify the appropriate contact points and evacuation locations depending on the situation.

[1480] Examples:

[1481] If the emotion engine detects strong anxiety or stress in the user while evacuating, the server will use that information to determine that "support staff is needed nearby." The server will send a notification to the support staff at the evacuation site and quickly arrange for support for the user. Additionally, if the emotion engine detects that the user is in a calm emotional state, the server will send a notification that "safety is secured at the current evacuation site," helping the user to feel at ease.

[1482] The above is a specific embodiment of the invention that combines an emotion engine. This system enables users to respond quickly and appropriately in the event of a disaster, managing risks and protecting lives, while also providing appropriate support according to the user's emotional state.

[1483] The processing flow will be explained below.

[1484] Analysis of furniture layout and earthquake resistance measures

[1485] Step 1:

[1486] Users take photos of their home using a smartphone or tablet and upload the photos from the device to the server via the app.

[1487] Step 2:

[1488] The server passes the received photo data to an AI module, which then uses image recognition technology to identify the location, type, height, etc. of the furniture.

[1489] Step 3:

[1490] The server refers to past earthquake data and furniture characteristic data to evaluate the risk of the recognized furniture tipping over.

[1491] Step 4:

[1492] Based on the evaluation results, the server will propose specific earthquake-resistant measures, such as fastening furniture and rearranging it.

[1493] Step 5:

[1494] The server transmits the recommended earthquake-resistance measures to the terminal, which then displays them to the user.

[1495] Hazard and evacuation map information mapping

[1496] Step 1:

[1497] The user allows the app to obtain location information, and the device obtains the current location information from GPS.

[1498] Step 2:

[1499] The location information acquired by the device is sent to the server.

[1500] Step 3:

[1501] The server integrates various hazard map data obtained from national and local governments based on location information, and generates hazard maps and evacuation maps for the target area.

[1502] Step 4:

[1503] The server sends the generated hazard map and evacuation map to the terminal.

[1504] Step 5:

[1505] The device displays hazard maps and evacuation maps to the user and provides evacuation route guidance as needed.

[1506] Disaster prediction and evacuation instructions based on real-time data

[1507] Step 1:

[1508] The server periodically collects real-time data such as weather data, seismograph data, and river water level data.

[1509] Step 2:

[1510] The server passes the collected data to an AI module, which analyzes it and evaluates the prediction of disaster occurrence.

[1511] Step 3:

[1512] The server generates evacuation instructions based on the predicted disaster occurrence results.

[1513] Step 4:

[1514] The server generates evacuation instructions and sends them to the terminal along with information on the optimal evacuation route.

[1515] Step 5:

[1516] The device immediately notifies the user of the evacuation instructions and evacuation route information it receives, urging the user to take evacuation action.

[1517] Automatic safety confirmation

[1518] Step 1:

[1519] The device periodically collects biometric data such as heart rate and body temperature from the user's smart device (e.g., smartwatch).

[1520] Step 2:

[1521] The terminal transmits the collected biometric data to the server.

[1522] Step 3:

[1523] The server analyzes the biometric data and assesses the user's safety.

[1524] Step 4:

[1525] The server generates safety information based on the safety evaluation results and notifies other users (e.g., family and friends) designated in advance.

[1526] Step 5:

[1527] The designated user receives the safety notification from the server and confirms that the user is safe.

[1528] Emotion recognition by emotion engine

[1529] Step 1:

[1530] The user talks to the device and takes a selfie, and the device sends this data to the server.

[1531] Step 2:

[1532] The server uses an emotion engine to analyze the received image and audio data and identify the user's emotional state.

[1533] Step 3:

[1534] The server recommends necessary mental health care and counseling services based on the emotional state identified by the emotion engine.

[1535] Step 4:

[1536] The server notifies the device of recommendations for mental health care and counseling.

[1537] Step 5:

[1538] The device displays the received recommendations to the user, helping them get the support they need.

[1539] Evacuation support using an emotion engine

[1540] Step 1:

[1541] While the user is evacuating, the emotion engine detects strong anxiety or stress, and the device sends this information to the server.

[1542] Step 2:

[1543] The server uses this information to determine the user's condition and whether nearby support staff is needed.

[1544] Step 3:

[1545] If the server determines that a service is needed, it will notify the nearest support staff and arrange for support for the user.

[1546] Step 4:

[1547] The server sends information about the user's evacuation location and, if necessary, other safe evacuation location information to the terminal.

[1548] Step 5:

[1549] The device displays the received evacuation site information to the user, helping the user to feel at ease.

[1550] Example 2

[1551] 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."

[1552] In the event of a disaster, the provision of information for users to take prompt and appropriate evacuation actions was insufficient.In addition, the system was unable to confirm the user's safety or provide support based on the user's emotional state, making it impossible to reduce stress and difficulties during a disaster.

[1553] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving digital images taken by a user, means for analyzing the digital images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a risk map and an evacuation route map based on the location information, means for providing the generated risk map and evacuation route map to the user, means for collecting environmental data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for receiving the user's biometric data and evaluating the user's safety, means for notifying other users of safety information based on the evaluation results, means for analyzing the user's emotional data to recognize the user's emotional state, and means for recommending appropriate support based on the emotional state. This enables users to receive appropriate information and support in the event of a disaster and take evacuation action quickly and safely. It will also be possible to check the user's safety and provide support according to their emotional state, helping to reduce stress and difficulties during disasters.

[1554] "User" refers to an individual or organization that uses the disaster information provision system.

[1555] "Server" refers to the central system that collects, analyzes, manages data, and provides information to users.

[1556] A "terminal" is a device that a user uses to communicate with a server, and includes mobile devices such as smartphones and tablets.

[1557] "Digital image" refers to image data that a user takes using a terminal and uploads to a server.

[1558] "Seismic risk" refers to a parameter that evaluates the degree of damage or danger that the placement of furniture and equipment could cause during an earthquake.

[1559] "Evaluation Results" refers to the analysis data generated after the AI ​​module analyzes the digital image.

[1560] "Earthquake-resistant measures" refers to specific earthquake countermeasure methods proposed to users based on the evaluation results.

[1561] "Location information" refers to the user's current geographical location data obtained using the device's GPS function, etc.

[1562] "Risk map" refers to map data generated based on a user's location information that visually shows the risk of earthquakes and other disasters.

[1563] "Evacuation route map" refers to map data that shows the route from the user's current location to the optimal evacuation site.

[1564] "Environmental Data" means meteorological and other real-time data relating to the environment.

[1565] "Disaster information" refers to information regarding the extent of damage and the extent of impact when a disaster occurs.

[1566] "Disaster occurrence prediction" refers to the AI ​​module predicting the probability of disaster occurrence and its impact based on collected environmental data and damage information.

[1567] "Evacuation instructions" refers to evacuation instructions and recommendations generated by the server based on disaster predictions and issued to users.

[1568] "Biometric data" refers to physical information such as a user's heart rate and body temperature obtained from a smart device or other device.

[1569] "Safety information" refers to information used to evaluate the user's safety based on collected biometric data and to notify other designated users as necessary.

[1570] "Emotional data" refers to data relating to the emotional state of a user that is analyzed based on image and voice data.

[1571] "Emotional state" refers to the user's current mental state as determined by analyzing emotion data.

[1572] This invention is a system that provides users with quick and appropriate evacuation measures during disasters, and provides support based on the user's safety confirmation and emotional state. This system operates in cooperation with a server, terminals, and various data analysis modules.

[1573] First, the user uploads digital images of their home room to the server via their device. The device is equipped with a camera and has the functionality to securely transmit images taken by the user to the server. The server receives the images and uses AI modules (e.g., TensorFlow and PyTorch) to analyze furniture placement and tip-over risk. Based on the analysis results, the server generates earthquake-resistance measures and notifies the device. This allows the user to receive specific instructions for implementing earthquake measures.

[1574] Next, if the user allows location sharing, the device sends GPS data to the server. The server uses this location information to generate an updated risk map and evacuation route map. The risk map is created by integrating information obtained from national and local government databases. The generated map is sent to the device, and the user receives visual guidance on appropriate evacuation actions.

[1575] Furthermore, the server collects environmental data (such as weather information) and damage information in real time and uses AI to predict the occurrence of disasters. If the server determines that there is a high risk of a disaster occurring, it generates evacuation instructions and sends them to the device. The device immediately notifies the user, allowing them to quickly move to the designated evacuation route or evacuation location.

[1576] The terminal also collects biometric data (heart rate, body temperature, etc.) from smart devices (e.g., smartwatches) and sends it to a server. The server analyzes this data and evaluates the user's safety. The evaluation results are notified to other users (e.g., family and friends) designated in advance. This makes it possible to quickly check the user's safety information.

[1577] Finally, the user's emotional data (image and voice data) is also sent to the server via the device. The emotion engine uses this data to analyze the user's emotional state and recommend appropriate support. For example, if the user is experiencing extreme anxiety or fear, the server will notify them to contact a mental health care professional and provide guidance on receiving appropriate counseling services.

[1578] Examples:

[1579] The user takes a photo of their living room onto their device and presses the "Send" button. The device sends the photo data to the server, which analyzes it and determines that "tall bookshelves have a high risk of tipping over." The server generates a countermeasure, such as "Use a kit to secure the bookshelf to the wall," and sends it to the device. The user's device receives a notification, and the user confirms the details.

[1580] Example prompt sentence:

[1581] "I would like to use this system to analyze the earthquake resistance measures of my home and find out what measures are necessary. Please upload a photo of your living room and analyze what risks there are."

[1582] This system analyzes various data and provides users with appropriate information, minimizing risks in the event of a disaster and providing safety and security.

[1583] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1584] Step 1: User takes and uploads a digital image

[1585] Specific operation: The user launches the application on the device, takes a photo of a room such as the living room, or selects an existing photo, and then presses the upload button to send the image data to the server.

[1586] Input: A digital image taken or selected by the user.

[1587] Output: Digital image data sent to the server.

[1588] Step 2: The device sends the digital image data to the server.

[1589] Specific operation: The terminal receives user input and sends image data to the server using a secure protocol (e.g., HTTPS).

[1590] Input: A digital image taken by the user.

[1591] Output: Digital image data sent to the server.

[1592] Step 3: The server receives the digital image and begins analysis.

[1593] Specific operation: The server receives the uploaded image data and passes it to an AI module (e.g., TensorFlow or PyTorch).

[1594] Input: Digital image data sent from the device.

[1595] Output: Image data being analyzed.

[1596] Step 4: The AI ​​module analyzes furniture placement and seismic risk

[1597] How it works: The AI ​​module processes image data, identifies the location and type of furniture, and evaluates the earthquake risk. For example, if a tall bookshelf is not secured, it will determine that there is a high risk of it falling over.

[1598] Input: Digital image data passed by the server.

[1599] Output: Analysis results (furniture placement and seismic risk assessment).

[1600] Step 5: The server generates earthquake-resistance measures based on the analysis results and sends them to the device.

[1601] Specific operation: The server generates earthquake-resistance measures based on the analysis results of the AI ​​module. For example, it creates a specific countermeasure proposal such as "We recommend a kit for fixing bookshelves to the wall" and sends it to the terminal.

[1602] Input: Analysis results from the AI ​​module.

[1603] Output: Generated earthquake countermeasures and notification data to the terminal.

[1604] Step 6: The device notifies the user of earthquake countermeasure information

[1605] Specific operation: The device displays the notification data received from the server and informs the user of the proposed solution. The user can tap the notification to view more information.

[1606] Input: Earthquake countermeasure notification data sent from the server.

[1607] Output: The seismic action notification that is displayed to the user.

[1608] Step 7: The user allows location sharing

[1609] Specific behavior: The user allows location sharing in the application settings.

[1610] Input: User action (allow location sharing).

[1611] Output: Permission settings for the system to obtain location information.

[1612] Step 8: The device sends its location to the server

[1613] Specific operation: The device acquires GPS data in real time and sends it to the server.

[1614] Input: GPS data acquired by the device.

[1615] Output: The location data sent to the server.

[1616] Step 9: The server generates a risk map and evacuation route map based on the location information.

[1617] Specific operation: The server generates the latest risk maps and evacuation route maps from national and local government databases based on the user's location information.

[1618] Input: Location data and map data from government agencies.

[1619] Output: Generated risk map and evacuation route map.

[1620] Step 10: The server sends the generated risk map and evacuation route map to the terminal.

[1621] Specific operation: The server sends the generated map data to the terminal.

[1622] Input: Generated risk map and evacuation route map.

[1623] Output: Risk map and evacuation route map data sent to the terminal.

[1624] Step 11: The device displays map information to the user

[1625] Specific operation: The device displays the received map information so that the user can check it.

[1626] Input: Map data sent from the server.

[1627] Output: Risk map and evacuation route diagram displayed to the user.

[1628] Step 12: Server collects real-time environmental data

[1629] What it does: The server collects real-time environmental data from the Japan Meteorological Agency and other public data sources.

[1630] Input: Environmental data from the Japan Meteorological Agency and data providers.

[1631] Output: Collected real-time environmental data.

[1632] Step 13: The server makes a disaster prediction

[1633] Specific operation: Environmental data collected by the server is input into an AI model to evaluate and predict disaster risks.

[1634] Input: Collected environmental data.

[1635] Output: Disaster prediction results data.

[1636] Step 14: The server generates evacuation instructions based on the risk of disaster occurrence.

[1637] Specific operation: The server generates appropriate evacuation instructions based on the prediction results of the AI ​​model.

[1638] Input: Disaster prediction result data.

[1639] Output: Generated evacuation order data.

[1640] Step 15: The server sends the generated evacuation instructions to the terminal.

[1641] Specific operation: The server sends evacuation instruction data to the terminal.

[1642] Input: Generated evacuation order data.

[1643] Output: Evacuation instruction data sent to the terminal.

[1644] Step 16: The device notifies the user of the evacuation order

[1645] Specific operation: The device immediately notifies the user of the evacuation instruction data it receives.

[1646] Input: Evacuation order data sent from the server.

[1647] Output: Evacuation instructions displayed to the user.

[1648] Step 17: The terminal collects biometric data from the smart device

[1649] Specific operation: The terminal collects biometric data such as heart rate and body temperature from smart devices such as smartwatches.

[1650] Input: Biometric data from smart device.

[1651] output: The collected biometric data.

[1652] Step 18: The device sends the biometric data to the server.

[1653] Specific operation: The terminal sends the collected biometric data to the server.

[1654] Input: Biometric data collected from smart devices.

[1655] Output: Biometric data sent to the server.

[1656] Step 19: The server analyzes the biometric data and evaluates the user's safety.

[1657] Specific operation: The server analyzes the biometric data and evaluates the user's health condition. If it is within the normal range, it is deemed "safe."

[1658] Input: Biometric data sent from the device.

[1659] Output: Safety assessment results.

[1660] Step 20: The server notifies the safety information based on the evaluation result.

[1661] Specific operation: The server notifies other designated users (e.g., family members) of the safety information based on the safety evaluation results.

[1662] Input: Safety assessment results.

[1663] Output: Safety information sent to other users.

[1664] Step 21: The device acquires the user's emotion data and sends it to the server.

[1665] Specific operation: The user talks to the device or takes a selfie. The device sends this emotional data to the server.

[1666] Input: Emotional image and audio data.

[1667] Output: Emotion data sent to the server.

[1668] Step 22: The server analyzes the emotion data and recognizes the emotional state.

[1669] Specific operation: The emotion engine on the server analyzes the received emotion data and recognizes the user's emotional state (e.g., anxiety, fear).

[1670] Input: Emotion data sent from the device.

[1671] Output: Perceived emotional state.

[1672] Step 23: The server recommends appropriate assistance based on the emotional state.

[1673] Specific operation: Based on the recognized emotional state, the server generates a notification recommending appropriate support measures (e.g., contacting a mental health professional) and sends it to the device.

[1674] Input: Perceived emotional state.

[1675] Output: The generated help recommendation notification.

[1676] Step 24: The device displays a support recommendation notification to the user.

[1677] Specific operation: The device displays the support recommendation notification received from the server to the user so that appropriate support can be provided.

[1678] Input: Help recommendation notification sent from the server.

[1679] Output: Help recommendation notification displayed to the user.

[1680] (Application example 2)

[1681] 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."

[1682] During disasters, safe evacuation, accurate information provision, and rapid safety confirmation are important. However, conventional systems do not take into account real-time weather data or the user's emotional state, making it difficult to provide optimal evacuation instructions and support. Food delivery services also require rapid safety confirmation and appropriate support based on the user's emotional state during disasters, but there has been a lack of systems that comprehensively address these needs. Therefore, a new system is needed to ensure safety during disasters and provide support based on the user's emotional state.

[1683] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1684] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake resistance measures, means for collecting meteorological data and biometric data, means for analyzing the collected meteorological data to predict the occurrence of a disaster, means for analyzing the user's image data to evaluate the user's emotional state and provide appropriate support, and means for transmitting safety information to others registered by the user. This makes it possible to ensure safety and provide appropriate support based on the user's emotional state and biometric data even in the event of a disaster.

[1685] "Images taken by a user" refers to still image or video data taken by a user using a terminal.

[1686] "Furniture arrangement" is information that indicates the position and arrangement of furniture in a room.

[1687] "Seismic risk" is an index used to evaluate the risk of furniture tipping over or moving in the event of an earthquake.

[1688] "Earthquake-resistant measures" are measures and procedures to minimize damage to furniture and structures in the event of an earthquake.

[1689] "Weather data" refers to data that includes information about weather forecasts and current weather conditions.

[1690] "Biometric data" refers to data relating to the user's physical condition, such as heart rate and body temperature.

[1691] "Disaster prediction" is the process of analyzing the possibility of disasters occurring based on meteorological data and other environmental information.

[1692] "Emotional state" refers to the psychological state of the user that can be detected from facial expressions, tone of voice, etc.

[1693] "Safety information" is information for confirming the safety of a user in the event of a disaster or emergency.

[1694] A "hazard map" is a map that shows the potential damage that may occur in the event of a natural disaster.

[1695] An "evacuation map" is a map that shows safe evacuation routes and evacuation locations in the event of a disaster.

[1696] An "evacuation instruction" is a message that instructs the user to evacuate to a safe place when a disaster occurs.

[1697] This invention is a system for ensuring the safety of users in the event of a disaster and providing appropriate support promptly. This system has the function of receiving and analyzing images taken by the user and evaluating furniture placement and earthquake resistance risks. It can also collect meteorological data and biometric data to predict the occurrence of disasters. Furthermore, it uses an emotion engine to evaluate the user's emotional state and provide appropriate support based on that. It can also send safety information to others registered by the user.

[1698] Hardware and Software

[1699] Hardware: Smartphones, smartwatches

[1700] Software: Applications (Android / iOS), Google Maps API, Twilio SMS API, emotion engine (e.g., Affectiva SDK)

[1701] Program processing explanation

[1702] 1. Receiving and analyzing images taken by the user:

[1703] Users take pictures of their rooms with their smartphones and upload them to the server, which then uses AI modules to analyze the images and assess furniture placement and seismic risks. If necessary, the server recommends earthquake-resistance measures and notifies the user.

[1704] 2. Meteorological and biometric data collection:

[1705] Weather and biometric data are collected in real time from smartphones and smartwatches. The server analyzes the weather data and predicts the occurrence of disasters. At the same time, it analyzes the biometric data and evaluates the user's safety.

[1706] 3. Emotional assessment and support:

[1707] The system sends the user's image and voice data to the emotion engine to evaluate their emotional state, and if the user is in a stressful state, generates a notification to provide appropriate assistance.

[1708] 4. Provision of hazard maps and evacuation maps:

[1709] The server receives the user's location information and generates updated hazard and evacuation maps based on that location. These maps are provided to the user, guiding them to safe evacuation routes.

[1710] 5. Sending safety information:

[1711] The server sends safety information based on the user's biometric data to other people registered by the user, allowing the user's safety to be quickly confirmed.

[1712] Specific examples

[1713] A user takes a photo of their living room with their smartphone and uploads it to the server. The server uses AI to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then recommends a countermeasure, such as "We recommend a kit to secure the bookshelf to the wall," and sends a notification to the smartphone.

[1714] At the same time, the server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and notifies the smartphone. The smartphone then notifies the user of this information and displays a route to the nearest evacuation site.

[1715] Additionally, if a user is wearing a smartwatch, biometric data such as heart rate data is collected in real time. The server analyzes the biometric data, and if there are no abnormalities, it generates safety information stating "User A is safe" and notifies the user's family. The family receives this information and confirms that the user is safe.

[1716] Prompt Sentence Examples

[1717] The task is to obtain data from a weather forecast API and send evacuation instructions via SMS if heavy rain is predicted. It also analyzes the user's image data using an emotion engine and sends a support message if the user is in a stressful state. Specific examples of prompts are as follows:

[1718] plaintext

[1719] Prerequisites:

[1720] 1. A delivery staff member in Tokyo uses a smartphone and a smartwatch.

[1721] 2. Use weather forecast data and emotion engines to provide guidance and support during disasters.

[1722] task:

[1723] 1. Get data from the weather forecast API and send evacuation instructions via SMS if heavy rain is predicted.

[1724] 2. Analyze the user's image data with an emotion engine and send a supportive message if the user is in a stressful state.

[1725] question:

[1726] 1. Configure the API to retrieve weather forecast data.

[1727] 2. Provide an example of code to send important evacuation instructions via SMS.

[1728] 3. Give an example of the process of analyzing image data with an emotion engine.

[1729] As described above, the system of the present invention can be implemented. This system ensures safety and provides appropriate support based on the user's emotional state and biometric data even during a disaster.

[1730] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1731] Step 1:

[1732] The server receives images of the room taken by the user. The user takes an image of the room with their smartphone and uploads this image data to the server. The input is the image data taken with the user's smartphone, and the output is the image data stored on the server.

[1733] Step 2:

[1734] The server analyzes the received image data and evaluates furniture placement and seismic risk. It uses an AI module to perform image analysis and identify the location and placement of furniture that is prone to tipping over. The input is the image data received in step 1, and the output is the furniture placement information and seismic risk assessment as the analysis results.

[1735] Step 3:

[1736] The server recommends appropriate earthquake-resistance measures based on the analysis results. For example, it generates a message such as "We recommend a kit for fixing bookshelves to the wall." The input is the analysis results from step 2, and the output is the recommended measures.

[1737] Step 4:

[1738] The server notifies the user of the recommended countermeasure information. It sends the recommended message to the user's smartphone and displays it to the user. The input is the recommended countermeasure information generated in step 3, and the output is the message displayed on the user's smartphone.

[1739] Step 5:

[1740] The device collects weather data and biometric data and sends them to the server. The weather data is obtained from a weather forecast API, and the biometric data is collected from the smartwatch. The input is the weather data API response and the biometric data from the smartwatch, and the output is the weather data and biometric data sent to the server.

[1741] Step 6:

[1742] The server analyzes the collected weather data and predicts the occurrence of disasters. It analyzes the weather data and evaluates the risk of heavy rain and earthquakes. The input is the weather data from step 5, and the output is the predicted disaster occurrence results.

[1743] Step 7:

[1744] The server sends evacuation instructions to the user based on the disaster prediction results. For example, it generates a message saying, "Evacuation is required in this area immediately," and sends it to the user's smartphone. The input is the prediction result in step 6, and the output is the evacuation instruction message displayed on the user's smartphone.

[1745] Step 8:

[1746] The server sends the user's image data to the emotion engine to evaluate their emotional state. The engine analyzes the image and audio data sent by the user and determines whether they are under stress. The input is the user's image and audio data, and the output is the evaluation result of their emotional state.

[1747] Step 9:

[1748] The server generates a message offering appropriate support based on the evaluation result of the emotional state and notifies the user. For example, it generates a message such as "If the user is feeling stressed, we recommend counseling services." The input is the evaluation result of the emotional state in step 8, and the output is the support message displayed on the user's smartphone.

[1749] Step 10:

[1750] The server sends safety information to other people registered by the user. It analyzes biometric data and notifies the user of the results of its safety assessment via email or message. The input is the analysis result of the biometric data, and the output is a safety information message sent to the registered devices of other people.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] [Fourth embodiment]

[1755] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1756] 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.

[1757] 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).

[1758] 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.

[1759] 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.

[1760] 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).

[1761] 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.

[1762] 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.

[1763] 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.

[1764] 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.

[1765] 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.

[1766] 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.

[1767] 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."

[1768] This invention is a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, real-time disaster predictions and evacuation instructions, and automatic safety confirmation during disasters. This system transmits and receives data between a server, a terminal, and users, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[1769] Analysis of furniture layout and earthquake resistance measures

[1770] This function begins when the user uploads photos of their home from their device to the server. The server then passes the photos to an AI module, which analyzes furniture placement and the risk of furniture tipping over. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and helps them take the necessary measures.

[1771] Examples:

[1772] A user uploads a photo of their living room from their device to the server. The server uses AI to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[1773] Hazard and evacuation map information mapping

[1774] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[1775] Examples:

[1776] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[1777] Disaster prediction and evacuation instructions based on real-time data

[1778] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[1779] Examples:

[1780] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[1781] Automatic safety confirmation

[1782] With this function, the terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows the user's safety to be quickly confirmed.

[1783] Examples:

[1784] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[1785] As described above, the system of the present invention provides an integrated set of functions to help users respond quickly and appropriately in the event of a disaster, thereby contributing to reducing disaster risks and protecting lives.

[1786] The processing flow will be explained below.

[1787] Analysis of furniture layout and earthquake resistance measures

[1788] Step 1:

[1789] Users take photos of their home using a smartphone or tablet and upload the photos from the device to the server via the app.

[1790] Step 2:

[1791] The server passes the received photo data to an AI module, which then uses image recognition technology to identify the location, type, height, etc. of the furniture.

[1792] Step 3:

[1793] The server refers to past earthquake data and furniture characteristic data to evaluate the risk of the recognized furniture tipping over.

[1794] Step 4:

[1795] Based on the evaluation results, the server will propose specific earthquake-resistant measures, such as fastening furniture and rearranging it.

[1796] Step 5:

[1797] The server transmits the recommended earthquake-resistance measures to the terminal, which then displays them to the user.

[1798] Hazard and evacuation map information mapping

[1799] Step 1:

[1800] The user allows the app to obtain location information, and the device obtains the current location information from GPS.

[1801] Step 2:

[1802] The location information acquired by the device is sent to the server.

[1803] Step 3:

[1804] The server integrates various hazard map data obtained from national and local governments based on location information, and generates hazard maps and evacuation maps for the target area.

[1805] Step 4:

[1806] The server sends the generated hazard map and evacuation map to the terminal.

[1807] Step 5:

[1808] The device displays hazard maps and evacuation maps to the user and provides evacuation route guidance as needed.

[1809] Disaster prediction and evacuation instructions based on real-time data

[1810] Step 1:

[1811] The server periodically collects real-time data such as weather data, seismograph data, and river water level data.

[1812] Step 2:

[1813] The server passes the collected data to an AI module, which analyzes it to assess the possibility of a disaster occurring.

[1814] Step 3:

[1815] If the server determines that there is a high possibility of a disaster occurring, it generates appropriate evacuation instructions.

[1816] Step 4:

[1817] The server generates evacuation instructions and sends them to the terminal along with information on the optimal evacuation route.

[1818] Step 5:

[1819] The device immediately notifies the user of evacuation instructions and evacuation route information received, encouraging evacuation action.

[1820] Automatic safety confirmation

[1821] Step 1:

[1822] The device periodically collects biometric data such as heart rate and body temperature from the user's smart device (e.g., smartwatch).

[1823] Step 2:

[1824] The terminal transmits the collected biometric data to the server.

[1825] Step 3:

[1826] The server analyzes the biometric data and assesses the user's safety.

[1827] Step 4:

[1828] The server generates safety information based on the safety evaluation results and notifies other users (e.g., family and friends) designated in advance.

[1829] Step 5:

[1830] Family and friends (designated users) receive safety notifications from the server and confirm that the user is safe.

[1831] The above is the specific processing flow for each function. This system enables users to respond quickly and appropriately in the event of a disaster, thereby managing risks and protecting lives.

[1832] Example 1

[1833] 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."

[1834] In recent years, the frequent occurrence of natural disasters has created a need for safety measures in individual homes and local communities. However, current disaster prevention systems are overly focused on responding to disasters after they occur, and therefore lack advance measures and real-time information provision. Furthermore, they do not provide specific measures or instructions tailored to each user's situation, making it difficult for users to take optimal actions. Furthermore, safety confirmation during disasters is not carried out quickly and accurately. Therefore, there is a need for the development of a system that can provide consistent support, from advance measures to real-time information provision, evacuation instructions, and safety confirmation.

[1835] 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.

[1836] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a hazard map and an evacuation map based on the location information, means for providing the generated hazard map and evacuation map to the user, means for collecting meteorological data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for collecting biometric data from a smart device, means for analyzing the collected biometric data to evaluate the user's safety, and means for notifying the user of the evaluation results. This allows the user to receive a series of support, including advance earthquake-resistance measures, real-time disaster information, evacuation instructions, and even safety confirmation.

[1837] "Means for receiving images taken by a user" refers to a device or software that has the function of transmitting image data taken by a user to a server via a communication means and receiving the data on the server side.

[1838] "Means for analyzing the image and assessing furniture placement and seismic risk" refers to an algorithm or AI model that analyzes the received image data and assesses the furniture placement and seismic risk depicted in the image.

[1839] The "means for recommending earthquake-resistance measures based on the evaluation results" refers to a function that recommends appropriate earthquake-resistance measures to the user based on the evaluation results of furniture layout and earthquake risk.

[1840] The "means for notifying the user of the recommended earthquake-resistance measures" refers to a function for transmitting information on earthquake-resistance measures generated by the server to the user's terminal and notifying the user of the information.

[1841] "Means for receiving user location information" refers to a function that sends data from GPS or other location information acquisition means to a server and receives that data in order to determine the user's current location.

[1842] "Means for generating hazard maps and evacuation maps based on the location information" refers to algorithms or software that generate hazard maps showing disaster risks and evacuation maps showing safe evacuation routes based on the user's location information.

[1843] "Means for providing the generated hazard map and evacuation map to the user" refers to a function for transmitting the generated hazard map and evacuation map to the user's terminal so that the user can view them.

[1844] "Means of collecting meteorological data and disaster information in real time" refers to means of linking with external data sources and APIs to obtain meteorological data and information on disaster occurrence in real time.

[1845] "Means for analyzing the collected data and predicting the occurrence of disasters" refers to algorithms or AI models that analyze meteorological data and disaster information obtained in real time and predict the possibility of future disasters.

[1846] The "means for generating evacuation instructions based on the prediction results" refers to a function for generating information instructing the user on specific evacuation actions based on the prediction results of the occurrence of a disaster.

[1847] The "means for notifying the user of the generated evacuation instructions" refers to a function for transmitting the generated evacuation instructions to the user's terminal and notifying the user of the information.

[1848] "Means for collecting biometric data from smart devices" refers to the ability to obtain a user's biometric data from smart devices such as smartwatches and fitness trackers.

[1849] "Means for analyzing the collected biometric data and assessing the user's safety" refers to algorithms or software for analyzing the acquired biometric data and assessing the user's health condition and safety.

[1850] The "means for notifying the evaluation results" refers to a function for notifying designated other users (e.g., family members or friends) of the safety information obtained by the analysis through a means for notifying the user.

[1851] MODE FOR CARRYING OUT THE INVENTION

[1852] This invention is a system that provides users with earthquake-resistant furniture arrangements, hazard maps and evacuation map information, real-time disaster predictions and evacuation instructions, and automatic safety confirmation during disasters. This system transmits and receives data between a server, a terminal, and users, and provides appropriate information and notifications. The processing flow of the entire system is explained in detail below.

[1853] Analysis of furniture layout and earthquake resistance measures

[1854] This function begins when the user uploads photos of their home from their device to the server. The server then passes the received photos to an AI module, which analyzes furniture placement and the risk of it falling over. The AI ​​module can use an "image analysis algorithm" that is commonly used in image analysis services. Based on the analysis results, the server recommends appropriate earthquake-resistance measures and sends this information to the device. The device then notifies the user and helps them take the necessary measures.

[1855] Examples:

[1856] A user uploads a photo of their living room from their device to the server. The server uses an AI module to determine that a tall bookshelf is at high risk of falling over during an earthquake. The server then sends a recommendation to the device, recommending a kit to secure the bookshelf to the wall, and the device notifies the user.

[1857] Example prompt sentence:

[1858] User: "I'll send you a picture of my living room. Can you confirm if this room needs earthquake protection?"

[1859] Hazard and evacuation map information mapping

[1860] With this function, the device sends the user's location information to a server, which then generates appropriate hazard and evacuation maps based on the location information. The server then integrates the latest hazard map data obtained from national and local governments and provides it to the user. The generated maps are displayed on the device, allowing the user to refer to them and take safe evacuation actions.

[1861] Examples:

[1862] When the user goes out with their device, the app sends their location information to the server. The server generates an updated hazard map based on the location information and calculates the optimal route to the evacuation shelter. The device displays this to the user, allowing them to confirm the route they should take to evacuate.

[1863] Example prompt sentence:

[1864] User: "What is the nearest evacuation shelter from my current location?"

[1865] Disaster prediction and evacuation instructions based on real-time data

[1866] With this function, the server collects weather data and damage information in real time and uses AI to predict the occurrence of disasters. If there is a possibility of a disaster, the server immediately generates evacuation instructions and sends them to the device. The weather data and damage information used by the server can be obtained from a "weather observation system" or "data provider API." The device immediately notifies the user, who can then receive instructions on evacuation routes and evacuation locations.

[1867] Examples:

[1868] The server collects weather data and predicts heavy rain. If it determines that there is a high possibility of heavy rain, the server generates an evacuation instruction stating, "Evacuation is required in this area immediately," and sends it to the device. The device then notifies the user of this information and displays a route to the nearest evacuation site.

[1869] Example prompt sentence:

[1870] User: "Are there any upcoming weather forecasts and necessary evacuation warnings for this area?"

[1871] Automatic safety confirmation

[1872] With this function, the terminal collects biometric data from a smart device (e.g., a smartwatch) and sends it to a server. The server analyzes the biometric data and evaluates the user's safety. Using an AI-based "biometric data analysis algorithm," a quick and accurate evaluation is possible. Based on the evaluation results, the server notifies other designated users (e.g., family and friends) of the user's safety during a disaster. This allows for quick confirmation of the user's safety.

[1873] Examples:

[1874] The device receives the user's heart rate data from the smartwatch and sends it to the server. The server analyzes the received data and determines that it is within the normal range. The server then generates safety information stating "User A is safe" and sends an email to the user's family. The family receives this and confirms that the user is safe.

[1875] Example prompt sentence:

[1876] User: "Check my current physical condition based on my heart rate data."

[1877] In this way, the system of the present invention provides comprehensive information and support to enable users to respond to disasters quickly and appropriately. The system provides each function in an integrated manner to ensure the safety and security of users.

[1878] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1879] Analysis of furniture layout and earthquake resistance measures

[1880] Step 1:

[1881] Input: The user takes a photo of their home on their device.

[1882] How it works: A user uses the camera app on their smartphone to take a photo of their living room.

[1883] Output: Captured image data.

[1884] Step 2:

[1885] Input: Captured image data.

[1886] How it works: The user uses a smartphone app to upload this image data to a server.

[1887] Output: Image data sent to the server.

[1888] Step 3:

[1889] Input: Image data sent to the server.

[1890] How it works: The server stores the received image data in Google Cloud Storage and passes the URL to the image analysis service.

[1891] Output: URL of the image data.

[1892] Step 4:

[1893] Input: Image data URL.

[1894] How it works: The server passes the URL to the image analysis service, which performs image analysis to evaluate furniture placement and the associated seismic risk.

[1895] Output: Analysis results (furniture placement and seismic risk information).

[1896] Step 5:

[1897] Input: Analysis results (furniture layout and seismic risk information).

[1898] Operation: The server generates appropriate earthquake resistance measures based on the analysis results.

[1899] Output: Seismic countermeasure recommendations.

[1900] Step 6:

[1901] Input: Seismic resilience recommendations.

[1902] Operation: The server sends the generated earthquake resistance countermeasure information to the terminal.

[1903] Output: Earthquake countermeasure information sent to the device.

[1904] Step 7:

[1905] Input: Earthquake prevention information sent to the device.

[1906] Operation: The device displays earthquake-resistance countermeasure information on the user interface and notifies the user.

[1907] Output: Seismic countermeasure information notified to the user.

[1908] Hazard and evacuation map information mapping

[1909] Step 1:

[1910] Input: The user's current location.

[1911] How it works: A user enables the GPS function on their smartphone and presses the "Send current location" button in a system app.

[1912] Output: Location data sent to the server.

[1913] Step 2:

[1914] Input: Location data sent to the server.

[1915] How it works: The server retrieves location information and uses the Google Maps API to aggregate and process hazard map data for the area.

[1916] Output: Organized hazard map and evacuation map data.

[1917] Step 3:

[1918] Input: Organized hazard and evacuation map data.

[1919] Operation: The server sends the consolidated hazard and evacuation maps to the terminal.

[1920] Output: Hazard and evacuation maps sent to the device.

[1921] Step 4:

[1922] Input: Hazard and evacuation maps sent to the device.

[1923] How it works: The device displays map data to the user and guides them to a safe evacuation route.

[1924] Output: Hazard and evacuation maps displayed to the user.

[1925] Disaster prediction and evacuation instructions based on real-time data

[1926] Step 1:

[1927] Input: Real-time weather data and disaster information.

[1928] How it works: The server collects data in real time from the Japan Meteorological Agency API and other data providers.

[1929] Output: Weather data and disaster information collected on the server.

[1930] Step 2:

[1931] Input: Weather data and disaster information collected on the server.

[1932] How it works: The server inputs this data into an AI model to predict the occurrence of disasters.

[1933] Output: Disaster occurrence prediction results.

[1934] Step 3:

[1935] Input: Disaster occurrence prediction results.

[1936] Operation: The server generates evacuation instructions based on the prediction results.

[1937] Output: Evacuation order information.

[1938] Step 4:

[1939] Input: Evacuation order information.

[1940] Operation: The server sends the generated evacuation instructions to the device.

[1941] Output: Evacuation order information sent to the device.

[1942] Step 5:

[1943] Input: Evacuation order information sent to the device.

[1944] How it works: The device displays a pop-up notification of evacuation instructions, urging the user to take immediate action.

[1945] Output: Evacuation instructions notified to the user.

[1946] Automatic safety confirmation

[1947] Step 1:

[1948] Input: Biometric data from a smart device.

[1949] How it works: The device collects biometric data from the smartwatch using Bluetooth or other means.

[1950] Output: Biometric data collected on the device.

[1951] Step 2:

[1952] Input: Biometric data collected on the device.

[1953] Operation: The device sends the collected data to the server.

[1954] Output: Biometric data sent to the server.

[1955] Step 3:

[1956] Input: Biometric data sent to the server.

[1957] How it works: The server analyzes biometric data and assesses the user's safety.

[1958] Output: Safety assessment results.

[1959] Step 4:

[1960] Input: Safety assessment results.

[1961] Operation: The server generates safety information based on the evaluation results.

[1962] Output: Generated safety information.

[1963] Step 5:

[1964] Input: Generated safety information.

[1965] Operation: The server notifies the generated safety information to other users (e.g., family and friends) who have registered in advance.

[1966] Output: Notified safety information.

[1967] The above is the specific processing flow of this system's program. Each step works together to achieve comprehensive disaster prevention measures and safety confirmation.

[1968] (Application example 1)

[1969] 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."

[1970] Conventional disaster response support systems provide individual functions, such as furniture placement, earthquake-resistance measures, hazard map provision, evacuation instructions, and automatic safety confirmation, making it difficult for users to use them in a centralized manner. It is also difficult for autonomous vehicles to respond in real time to situations that change over time. As a result, optimal information cannot be provided to users to take safe action quickly in the event of a disaster, which can lead to delayed responses. Furthermore, there is a lack of disaster response systems that effectively utilize vehicle location information and biometric data.

[1971] 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.

[1972] In this invention, the server includes means for receiving images taken by a user, means for analyzing the images to evaluate furniture layout and earthquake risk, means for recommending earthquake-resistance measures based on the evaluation results, means for notifying the user of the recommended earthquake-resistance measures, means for receiving user location information, means for generating a hazard map and an evacuation map based on the location information, means for providing the generated hazard map and evacuation map to the user, means for collecting meteorological data and damage information in real time, means for analyzing the collected data to predict the occurrence of a disaster, means for generating evacuation instructions based on the prediction results, means for notifying the user of the generated evacuation instructions, means for a terminal to transmit vehicle location information to the server and display evacuation information on a vehicle display, means for collecting user biometric data and notifying other users of the user's safety, and means for analyzing the biometric data and safety information and evaluating safety. This enables centralized and real-time provision of information to users so that they can respond quickly and appropriately in the event of a disaster.

[1973] The "means for receiving images" refers to a system or component for transmitting images taken by a user from a terminal to a server and receiving the images.

[1974] "Means for analyzing images" means a system or algorithm that uses an AI module or image processing technology to analyze the information contained in the received images and evaluate specific patterns or risks.

[1975] "Means for assessing furniture placement and seismic risk" refers to a system or process for recognizing the location and placement of furniture based on the results of image analysis and assessing the associated seismic risk.

[1976] The "means for recommending earthquake-resistance measures" is a system or component for presenting optimal measures to users based on the evaluation results.

[1977] "Means for notifying users" refers to a system or application that sends recommended measures and importan...

Claims

1. means for receiving an image captured by a user; means for analyzing the images to evaluate furniture placement and seismic risk; a means for recommending earthquake-resistance measures based on the evaluation results; means for notifying a user of the recommended earthquake resistance measures; A system including:

2. means for receiving user location information; a means for generating a hazard map and an evacuation map based on the location information; means for providing the generated hazard map and evacuation map to a user; The system of claim 1 , comprising:

3. A means for collecting meteorological data and disaster information in real time; means for analyzing the collected data and predicting the occurrence of a disaster; means for generating evacuation instructions based on the prediction results; means for notifying a user of the generated evacuation instructions; The system of claim 1 , comprising:

4. means for receiving biometric data from a smart device; means for analyzing the biometric data to evaluate the safety of the user; a means for generating safety information based on the evaluation result and notifying the information to other designated users; The system of claim 1 , comprising:

Images