system

Abstract

Provide a system. [Solution] A means for acquiring location information; a means of collecting disaster data; A means of predicting the impact based on collected disaster data, and means for generating optimal actions for individual users based on the predicted impact; means for transmitting the generated action instructions to the terminals of the respective users; A system including:

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] Providing prompt and accurate instructions on optimal actions when a disaster occurs poses many challenges. The wide variety of disaster types and situations makes it difficult to make appropriate decisions based on each individual situation. Furthermore, there is a possibility that communications may be interrupted, making it difficult to provide information in real time. While it is important to confirm the safety of family members and share evacuation locations, it has been difficult to do so effectively using conventional methods. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a means for acquiring location information, a means for collecting disaster data, a means for predicting impacts based on the collected disaster data, a means for generating optimal actions for individual users based on the predicted impacts, and a means for transmitting the generated action instructions to each user's device. This system supports users in taking appropriate action promptly by including a means for notifying and displaying the transmitted action instructions to the user. The system also includes a means for caching the latest data at the time of a disaster in the device and displaying action instructions based on the cached data when communication is interrupted, thereby enabling appropriate information to be provided even when communication is interrupted. Furthermore, the system includes a means for acquiring pre-registered family information, periodically updating the family's location information, and a means for transmitting the updated location information to the device and displaying it to the user, thereby providing a system that allows effective confirmation of family safety.

[0006] "Location information" is data relating to the latitude, longitude, and altitude of a specific point, and is data for identifying the user's current location.

[0007] "Disaster data" refers to information related to natural disasters such as earthquakes, tsunamis, floods, and typhoons, and includes specific data such as the location of the epicenter, seismic intensity, tsunami height, and flood extent.

[0008] A "hazard map" is a map that shows disaster risk, visually indicating risk areas such as earthquakes, tsunamis, floods, and landslides in a specific area.

[0009] "Analysis" is the process of applying algorithms to collected data to predict the impact of disasters.

[0010] "Action instructions" are instructions indicating specific actions that a user should take when a disaster occurs, and include, for example, "move to an evacuation site" or "stay at home."

[0011] A "terminal" is a mobile phone, smartphone, tablet, or other communication device carried by a user, which receives data from a server and notifies the user.

[0012] "Caching" is the process of temporarily storing data in a storage device, used to quickly access needed information.

[0013] A "communications blackout" is a state in which normal means of communication (such as the Internet or telephone lines) become unavailable.

[0014] "Family information" is information about family members that the user has registered in advance, and is data including names, contact information, location information, and the like.

[0015] "Updating location information" means obtaining the latest location data and reflecting it in databases and related systems. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] overview

[0038] This invention is a system that enables individual users to take the most appropriate action quickly and accurately in the event of a disaster. This system comprehensively analyzes location information, disaster data, hazard maps, local government information, etc., and provides specific instructions to each user. It also has a function to provide instructions even when communication is cut off, and a tracking function to confirm the safety of family members.

[0039] Key Components

[0040] 1. Location information acquisition means

[0041] The device is equipped with a function to acquire the user's current location. Using GPS or Wi-Fi location services, the device periodically sends location data to a server.

[0042] 2. Disaster data collection methods

[0043] The server collects information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, and evacuation shelter information.

[0044] 3. Data Analysis Methods

[0045] The server analyzes the collected disaster data and user location information, and uses AI algorithms to predict the impact of the disaster, thereby generating optimal action instructions for each individual user.

[0046] 4. Action instruction generation means

[0047] The server generates specific instructions for each user based on the analysis results, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[0048] 5. Action instruction notification means

[0049] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via push notification or alarm.

[0050] 6. Data Caching Methods

[0051] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[0052] 7. Family Information Tracking Methods

[0053] The server tracks the location of family members based on pre-registered family information in the event of a disaster and sends that information to the user's device, which then displays their location and helps users confirm their safety.

[0054] Program processing explanation

[0055] Data collection and analysis

[0056] The server collects disaster data from the Japan Meteorological Agency and local governments at preset intervals, then analyzes the data using AI algorithms to predict the risk level for each area.

[0057] Action instruction generation and notification

[0058] Based on the analysis results, the server generates optimal instructions for each user and immediately sends them to the device, which then notifies the user of the received instructions via pop-up notifications or audio alerts.

[0059] Data caching and communication interruption support

[0060] The server caches the latest important data on each user's device in preparation for a disaster. Even if communication is interrupted, the device will display instructions based on the cached data.

[0061] Family Tracking

[0062] Users register their family members' information in the app in advance. When a disaster occurs, the server periodically obtains the family members' location information and sends it to the device. The device then displays the family members' location information to the user, helping them confirm their safety.

[0063] Specific examples

[0064] Example 1: When an earthquake occurs

[0065] The server obtains data on the epicenter and seismic intensity from the Japan Meteorological Agency and predicts the extent of the impact through AI analysis. If the area where User A is located is affected, the server generates an action instruction such as "Please evacuate to the nearest evacuation shelter" and sends it to User A's device. The device notifies User A of this instruction via a pop-up notification and a voice alert.

[0066] Example 2: When a tsunami warning is issued

[0067] The server acquires the tsunami warning information and generates an action instruction for User B living in a coastal area, such as "Please evacuate to higher ground," based on the analysis results. The device immediately notifies User B of this action instruction via push notification.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The server periodically collects real-time disaster data from various data sources (Japan Meteorological Agency, local governments, geographic information systems, etc.). The server uses APIs to obtain earthquake epicenter data, tsunami warnings, flood forecasts, etc. from the Japan Meteorological Agency, and also collects evacuation center location information from local governments.

[0071] Step 2:

[0072] The server stores the collected disaster data in a database and performs a comprehensive analysis using AI algorithms. The server overlays the acquired epicenter data with a hazard map to evaluate the risk level of each area. Different analysis methods are used for each type of disaster (earthquake, tsunami, flood, etc.).

[0073] Step 3:

[0074] The server uses each user's data, including their location information, to generate optimal instructions for each user based on the analysis results. For example, if evacuation is necessary, the server generates instructions such as "Please evacuate to the nearest evacuation shelter."

[0075] Step 4:

[0076] The server immediately sends the generated action instructions to each user's device using push notifications, with different notifications sent to each user.

[0077] Step 5:

[0078] The device notifies the user of the action instructions received from the server, displaying specific instructions such as "Please evacuate" or "Please stay at home" using pop-up notifications and audio alerts.

[0079] Step 6:

[0080] When a disaster is predicted, the server caches the latest important data on each user's device, so that even if communication is interrupted, the device can provide users with action instructions based on the cached data.

[0081] Step 7:

[0082] If communication is interrupted, the device will automatically load the cached data and display instructions to the user. The device will continuously display specific instructions along with the message "Displaying evacuation instructions based on data cached by this device."

[0083] Step 8:

[0084] Users can register their family members' information (such as names, contact information, and location sharing settings) in advance on the app's settings screen, which allows the system to help confirm the safety of family members in the event of a disaster.

[0085] Step 9:

[0086] The server periodically acquires and analyzes the location information of pre-registered family members in the event of a disaster. The location information of family members is tracked in real time, and the latest data is sent to the device.

[0087] Step 10:

[0088] The device displays the location information of the family sent from the server to the user, and displays a message such as "The family is currently evacuated to △△ Elementary School" to allow the user to confirm the safety of their family.

[0089] The above is a detailed explanation of the program's processing steps. This system provides support to users so that they can act quickly and appropriately in the event of a disaster.

[0090] Example 1

[0091] 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."

[0092] Conventional disaster prevention systems have problems with insufficient collection and analysis of disaster information and difficulty in assessing the situation in real time, making it difficult to provide prompt instructions for action. Furthermore, when communications are cut off, it is difficult to provide appropriate information, making it difficult to adequately confirm the safety of family members.

[0093] 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.

[0094] In this invention, the server includes a means for acquiring location information, a means for collecting disaster data, a means for predicting impacts based on the collected disaster data and location information, a means for generating optimal action instructions for individual users based on the predicted impacts, a means for transmitting the generated action instructions to each user's device, and a means for tracking the location information of family members and confirming their safety. This enables prompt and accurate action instructions to be provided in the event of a disaster and appropriate support even when communication is interrupted. It also enables the safety of family members to be confirmed in real time.

[0095] "Means for acquiring location information" refers to the technology or device used to accurately acquire the user's current location. Specifically, this includes GPS and Wi-Fi location services.

[0096] "Means of collecting disaster data" refers to technologies and devices for collecting disaster information on earthquakes, tsunamis, floods, etc. in real time provided by the Japan Meteorological Agency and local governments.

[0097] "Means for predicting impacts" refers to technologies and devices that use AI algorithms and other methods to analyze risk levels in specific areas based on collected disaster data and location information, and predict future impacts.

[0098] "Means for generating action instructions" refers to technology or devices that create information to instruct each user on the optimal action based on the predicted impact of the disaster. Examples include evacuation advisories and standby instructions.

[0099] The "means for transmitting action instructions" refers to a technique or device for transmitting the generated action instructions to each user's terminal in real time via a communication network.

[0100] The "means for notifying an action instruction" refers to a technique or device for immediately notifying the user of an action instruction received by the terminal. Specifically, this includes pop-up notifications, audio alerts, vibrations, etc.

[0101] The "means for displaying action instructions" refers to a technique or device that displays the notified action instructions on the screen of the terminal so that the user can visually confirm them.

[0102] "Data caching means" refers to technology or devices that temporarily store the latest information on a device when a disaster occurs. This cached data allows instructions to be provided even if communication is interrupted.

[0103] The "means for displaying action instructions when communication is interrupted" refers to a technique or device for displaying appropriate action instructions to the user based on cached data even when communication is interrupted.

[0104] "Means for tracking family location information" refers to technology or devices that track the location information of pre-registered family members in the event of a disaster and ensure their safety.

[0105] "Means for confirming safety" refers to technologies and devices that use family location information to enable users to check the safety status of their family members in real time.

[0106] MODE FOR CARRYING OUT THE INVENTION

[0107] overview

[0108] This invention is a support system that enables individual users to take the most appropriate action quickly and accurately when a disaster occurs. This system comprehensively analyzes location information, disaster data, hazard maps, and local government information, and provides specific instructions to each user. It also has a function to provide instructions even when communication is cut off, and a tracking function to confirm the safety of family members.

[0109] Key Components

[0110] 1. Location information acquisition means:

[0111] The device uses GPS and Wi-Fi location services to accurately determine the user's current location. The device periodically sends location data to a server.

[0112] Example: When a user carries a smartphone, it obtains the current latitude and longitude every minute and sends that data to a server.

[0113] 2. Disaster data collection methods:

[0114] The server collects real-time disaster information on earthquakes, tsunamis, floods, and other disasters provided by the Japan Meteorological Agency and local governments.

[0115] Example: When the Japan Meteorological Agency issues a new earthquake alert, the server collects the data immediately.

[0116] 3. Data analysis methods:

[0117] The server analyzes the collected disaster data and location information using an AI algorithm, which predicts the risk level for the user's location.

[0118] Example: The server uses data on the epicenter of an earthquake to determine whether the user's current location is safe.

[0119] 4. Action instruction generation means:

[0120] The server generates specific instructions for each user based on the results of the data analysis. For example, if evacuation is necessary, the server generates instructions such as "Please evacuate to the nearest evacuation shelter."

[0121] Example: If user A is in an area at risk of a tsunami, the server generates an action instruction such as "Please evacuate to higher ground."

[0122] 5. Action instruction notification means:

[0123] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via a pop-up notification, audio alert, or vibration.

[0124] Example: User B receives a voice alert saying "Please evacuate."

[0125] 6. Data caching methods:

[0126] The device caches the latest important data received from the server in case of a communication interruption, allowing the device to provide the user with action instructions even if communication is interrupted.

[0127] Example: When communication is lost, the device displays the cached "shelter location information."

[0128] 7. Family Information Tracking Methods:

[0129] Users register their family members' information in advance in the app. When a disaster occurs, the server periodically obtains their family members' location information and sends it to the user's device. The device then displays their location and helps users confirm their safety.

[0130] Example: The server verifies the location of User C's family and notifies User C's device that "the family has safely arrived at the evacuation shelter."

[0131] The specific hardware and software used

[0132] Hardware: Smartphones, tablets, servers, GPS modules, communication network equipment

[0133] Software: Location services, real-time data collection software, AI analysis algorithms, notification systems, cache management software

[0134] Explanation of operation using concrete examples

[0135] If an earthquake occurs:

[0136] 1. The server obtains data on the epicenter and intensity of the earthquake from the Japan Meteorological Agency.

[0137] 2. The device sends the user's new location information to the server.

[0138] 3. The server uses AI analysis to predict the impact from the epicenter and determines that the area where User A is located is dangerous.

[0139] 4. The server generates an action instruction such as "Please evacuate to the nearest evacuation shelter" and sends it to User A's device.

[0140] 5. The device notifies User A with a pop-up notification and a voice alert.

[0141] If a tsunami warning is issued:

[0142] 1. The server obtains tsunami warning information.

[0143] 2. The server checks the location information of user B, who lives in a coastal area, and generates an instruction to "evacuate to higher ground."

[0144] 3. The server immediately sends this action instruction to User B's device.

[0145] 4. The device immediately notifies User B via push notification and vibration.

[0146] Example prompt statement

[0147] Examples of prompts to illustrate specific usage scenarios are:

[0148] When an earthquake occurs, User A receives instructions via his smartphone saying, "Please evacuate to the nearest evacuation shelter." Based on these instructions, he quickly begins evacuation.

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

[0150] Step 1: Obtaining location information

[0151] The device obtains the user's current location. Specifically, it uses GPS or Wi-Fi location services. As input, a trigger occurs that causes the device to periodically obtain the user's current location. As output, the obtained latitude and longitude data is generated. The device then sends this location data to the server.

[0152] Specific operation: The device obtains the user's current latitude and longitude every minute and sends them to the server.

[0153] Step 2: Collect disaster data

[0154] The server collects disaster data in real time from the Japan Meteorological Agency and local governments. As input, new disaster information is acquired from the servers of the Japan Meteorological Agency and local governments. As output, data such as earthquake epicenters, tsunami warnings, and flood forecasts are stored on the server.

[0155] Specific operation: The server periodically calls the Japan Meteorological Agency's API to obtain the latest earthquake data and stores it in a database.

[0156] Step 3: Analyze the data

[0157] The server uses an AI algorithm to analyze the collected disaster data and received location information. The disaster data and location information are provided as input. The output is a risk level index generated as a result of the analysis. Based on this risk level, a basis for optimal action instructions for each user is created.

[0158] How it works: The server uses an AI model to analyze earthquake data and the user's location to calculate the risk level for the user's location.

[0159] Step 4: Generate action instructions

[0160] The server generates specific instructions for each user based on the data analysis results. The risk level indicator is used as input. Instructions for each user are generated as output. These instructions include, for example, "Please evacuate to the nearest evacuation shelter" or "Please stay at home."

[0161] Specific operation: Based on the risk level indicator, the server generates action instructions such as "Evacuate to higher ground" for users in areas where a tsunami warning has been issued.

[0162] Step 5: Notification of instructions to act

[0163] The server immediately sends the generated action instructions to each user's device. The device notifies the user of the received action instructions via a pop-up notification or a voice alert. The action instruction data is sent from the server as input. The notification itself is sent as output.

[0164] Specific operation: The server sends evacuation instructions to the user's device, which then issues a pop-up notification and an audio alert.

[0165] Step 6: Caching Data

[0166] The terminal caches the most recent important data received from the server in case of a communication interruption. The most recent action instruction data is cached as input. The cached data to be used when communication is interrupted is obtained as output.

[0167] Specific operation: The device locally stores the "shelter location information" received from the server and prepares to display it when communication is lost.

[0168] Step 7: Tracking Family Information

[0169] Users register their family members' information in the app in advance. When a disaster occurs, the server periodically obtains the location information of the registered family members and sends it to the user's device. The family members' location information is sent to the server as input. The family members' location data is displayed on the user's device as output.

[0170] Specific operation: The server periodically obtains the family's location data and notifies User C that the family has safely arrived at the evacuation shelter.

[0171] (Application example 1)

[0172] 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."

[0173] In the event of a disaster, it is extremely important to quickly and accurately ensure the safety of workers and machinery in logistics centers. However, conventional systems have insufficient disaster information collection and analysis, making it impossible to track location information in real time or generate and notify appropriate action instructions. Furthermore, there is a lack of a way to provide accurate action instructions even in the event of a communications outage. This can lead to reduced efficiency in logistics operations and delays in ensuring the safety of workers.

[0174] 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.

[0175] In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impacts based on the collected disaster data, means for generating optimal actions for individual users based on the predicted impacts, means for transmitting the generated action instructions to each user's terminal, means for acquiring and notifying location information of equipment and workers in the logistics center, and means for predicting the impacts on the logistics center in the event of a disaster and generating optimal action instructions. This makes it possible to quickly and accurately ensure the safety of workers and equipment in the logistics center even in the event of a disaster and prevent a decline in work efficiency.

[0176] "Location information acquisition means" refers to technologies or devices used to determine the current location of a user or device, including GPS functionality and Wi-Fi location services.

[0177] "Means of collecting disaster data" refers to systems and services for collecting disaster-related information provided by official organizations such as the Japan Meteorological Agency and local governments.

[0178] The "means for predicting impact" is a system that uses AI algorithms and analytical technology to estimate the impact of a disaster based on collected disaster data and location information.

[0179] The "means for generating optimal behavior" is an algorithm or program that determines and generates the best behavior that a user or device should take based on the predicted impact.

[0180] The "means for transmitting behavioral instructions to each user's terminal" refers to a communication technology or protocol for transmitting the generated behavioral instructions to each user's electronic device.

[0181] "Means for acquiring and notifying location information of equipment and workers within a logistics center" refers to a system for identifying the current location of machines and workers used within a logistics center and informing managers and other relevant parties.

[0182] The "means of predicting the impact on logistics centers in the event of a disaster and generating optimal action instructions" is a system that analyzes risks at logistics centers in the event of a disaster and determines safe and effective response measures based on that analysis.

[0183] overview

[0184] This invention is a system for quickly and accurately ensuring the safety of workers and equipment in a logistics center in the event of a disaster. This system comprehensively utilizes location information, disaster data, risk analysis data, etc. to provide specific action instructions to individual users. It also has the ability to provide instructions even when communication is interrupted, and the ability to track locations.

[0185] Key Components

[0186] 1. Location information acquisition means

[0187] The server periodically collects the current locations of equipment and workers within the logistics center using GPS and Wi-Fi location information services, making it possible to grasp overall location information within the logistics center.

[0188] 2. Disaster data collection methods

[0189] The server collects disaster information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, etc. This data is periodically retrieved by the server using an API.

[0190] 3. Data Analysis Methods

[0191] The server analyzes the collected disaster data and location information and uses generative AI models and algorithms to predict the impact of the disaster, thereby identifying the risk level for each piece of equipment and worker.

[0192] 4. Action instruction generation means

[0193] Based on the analysis results, the server generates optimal action instructions for each piece of equipment and worker in the logistics center, including instructions for safe evacuation routes and instructions to suspend work.

[0194] 5. Action instruction notification means

[0195] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received action instructions via a pop-up notification or a voice alert.

[0196] 6. Data Caching Methods

[0197] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[0198] Operational flow and concrete examples

[0199] Server-side processing

[0200] The server periodically obtains location information within the logistics center using the location information acquisition means. It also collects real-time disaster information from outside using the disaster data collection means. Based on this data, it predicts the impact of the disaster using the data analysis means. For example, if an earthquake occurs nearby, the server analyzes the location of the epicenter and the logistics center and predicts the impact. Next, based on the predicted impact, the action instruction generation means generates optimal action instructions for each piece of equipment and worker. These action instructions relate to safe evacuation routes within the logistics center, interruptions to work, etc.

[0201] Terminal side processing

[0202] When the device receives the action instructions sent from the server, it notifies the user of the contents. The notification is displayed as a pop-up or a sound alert. The device also uses a data caching means to cache the latest important data. Even if communication is interrupted, the device can display the action instructions based on the cached data.

[0203] Specific examples

[0204] Example 1: High flood risk

[0205] If a logistics center is located near a river, the server obtains flood information from the Japan Meteorological Agency and predicts the impact. If the risk is deemed high, the server generates an action instruction to "evacuate to higher ground" and notifies the worker's device. The device immediately notifies the worker of this instruction via a voice alert.

[0206] Example 2: Structural risk of a logistics center due to an earthquake

[0207] If a major earthquake occurs nearby, the server analyzes data on the epicenter and the location of the logistics center to predict the impact. If it determines that the impact on the entire logistics center will be significant, the server generates an action instruction stating, "We recommend you evacuate outside the building," and notifies the user's device. This allows forklifts and workers to quickly evacuate outside the building.

[0208] Prompt Sentence Examples

[0209] Please describe the detailed features of the "Disaster Response Logistics Management App." Please include the following elements:

[0210] Location information acquisition function

[0211] Disaster data collection function

[0212] Data analysis function

[0213] Action instruction generation function

[0214] Action instruction notification function

[0215] Data cache function

[0216] Worker tracking function

[0217] Specific examples include instructions for action in cases of high flood risk or earthquake risk.

[0218] As described above, by using this system, it is possible to ensure the safety of workers and equipment within the logistics center and prevent a decline in work efficiency.

[0219] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0220] Step 1:

[0221] The server periodically collects the current locations of equipment and workers in the logistics center using location information acquisition means. The input is GPS data of the equipment and workers, and the output is a real-time location information list. Specifically, the server receives the GPS coordinates sent from each device and stores them in a list.

[0222] Step 2:

[0223] The server uses disaster data collection tools to collect disaster information in real time from the Japan Meteorological Agency and local governments. The input is disaster data from the public API, and the output is a disaster information database. Specifically, the weather information and disaster notifications obtained via the API are stored in the database and updated as needed.

[0224] Step 3:

[0225] The server uses data analysis tools to analyze the collected disaster data and location information. The input is a disaster information database and a location information list, and the output is impact prediction data. Specifically, a generative AI model is used to run an algorithm that predicts the impact of a disaster based on the location information of each piece of equipment and worker.

[0226] Step 4:

[0227] The server uses the action instruction generation means to generate optimal action instructions for each piece of equipment and worker in the logistics center based on the prediction results. The input is impact prediction data, and the output is specific action instructions. Specifically, action instructions (e.g., "evacuate to the nearest evacuation shelter" or "move to a safe location") are automatically generated according to the risk level.

[0228] Step 5:

[0229] The server uses the action instruction notification means to instantly send the generated action instructions to each user's device. The input is the generated action instruction, and the output is a notification to each user's device. Specifically, the action instruction is sent to each device and notified to the user as a push notification or a voice alert.

[0230] Step 6:

[0231] The terminal uses a data caching means to cache the latest data sent from the server in case of a communication interruption. The input is important data from the server, and the output is cached data. Specifically, the latest disaster information and action instructions are periodically saved locally to prepare for communication interruptions.

[0232] Step 7:

[0233] The user takes appropriate action based on the received action instructions. The input is an action instruction notification from the device, and the output is a change in the user's behavior. Specifically, the user takes specific action, such as following the evacuation instructions displayed on the device and moving to a safe place.

[0234] 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.

[0235] overview

[0236] This invention combines a system that provides optimal action instructions to individuals based on their location information when a disaster occurs with an emotion engine that recognizes the user's emotions. This allows the content and display method of action instructions to be optimized according to the user's emotional state, providing more effective support.

[0237] Key Components

[0238] 1. Location information acquisition means

[0239] The device is equipped with a function to acquire the user's current location. Using GPS or Wi-Fi location services, the device periodically sends location data to a server.

[0240] 2. Disaster data collection methods

[0241] The server collects information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, and evacuation shelter information.

[0242] 3. Data Analysis Methods

[0243] The server analyzes the collected disaster data and user location information, and uses AI algorithms to predict the impact of the disaster, thereby generating optimal action instructions for each individual user.

[0244] 4. Action instruction generation means

[0245] The server generates specific instructions for each user based on the analysis results, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[0246] 5. Action instruction notification means

[0247] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via push notification or alarm.

[0248] 6. Data Caching Methods

[0249] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[0250] 7. Family Information Tracking Methods

[0251] The server tracks the location of family members based on pre-registered family information in the event of a disaster and sends that information to the user's device, which then displays their location and helps users confirm their safety.

[0252] 8. Emotion Engine

[0253] The device is equipped with an engine for recognizing the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state.

[0254] 9. Emotional Data Analysis Methods

[0255] The server receives the user's emotional data and integrates it with the disaster data analysis results to optimize behavioral instructions based on the user's emotional state.

[0256] Program processing explanation

[0257] Data collection and analysis

[0258] The server periodically collects disaster data from the Japan Meteorological Agency and local governments and stores it in a database. The server then applies AI algorithms to assess the risk level of each area. Based on the analysis results, it generates optimal action instructions taking into account the user's location information.

[0259] Action instruction generation and notification

[0260] The server generates specific instructions for each user based on the analysis results. The instructions are then immediately sent to the user's device. The device then notifies the user of the received instructions via push notification or voice alert.

[0261] Data caching and communication interruption support

[0262] The server caches the latest important data on each user's device in preparation for a disaster. Even if communication is interrupted, the device can provide the user with instructions based on the cached data. For example, when communication is interrupted, the device displays the message "Displaying evacuation instructions based on data cached by this device."

[0263] Family Tracking

[0264] Users register their family information in advance on the app's settings screen. When a disaster occurs, the server periodically obtains and analyzes the family's location information. The latest location information is sent to the user's device, which then displays a message such as, "The family is currently evacuated to XX Elementary School."

[0265] Implementing the Emotion Engine

[0266] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state. For example, if the user is feeling anxious, it will recognize this and send emotional data to the server.

[0267] Emotional data analysis and optimization of behavioral instructions

[0268] The server receives the user's emotional data and integrates it with disaster data analysis results to optimize instructions. For example, if the user is in a panic, the server will adjust the notification display (such as font size and audio level) to provide detailed evacuation instructions along with a message to "stay calm."

[0269] Specific examples

[0270] Example 1: An earthquake occurs and the user is anxious.

[0271] The server acquires data on the epicenter and seismic intensity, and uses AI analysis to predict the extent of the impact. User A's device uses an emotion engine to send a message to the server indicating that User A is in an anxious state. Based on the analysis results and emotion data, the server generates action instructions such as "Please evacuate slowly to the nearest evacuation shelter" and sends them to User A's device. The device then notifies User A of these instructions in large, easy-to-read letters.

[0272] Example 2: A tsunami warning is issued and the user is in a panic

[0273] The server receives the tsunami warning information and generates an action instruction for User B, who lives in a coastal area, saying, "Please evacuate to higher ground." Meanwhile, the device notifies the server that User B is in a panic. The server takes into account the emotion data and generates a message such as, "Take a deep breath and stay calm. Move slowly to safe higher ground," and sends it to User B's device.

[0274] The processing flow will be explained below.

[0275] Step 1:

[0276] The server periodically collects disaster data from various data sources (Japan Meteorological Agency, local governments, etc.). Specifically, the server uses APIs to obtain the latest data such as epicenter information, tsunami warnings, and flood forecasts from the Japan Meteorological Agency, and also collects location information of evacuation centers from local governments.

[0277] Step 2:

[0278] The server stores the collected disaster data in a database, updates the database, and manages all information appropriately.

[0279] Step 3:

[0280] The server analyzes the collected disaster data using AI algorithms, which allows the server to assess the risk level of each area and generate predictions.

[0281] Step 4:

[0282] The server obtains the user's location information from the device and generates optimal instructions for each user based on that location information. The server takes into account the analysis results and creates specific instructions such as "evacuate to the nearest evacuation shelter" or "stay at home."

[0283] Step 5:

[0284] The server immediately sends the generated action instructions to each user's device. The server uses push notifications to send localized action instructions to each device.

[0285] Step 6:

[0286] The device notifies the user of the action instructions received from the server, displaying instructions such as "Please evacuate" or "Please stay at home" using pop-up notifications and audio alerts.

[0287] Step 7:

[0288] When a disaster is predicted, the server caches the latest important data on each user's device, allowing the device to provide action instructions based on the cached data even if communication is interrupted.

[0289] Step 8:

[0290] If communication is interrupted, the device will automatically load cached data and display instructions to the user. The device will continuously display specific instructions along with the message "Displaying evacuation instructions based on data cached by this device."

[0291] Step 9:

[0292] Users enter their family members' information in advance through the app's settings screen, including their names, contact details, and location sharing settings.

[0293] Step 10:

[0294] When a disaster occurs, the server periodically obtains the location information of pre-registered family members, tracks their location in real time, and collects and analyzes the latest data.

[0295] Step 11:

[0296] The server sends updated family location information to each user's device. The server periodically sends family location information to each user's device so that the user can check the information.

[0297] Step 12:

[0298] The device displays the location information of the family sent from the server to the user, and displays a message such as "The family is currently evacuated to △△ Elementary School" to allow the user to confirm the safety of their family.

[0299] Step 13:

[0300] The device uses an emotion engine to recognize the user's emotions by analyzing the user's facial expressions, voice, and other biometric signals. The emotion engine determines the user's emotional state (e.g., anxiety, fear, panic, etc.).

[0301] Step 14:

[0302] The device sends the recognized emotion data to the server, which receives the emotion data and integrates it with the disaster data analysis results.

[0303] Step 15:

[0304] The server optimizes the analysis results and action instructions based on the emotional data. For example, if the user is in a panic state, it will add a message such as "Please stay calm."

[0305] Step 16:

[0306] The server sends optimized behavioral instructions to each user's device, which then displays appropriate instructions according to the user's emotional state, helping the user to act calmly.

[0307] The above is a detailed description of the processing steps of the disaster prevention system that combines the emotion engine. This system provides appropriate action instructions that take into account the user's emotional state, thereby more effectively ensuring the user's safety in the event of a disaster.

[0308] Example 2

[0309] 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."

[0310] Conventional disaster response systems have been effective to a certain extent in providing action instructions based on the user's location information and disaster data, but because they do not take the user's emotional state into consideration, there are issues with the effectiveness of the action instructions being insufficient in situations where users are prone to stress or panic. There is also the problem that it is difficult to provide appropriate action instructions to users when communication is cut off.

[0311] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impacts based on the collected disaster data, means for generating optimal actions for individual users based on the predicted impacts, means for transmitting the generated action instructions to each user's terminal, means for analyzing the emotional state of the user, and means for optimizing the action instructions taking the emotional state into consideration. This makes it possible to provide optimal action instructions according to the emotional state, reduce stress and panic in users when a disaster occurs, and provide appropriate action instructions even when communication is interrupted.

[0312] "Location information acquisition means" refers to a function for acquiring the user's current location, and collects location data using technologies such as GPS and Wi-Fi.

[0313] "Disaster data collection means" refers to the function for collecting disaster data from the Japan Meteorological Agency and local governments in real time, including information on earthquakes, tsunamis, floods, etc.

[0314] "Impact prediction means" refers to a function that uses an AI algorithm to predict impact based on collected disaster data, and analyzes the user's location information and disaster data to assess the risk level.

[0315] The "action instruction generation means" refers to a function that generates optimal action instructions for each user based on the impact prediction, and specifically includes guidance on evacuation sites and instructions to stay at home.

[0316] The "action instruction sending means" refers to a function for sending the generated action instructions to each user's terminal, and aims to quickly deliver the action instructions to the user.

[0317] "Emotional state analysis means" refers to a function for analyzing the user's emotional state, which determines emotions by analyzing the user's facial expressions, voice, and biological signals.

[0318] The "action instruction optimization means" refers to a function that takes into account the user's emotional state and appropriately adjusts action instructions accordingly, changing the content and display format of notifications based on emotional data.

[0319] "Push notification" refers to a method in which a server instantly sends information to a device to notify the user, and is a means of quickly delivering important information and instructions for action.

[0320] "Data caching means" refers to a function that stores the latest important data on the terminal in preparation for communication interruptions, enabling appropriate action instructions to be provided even in the event of a disaster.

[0321] overview

[0322] This invention combines a system that provides optimal action instructions based on the user's location information when a disaster occurs with an emotion engine that recognizes the user's emotional state. This enables the content and display method of action instructions to be optimized according to the user's emotional state, making it possible to provide more effective support.

[0323] System Program Overview

[0324] The system includes a location information acquisition means, a disaster data collection means, an impact prediction means, an action instruction generation means, an action instruction transmission means, an emotional state analysis means, and an action instruction optimization means.

[0325] 1. Location information acquisition means

[0326] The device uses GPS and Wi-Fi location services to obtain the user's current location, and periodically sends the obtained location data to a server.

[0327] 2. Disaster data collection methods

[0328] The server collects disaster data on earthquakes, tsunamis, floods, etc. in real time from the Japan Meteorological Agency and local governments, and stores this data in a database.

[0329] 3. Impact prediction methods

[0330] The server applies AI algorithms based on the collected disaster data and user location information to assess risk levels and predict the impact of disasters, generating optimal action instructions for each user.

[0331] 4. Action instruction generation means

[0332] Based on the predicted impact, the server generates specific instructions for each user, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[0333] 5. Means of sending instructions for action

[0334] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions using push notifications or voice alerts.

[0335] 6. Emotional state analysis method

[0336] The device is equipped with an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state. The determined emotion data is then sent to a server.

[0337] 7. Action Instruction Optimization Method

[0338] The server receives the user's emotional data and integrates it with disaster data analysis results to optimize instructions. For example, if the user is in a panic, it will provide detailed evacuation instructions along with a message such as "Please stay calm." The notification display method (font size, audio level, etc.) is also adjusted according to the emotional data.

[0339] Specific examples

[0340] Example 1: An earthquake occurs and the user is anxious.

[0341] The server collects data on the epicenter and seismic intensity, and uses AI analysis to predict the extent of the impact.

[0342] The terminal transmits to the server, via the emotion engine, that User A is in an anxious state.

[0343] Based on the analysis results and emotion data, the server generates action instructions such as "Please evacuate slowly to the nearest evacuation shelter" and sends them to User A's device.

[0344] The terminal notifies User A of this instruction in large, easy-to-read letters.

[0345] Example 2: A tsunami warning is issued and the user is in a panic

[0346] The server receives tsunami warning information and generates an action instruction for User B who lives in a coastal area, such as "Evacuate to higher ground."

[0347] The device detects that User B is in a panic state using its emotion engine and notifies the server.

[0348] The server takes into consideration the emotion data and generates an action instruction such as "Take a deep breath and calm down. Move slowly to safe, higher ground," and sends it to User B's terminal.

[0349] The terminal displays the received instruction together with a voice message, providing User B with a sense of security.

[0350] Prompt Sentence Examples

[0351] Below are some example prompts to input to a generative AI model:

[0352] "Earthquake information has been obtained. The epicenter is in Tokyo, with a seismic intensity of 6+. The user is currently in Ota Ward and is feeling uneasy. Please generate the most appropriate instructions for action."

[0353]

[0354] "A tsunami warning has been issued. The user lives in Yokohama and is currently in a state of panic. Please take a deep breath and generate a calming message and evacuation instructions."

[0355] This will realize a system that can provide users with prompt and effective support in the event of a disaster and provide optimal action instructions according to their emotional state.

[0356] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0357] Step 1:

[0358] Location information collection

[0359] Input: User's current location (GPS data and / or Wi-Fi location).

[0360] Specific operation: The device periodically collects the user's current location. Specifically, it obtains location information using GPS and Wi-Fi.

[0361] Output: Send the acquired location data to the server.

[0362] Step 2:

[0363] Disaster data collection

[0364] Input: Disaster information (earthquakes, tsunamis, floods, etc.) from the Japan Meteorological Agency and local governments.

[0365] Specific operation: The server collects real-time disaster data through APIs from the Japan Meteorological Agency and local governments.

[0366] Output: Collected disaster data is stored in a database.

[0367] Step 3:

[0368] Data analysis

[0369] Input: Location information from step 1 and disaster data from step 2.

[0370] How it works: The server uses AI algorithms to analyze location information and disaster data, and then evaluates the risk level for each user's current location.

[0371] Output: Generate risk assessment results according to each user's location.

[0372] Step 4:

[0373] Collecting Emotional Data

[0374] Input: User's facial, voice, and biometric data.

[0375] Specific operation: The device uses an emotion engine to analyze the user's facial expressions, voice, and biometric signals to determine their current emotional state.

[0376] Output: The determined emotion data is sent to the server.

[0377] Step 5:

[0378] Generate action instructions

[0379] Input: Risk assessment results from step 3 and sentiment data from step 4.

[0380] Specific operation: The server integrates the risk assessment results and emotional data, and generates optimal instructions for action using an AI algorithm. Specifically, it creates messages and instructions for action that are tailored to the user's emotional state.

[0381] Output: Generate optimized action instructions for each user.

[0382] Step 6:

[0383] Sending action instructions and notifications

[0384] Input: Action instructions generated in step 5.

[0385] Specific operation: The server immediately sends the generated action instructions to the user's device, and the device notifies the user of the received action instructions via push notification or voice alert.

[0386] Output: Instructions for action sent to the user's device.

[0387] Step 7:

[0388] Data Cache

[0389] Input: Latest critical data (disaster data from Step 2 and action instructions from Step 5).

[0390] Specific operation: The device caches the latest important data in preparation for communication interruptions, making it possible to respond even in the event of a disaster.

[0391] Output: Cached sensitive data.

[0392] Step 8:

[0393] Use of cached data

[0394] Input: State when communication is interrupted.

[0395] Specific operation: If communication is interrupted, the device will use cached data to provide instructions to the user, specifically displaying the message "Providing evacuation instructions based on data cached by this device."

[0396] Output: Action instructions provided even in the event of communication blackout.

[0397] Step 9:

[0398] Family Tracking

[0399] Input: Family information registered by the user.

[0400] Specific operation: Users register their family members' information in advance on the app's settings screen. When a disaster occurs, the server periodically obtains the family members' location information and sends it to the user's device.

[0401] Output: Location information and updates of family members.

[0402] Step 10:

[0403] Family location notifications

[0404] Input: Family location information from step 9.

[0405] Specific operation: The device notifies the user of the latest location information, saying, "Your family has evacuated to △△ Elementary School."

[0406] Output: Family location information displayed on the user's device.

[0407] (Application example 2)

[0408] 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."

[0409] Conventional disaster response systems can generate action instructions based on a user's location information and disaster information, but they cannot take the user's emotional state into consideration. As a result, if a user is in a state of panic or anxiety, appropriate action instructions cannot be provided quickly, making it difficult to evacuate efficiently and ensure safety. Furthermore, if the information notification method is not appropriate for the user's emotional state, there is also the problem that instructions cannot be effectively conveyed. The present invention aims to solve these problems and support rapid and appropriate evacuation by providing optimal action instructions based on the user's emotional state during a disaster.

[0410] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impact based on the collected disaster data, means for analyzing the emotional state using an emotion engine that recognizes the user's emotions, means for generating optimal actions for each user based on the predicted impact and emotional state, and means for transmitting the generated action instructions to each user's terminal. As a result, optimal action instructions are generated taking into account the user's location information and emotional state when a disaster occurs, enabling quick and appropriate evacuation and safety assurance.

[0411] "Means for acquiring location information" refers to a function that collects the user's current location using technologies such as GPS or Wi-Fi and transmits it to a server.

[0412] "Means for collecting disaster data" refers to the function of acquiring disaster-related information provided by the Japan Meteorological Agency and local governments in real time and storing it in a database.

[0413] "Means for predicting impact based on collected disaster data" refers to a function that analyzes collected disaster data and uses an AI algorithm to predict the impact of a disaster on specific regions or users.

[0414] The "means for analyzing the emotional state by utilizing an emotion engine that recognizes the user's emotions" is an engine that analyzes the user's facial expressions, voice, and other biometric signals to determine the emotional state at that time.

[0415] The "means for generating optimal behavior for each individual user" is a function that generates specific and appropriate behavioral instructions for each user based on the predicted impact results and emotional state.

[0416] The "means for transmitting generated action instructions to each user's terminal" is a function for instantly transmitting action instructions generated by the server to the user's terminal such as a smartphone or smart glasses.

[0417] The "means for notifying the user of the transmitted action instruction" is a function for notifying the user of the received action instruction through a push notification or a voice alert.

[0418] The "means for displaying the notified action instruction to the user" is a function for displaying the received action instruction on the terminal screen of the user.

[0419] The "means for optimizing the display method of action instructions according to the emotional state of the user" is a function for adjusting the display method of action instructions (character size, audio level, etc.) based on the emotional state of the user.

[0420] "Means for caching the latest data on the terminal when a disaster occurs" is a function that temporarily stores important data on the terminal when a disaster occurs.

[0421] The "means for displaying action instructions based on cached data when communication is interrupted" is a function that provides action instructions to the user using cached data when communication is interrupted.

[0422] MODE FOR CARRYING OUT THE INVENTION

[0423] overview

[0424] This invention combines a system that provides optimal action instructions to individuals based on their location information when a disaster occurs with an emotion engine that recognizes the user's emotions. This system optimizes the content and display method of action instructions according to the user's emotional state, making it possible to provide more effective support.

[0425] Main Components and Processing

[0426] The main components required for this system are as follows:

[0427] Location information acquisition means

[0428] The server collects location information from users' devices, such as smartphones and smart glasses, using GPS and Wi-Fi, and periodically transmits this location information to the server.

[0429] Disaster data collection methods

[0430] The server collects disaster information from the Japan Meteorological Agency and local governments, including earthquake, tsunami, flood, and evacuation shelter information. The collected data is stored on the server.

[0431] Impact prediction methods

[0432] The server analyzes the collected disaster data using AI algorithms (such as TENSORFLOW (registered trademark) and PyTorch) to predict the impact of the disaster on specific areas and users.

[0433] emotion recognition means

[0434] The device is equipped with an emotion engine (such as Affectiva SDK or Microsoft® Azure® Face API) that recognizes the user's emotions. It uses a camera and microphone to analyze the user's facial expressions and voice to determine their current emotional state.

[0435] Action instruction generation means

[0436] The server generates specific and appropriate action instructions for each user based on the impact prediction results and the user's emotional state, including evacuation route guidance.

[0437] Action instruction notification means

[0438] The server immediately sends the generated action instructions to the user's device, which then notifies the user of the instructions via push notification or audio alert.

[0439] Data caching method

[0440] The device temporarily stores (caches) the latest data sent from the server, and can provide action instructions based on the cached data even if communication is interrupted.

[0441] Notification optimization method based on emotional state

[0442] The server adjusts the display method of action instructions (such as font size and audio level) according to the user's emotions. For users in a panic state, it adjusts visual and auditory elements to convey calming messages.

[0443] Specific examples

[0444] Example 1: An earthquake occurs and the user is anxious.

[0445] The server obtains data on the epicenter and seismic intensity, and predicts the extent of the impact using AI analysis.

[0446] The emotion recognition engine on User A's device determines that he is in an anxious state.

[0447] Based on the analysis results and emotion data, the server generates an action instruction such as "Please evacuate slowly to the nearest evacuation shelter" and sends this to User A's device.

[0448] The device will notify User A of this instruction in large letters and via a push notification.

[0449] Prompt Sentence Examples

[0450] Regarding apps that provide instructions for users to take action in the event of a disaster:

[0451] The system acquires the user's location information and combines it with disaster data to generate optimal instructions for action.

[0452] An emotion recognition engine is used to optimize the content of instructions and notification methods according to the user's emotional state.

[0453] Example 1: If an earthquake occurs and the user is anxious, instruct them to "evacuate slowly to the nearest evacuation shelter."

[0454] Example 2: A tsunami warning is issued and the user is in a panic

[0455] The server receives tsunami warning information and generates evacuation instructions to User B who lives in a coastal area to move to higher ground.

[0456] The device's emotion recognition engine determines that User B is in a panic.

[0457] The server generates a message saying, "Take a deep breath and calm down. Move slowly to safe, higher ground," and sends it to User B's terminal.

[0458] The device will display this message with an audio alert and a visual notification.

[0459] Prompt Sentence Examples

[0460] Regarding the Security Assistant app:

[0461] When a disaster occurs, optimal action instructions are generated based on the user's location information and emotional state.

[0462] Example 2: If a tsunami warning is issued and the user is panicking, audio and visual notifications will be provided to instruct them to "take a deep breath and stay calm. Move slowly to higher ground."

[0463] In this way, the present invention integrates the user's emotional state and location information when a disaster occurs, and provides optimal action instructions, thereby helping the user to respond quickly and accurately.

[0464] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0465] Step 1:

[0466] The server collects disaster data in real time from the Japan Meteorological Agency and local governments. At this time, information on earthquakes, tsunamis, floods, evacuation shelters, etc. is stored in a database and analyzed using AI models (e.g., TensorFlow, PyTorch).

[0467] Input: Disaster data from the Japan Meteorological Agency and local governments

[0468] Output: Storing disaster data in a database

[0469] Step 2:

[0470] The device acquires the user's location information via GPS or Wi-Fi and periodically sends it to the server, thereby updating the user's current location in real time.

[0471] Input: GPS or Wi-Fi location

[0472] Output: Send user location to server

[0473] Step 3:

[0474] The server uses AI algorithms to predict the impact of disasters based on collected disaster data and user location information, and this analysis assesses the risk level of a specific area.

[0475] Input: Disaster data, user location information

[0476] Output: Risk assessment results by region

[0477] Step 4:

[0478] The device's emotion recognition engine collects emotional data from the user's facial expressions and voice via the camera and microphone, analyzes their current emotional state, and sends this data to a server.

[0479] Input: User's facial expression and voice data

[0480] Output: Emotional state sent to the server

[0481] Step 5:

[0482] The server integrates the impact prediction results with the emotional data and uses an AI algorithm to generate optimal behavioral instructions for each individual user. These behavioral instructions are specific and include responses based on the user's emotional state.

[0483] Input: Regional risk assessment results, emotional state data

[0484] Output: Optimal action instructions

[0485] Step 6:

[0486] The server immediately sends the generated action instructions to each user's device, which then notifies the user of the instructions via push notification or voice alert.

[0487] Input: Optimal action instructions

[0488] Output: Notification of action instructions to the user device

[0489] Step 7:

[0490] The device caches the latest important data, so even if communication is interrupted, instructions can be displayed based on the cached data.

[0491] Input: Latest critical data from server

[0492] Output: Caching data

[0493] Step 8:

[0494] The device displays the received action instructions to the user, and the display method (character size, audio level, etc.) of the action instructions is optimized according to the user's emotional state.

[0495] Input: Action instructions, emotional state data

[0496] Output: Display of optimized action instructions

[0497] Step 9:

[0498] The user begins evacuation according to the instructions. The user's device continues to collect emotional state and location information and transmits it to the server.

[0499] Input: Evacuation behavior

[0500] Output: Continuous emotional state data and location information sent to a server

[0501] 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.

[0502] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0503] 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.

[0504] [Second embodiment]

[0505] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0506] 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.

[0507] 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).

[0508] 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.

[0509] 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.

[0510] 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).

[0511] 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.

[0512] 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.

[0513] 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.

[0514] 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.

[0515] 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.

[0516] 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."

[0517] overview

[0518] This invention is a system that enables individual users to take the most appropriate action quickly and accurately in the event of a disaster. This system comprehensively analyzes location information, disaster data, hazard maps, local government information, etc., and provides specific instructions to each user. It also has a function to provide instructions even when communication is cut off, and a tracking function to confirm the safety of family members.

[0519] Key Components

[0520] 1. Location information acquisition means

[0521] The device is equipped with a function to acquire the user's current location. Using GPS or Wi-Fi location services, the device periodically sends location data to a server.

[0522] 2. Disaster data collection methods

[0523] The server collects information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, and evacuation shelter information.

[0524] 3. Data Analysis Methods

[0525] The server analyzes the collected disaster data and user location information, and uses AI algorithms to predict the impact of the disaster, thereby generating optimal action instructions for each individual user.

[0526] 4. Action instruction generation means

[0527] The server generates specific instructions for each user based on the analysis results, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[0528] 5. Action instruction notification means

[0529] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via push notification or alarm.

[0530] 6. Data Caching Methods

[0531] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[0532] 7. Family Information Tracking Methods

[0533] The server tracks the location of family members based on pre-registered family information in the event of a disaster and sends that information to the user's device, which then displays their location and helps users confirm their safety.

[0534] Program processing explanation

[0535] Data collection and analysis

[0536] The server collects disaster data from the Japan Meteorological Agency and local governments at preset intervals, then analyzes the data using AI algorithms to predict the risk level for each area.

[0537] Action instruction generation and notification

[0538] Based on the analysis results, the server generates optimal instructions for each user and immediately sends them to the device, which then notifies the user of the received instructions via pop-up notifications or audio alerts.

[0539] Data caching and communication interruption support

[0540] The server caches the latest important data on each user's device in preparation for a disaster. Even if communication is interrupted, the device will display instructions based on the cached data.

[0541] Family Tracking

[0542] Users register their family members' information in the app in advance. When a disaster occurs, the server periodically obtains the family members' location information and sends it to the device. The device then displays the family members' location information to the user, helping them confirm their safety.

[0543] Specific examples

[0544] Example 1: When an earthquake occurs

[0545] The server obtains data on the epicenter and seismic intensity from the Japan Meteorological Agency and predicts the extent of the impact through AI analysis. If the area where User A is located is affected, the server generates an action instruction such as "Please evacuate to the nearest evacuation shelter" and sends it to User A's device. The device notifies User A of this instruction via a pop-up notification and a voice alert.

[0546] Example 2: When a tsunami warning is issued

[0547] The server acquires the tsunami warning information and generates an action instruction for User B living in a coastal area, such as "Please evacuate to higher ground," based on the analysis results. The device immediately notifies User B of this action instruction via push notification.

[0548] The processing flow will be explained below.

[0549] Step 1:

[0550] The server periodically collects real-time disaster data from various data sources (Japan Meteorological Agency, local governments, geographic information systems, etc.). The server uses APIs to obtain earthquake epicenter data, tsunami warnings, flood forecasts, etc. from the Japan Meteorological Agency, and also collects evacuation center location information from local governments.

[0551] Step 2:

[0552] The server stores the collected disaster data in a database and performs a comprehensive analysis using AI algorithms. The server overlays the acquired epicenter data with a hazard map to evaluate the risk level of each area. Different analysis methods are used for each type of disaster (earthquake, tsunami, flood, etc.).

[0553] Step 3:

[0554] The server uses each user's data, including their location information, to generate optimal instructions for each user based on the analysis results. For example, if evacuation is necessary, the server generates instructions such as "Please evacuate to the nearest evacuation shelter."

[0555] Step 4:

[0556] The server immediately sends the generated action instructions to each user's device using push notifications, with different notifications sent to each user.

[0557] Step 5:

[0558] The device notifies the user of the action instructions received from the server, displaying specific instructions such as "Please evacuate" or "Please stay at home" using pop-up notifications and audio alerts.

[0559] Step 6:

[0560] When a disaster is predicted, the server caches the latest important data on each user's device, so that even if communication is interrupted, the device can provide users with action instructions based on the cached data.

[0561] Step 7:

[0562] If communication is interrupted, the device will automatically load the cached data and display instructions to the user. The device will continuously display specific instructions along with the message "Displaying evacuation instructions based on data cached by this device."

[0563] Step 8:

[0564] Users can register their family members' information (such as names, contact information, and location sharing settings) in advance on the app's settings screen, which allows the system to help confirm the safety of family members in the event of a disaster.

[0565] Step 9:

[0566] The server periodically acquires and analyzes the location information of pre-registered family members in the event of a disaster. The location information of family members is tracked in real time, and the latest data is sent to the device.

[0567] Step 10:

[0568] The device displays the location information of the family sent from the server to the user, and displays a message such as "The family is currently evacuated to △△ Elementary School" to allow the user to confirm the safety of their family.

[0569] The above is a detailed explanation of the program's processing steps. This system provides support to users so that they can act quickly and appropriately in the event of a disaster.

[0570] Example 1

[0571] 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."

[0572] Conventional disaster prevention systems have problems with insufficient collection and analysis of disaster information and difficulty in assessing the situation in real time, making it difficult to provide prompt instructions for action. Furthermore, when communications are cut off, it is difficult to provide appropriate information, making it difficult to adequately confirm the safety of family members.

[0573] 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.

[0574] In this invention, the server includes a means for acquiring location information, a means for collecting disaster data, a means for predicting impacts based on the collected disaster data and location information, a means for generating optimal action instructions for individual users based on the predicted impacts, a means for transmitting the generated action instructions to each user's device, and a means for tracking the location information of family members and confirming their safety. This enables prompt and accurate action instructions to be provided in the event of a disaster and appropriate support even when communication is interrupted. It also enables the safety of family members to be confirmed in real time.

[0575] "Means for acquiring location information" refers to the technology or device used to accurately acquire the user's current location. Specifically, this includes GPS and Wi-Fi location services.

[0576] "Means of collecting disaster data" refers to technologies and devices for collecting disaster information on earthquakes, tsunamis, floods, etc. in real time provided by the Japan Meteorological Agency and local governments.

[0577] "Means for predicting impacts" refers to technologies and devices that use AI algorithms and other methods to analyze risk levels in specific areas based on collected disaster data and location information, and predict future impacts.

[0578] "Means for generating action instructions" refers to technology or devices that create information to instruct each user on the optimal action based on the predicted impact of the disaster. Examples include evacuation advisories and standby instructions.

[0579] The "means for transmitting action instructions" refers to a technique or device for transmitting the generated action instructions to each user's terminal in real time via a communication network.

[0580] The "means for notifying an action instruction" refers to a technique or device for immediately notifying the user of an action instruction received by the terminal. Specifically, this includes pop-up notifications, audio alerts, vibrations, etc.

[0581] The "means for displaying action instructions" refers to a technique or device that displays the notified action instructions on the screen of the terminal so that the user can visually confirm them.

[0582] "Data caching means" refers to technology or devices that temporarily store the latest information on a device when a disaster occurs. This cached data allows instructions to be provided even if communication is interrupted.

[0583] The "means for displaying action instructions when communication is interrupted" refers to a technique or device for displaying appropriate action instructions to the user based on cached data even when communication is interrupted.

[0584] "Means for tracking family location information" refers to technology or devices that track the location information of pre-registered family members in the event of a disaster and ensure their safety.

[0585] "Means for confirming safety" refers to technologies and devices that use family location information to enable users to check the safety status of their family members in real time.

[0586] MODE FOR CARRYING OUT THE INVENTION

[0587] overview

[0588] This invention is a support system that enables individual users to take the most appropriate action quickly and accurately when a disaster occurs. This system comprehensively analyzes location information, disaster data, hazard maps, and local government information, and provides specific instructions to each user. It also has a function to provide instructions even when communication is cut off, and a tracking function to confirm the safety of family members.

[0589] Key Components

[0590] 1. Location information acquisition means:

[0591] The device uses GPS and Wi-Fi location services to accurately determine the user's current location. The device periodically sends location data to a server.

[0592] Example: When a user carries a smartphone, it obtains the current latitude and longitude every minute and sends that data to a server.

[0593] 2. Disaster data collection methods:

[0594] The server collects real-time disaster information on earthquakes, tsunamis, floods, and other disasters provided by the Japan Meteorological Agency and local governments.

[0595] Example: When the Japan Meteorological Agency issues a new earthquake alert, the server collects the data immediately.

[0596] 3. Data analysis methods:

[0597] The server analyzes the collected disaster data and location information using an AI algorithm, which predicts the risk level for the user's location.

[0598] Example: The server uses data on the epicenter of an earthquake to determine whether the user's current location is safe.

[0599] 4. Action instruction generation means:

[0600] The server generates specific instructions for each user based on the results of the data analysis. For example, if evacuation is necessary, the server generates instructions such as "Please evacuate to the nearest evacuation shelter."

[0601] Example: If user A is in an area at risk of a tsunami, the server generates an action instruction such as "Please evacuate to higher ground."

[0602] 5. Action instruction notification means:

[0603] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via a pop-up notification, audio alert, or vibration.

[0604] Example: User B receives a voice alert saying "Please evacuate."

[0605] 6. Data caching methods:

[0606] The device caches the latest important data received from the server in case of a communication interruption, allowing the device to provide the user with action instructions even if communication is interrupted.

[0607] Example: When communication is lost, the device displays the cached "shelter location information."

[0608] 7. Family Information Tracking Methods:

[0609] Users register their family members' information in advance in the app. When a disaster occurs, the server periodically obtains their family members' location information and sends it to the user's device. The device then displays their location and helps users confirm their safety.

[0610] Example: The server verifies the location of User C's family and notifies User C's device that "the family has safely arrived at the evacuation shelter."

[0611] The specific hardware and software used

[0612] Hardware: Smartphones, tablets, servers, GPS modules, communication network equipment

[0613] Software: Location services, real-time data collection software, AI analysis algorithms, notification systems, cache management software

[0614] Explanation of operation using concrete examples

[0615] If an earthquake occurs:

[0616] 1. The server obtains data on the epicenter and intensity of the earthquake from the Japan Meteorological Agency.

[0617] 2. The device sends the user's new location information to the server.

[0618] 3. The server uses AI analysis to predict the impact from the epicenter and determines that the area where User A is located is dangerous.

[0619] 4. The server generates an action instruction such as "Please evacuate to the nearest evacuation shelter" and sends it to User A's device.

[0620] 5. The device notifies User A with a pop-up notification and a voice alert.

[0621] If a tsunami warning is issued:

[0622] 1. The server obtains tsunami warning information.

[0623] 2. The server checks the location information of user B, who lives in a coastal area, and generates an instruction to "evacuate to higher ground."

[0624] 3. The server immediately sends this action instruction to User B's device.

[0625] 4. The device immediately notifies User B via push notification and vibration.

[0626] Example prompt statement

[0627] Examples of prompts to illustrate specific usage scenarios are:

[0628] When an earthquake occurs, User A receives instructions via his smartphone saying, "Please evacuate to the nearest evacuation shelter." Based on these instructions, he quickly begins evacuation.

[0629] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0630] Step 1: Obtaining location information

[0631] The device obtains the user's current location. Specifically, it uses GPS or Wi-Fi location services. As input, a trigger occurs that causes the device to periodically obtain the user's current location. As output, the obtained latitude and longitude data is generated. The device then sends this location data to the server.

[0632] Specific operation: The device obtains the user's current latitude and longitude every minute and sends them to the server.

[0633] Step 2: Collect disaster data

[0634] The server collects disaster data in real time from the Japan Meteorological Agency and local governments. As input, new disaster information is acquired from the servers of the Japan Meteorological Agency and local governments. As output, data such as earthquake epicenters, tsunami warnings, and flood forecasts are stored on the server.

[0635] Specific operation: The server periodically calls the Japan Meteorological Agency's API to obtain the latest earthquake data and stores it in a database.

[0636] Step 3: Analyze the data

[0637] The server uses an AI algorithm to analyze the collected disaster data and received location information. The disaster data and location information are provided as input. The output is a risk level index generated as a result of the analysis. Based on this risk level, a basis for optimal action instructions for each user is created.

[0638] How it works: The server uses an AI model to analyze earthquake data and the user's location to calculate the risk level for the user's location.

[0639] Step 4: Generate action instructions

[0640] The server generates specific instructions for each user based on the data analysis results. The risk level indicator is used as input. Instructions for each user are generated as output. These instructions include, for example, "Please evacuate to the nearest evacuation shelter" or "Please stay at home."

[0641] Specific operation: Based on the risk level indicator, the server generates action instructions such as "Evacuate to higher ground" for users in areas where a tsunami warning has been issued.

[0642] Step 5: Notification of instructions to act

[0643] The server immediately sends the generated action instructions to each user's device. The device notifies the user of the received action instructions via a pop-up notification or a voice alert. The action instruction data is sent from the server as input. The notification itself is sent as output.

[0644] Specific operation: The server sends evacuation instructions to the user's device, which then issues a pop-up notification and an audio alert.

[0645] Step 6: Caching Data

[0646] The terminal caches the most recent important data received from the server in case of a communication interruption. The most recent action instruction data is cached as input. The cached data to be used when communication is interrupted is obtained as output.

[0647] Specific operation: The device locally stores the "shelter location information" received from the server and prepares to display it when communication is lost.

[0648] Step 7: Tracking Family Information

[0649] Users register their family members' information in the app in advance. When a disaster occurs, the server periodically obtains the location information of the registered family members and sends it to the user's device. The family members' location information is sent to the server as input. The family members' location data is displayed on the user's device as output.

[0650] Specific operation: The server periodically obtains the family's location data and notifies User C that the family has safely arrived at the evacuation shelter.

[0651] (Application example 1)

[0652] 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."

[0653] In the event of a disaster, it is extremely important to quickly and accurately ensure the safety of workers and machinery in logistics centers. However, conventional systems have insufficient disaster information collection and analysis, making it impossible to track location information in real time or generate and notify appropriate action instructions. Furthermore, there is a lack of a way to provide accurate action instructions even in the event of a communications outage. This can lead to reduced efficiency in logistics operations and delays in ensuring the safety of workers.

[0654] 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.

[0655] In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impacts based on the collected disaster data, means for generating optimal actions for individual users based on the predicted impacts, means for transmitting the generated action instructions to each user's terminal, means for acquiring and notifying location information of equipment and workers in the logistics center, and means for predicting the impacts on the logistics center in the event of a disaster and generating optimal action instructions. This makes it possible to quickly and accurately ensure the safety of workers and equipment in the logistics center even in the event of a disaster and prevent a decline in work efficiency.

[0656] "Location information acquisition means" refers to technologies or devices used to determine the current location of a user or device, including GPS functionality and Wi-Fi location services.

[0657] "Means of collecting disaster data" refers to systems and services for collecting disaster-related information provided by official organizations such as the Japan Meteorological Agency and local governments.

[0658] The "means for predicting impact" is a system that uses AI algorithms and analytical technology to estimate the impact of a disaster based on collected disaster data and location information.

[0659] The "means for generating optimal behavior" is an algorithm or program that determines and generates the best behavior that a user or device should take based on the predicted impact.

[0660] The "means for transmitting behavioral instructions to each user's terminal" refers to a communication technology or protocol for transmitting the generated behavioral instructions to each user's electronic device.

[0661] "Means for acquiring and notifying location information of equipment and workers within a logistics center" refers to a system for identifying the current location of machines and workers used within a logistics center and informing managers and other relevant parties.

[0662] The "means of predicting the impact on logistics centers in the event of a disaster and generating optimal action instructions" is a system that analyzes risks at logistics centers in the event of a disaster and determines safe and effective response measures based on that analysis.

[0663] overview

[0664] This invention is a system for quickly and accurately ensuring the safety of workers and equipment in a logistics center in the event of a disaster. This system comprehensively utilizes location information, disaster data, risk analysis data, etc. to provide specific action instructions to individual users. It also has the ability to provide instructions even when communication is interrupted, and the ability to track locations.

[0665] Key Components

[0666] 1. Location information acquisition means

[0667] The server periodically collects the current locations of equipment and workers within the logistics center using GPS and Wi-Fi location information services, making it possible to grasp overall location information within the logistics center.

[0668] 2. Disaster data collection methods

[0669] The server collects disaster information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, etc. This data is periodically retrieved by the server using an API.

[0670] 3. Data Analysis Methods

[0671] The server analyzes the collected disaster data and location information and uses generative AI models and algorithms to predict the impact of the disaster, thereby identifying the risk level for each piece of equipment and worker.

[0672] 4. Action instruction generation means

[0673] Based on the analysis results, the server generates optimal action instructions for each piece of equipment and worker in the logistics center, including instructions for safe evacuation routes and instructions to suspend work.

[0674] 5. Action instruction notification means

[0675] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received action instructions via a pop-up notification or a voice alert.

[0676] 6. Data Caching Methods

[0677] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[0678] Operational flow and concrete examples

[0679] Server-side processing

[0680] The server periodically obtains location information within the logistics center using the location information acquisition means. It also collects real-time disaster information from outside using the disaster data collection means. Based on this data, it predicts the impact of the disaster using the data analysis means. For example, if an earthquake occurs nearby, the server analyzes the location of the epicenter and the logistics center and predicts the impact. Next, based on the predicted impact, the action instruction generation means generates optimal action instructions for each piece of equipment and worker. These action instructions relate to safe evacuation routes within the logistics center, interruptions to work, etc.

[0681] Terminal side processing

[0682] When the device receives the action instructions sent from the server, it notifies the user of the contents. The notification is displayed as a pop-up or a sound alert. The device also uses a data caching means to cache the latest important data. Even if communication is interrupted, the device can display the action instructions based on the cached data.

[0683] Specific examples

[0684] Example 1: High flood risk

[0685] If a logistics center is located near a river, the server obtains flood information from the Japan Meteorological Agency and predicts the impact. If the risk is deemed high, the server generates an action instruction to "evacuate to higher ground" and notifies the worker's device. The device immediately notifies the worker of this instruction via a voice alert.

[0686] Example 2: Structural risk of a logistics center due to an earthquake

[0687] If a major earthquake occurs nearby, the server analyzes data on the epicenter and the location of the logistics center to predict the impact. If it determines that the impact on the entire logistics center will be significant, the server generates an action instruction stating, "We recommend you evacuate outside the building," and notifies the user's device. This allows forklifts and workers to quickly evacuate outside the building.

[0688] Prompt Sentence Examples

[0689] Please describe the detailed features of the "Disaster Response Logistics Management App." Please include the following elements:

[0690] Location information acquisition function

[0691] Disaster data collection function

[0692] Data analysis function

[0693] Action instruction generation function

[0694] Action instruction notification function

[0695] Data cache function

[0696] Worker tracking function

[0697] Specific examples include instructions for action in cases of high flood risk or earthquake risk.

[0698] As described above, by using this system, it is possible to ensure the safety of workers and equipment within the logistics center and prevent a decline in work efficiency.

[0699] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0700] Step 1:

[0701] The server periodically collects the current locations of equipment and workers in the logistics center using location information acquisition means. The input is GPS data of the equipment and workers, and the output is a real-time location information list. Specifically, the server receives the GPS coordinates sent from each device and stores them in a list.

[0702] Step 2:

[0703] The server uses disaster data collection tools to collect disaster information in real time from the Japan Meteorological Agency and local governments. The input is disaster data from the public API, and the output is a disaster information database. Specifically, the weather information and disaster notifications obtained via the API are stored in the database and updated as needed.

[0704] Step 3:

[0705] The server uses data analysis tools to analyze the collected disaster data and location information. The input is a disaster information database and a location information list, and the output is impact prediction data. Specifically, a generative AI model is used to run an algorithm that predicts the impact of a disaster based on the location information of each piece of equipment and worker.

[0706] Step 4:

[0707] The server uses the action instruction generation means to generate optimal action instructions for each piece of equipment and worker in the logistics center based on the prediction results. The input is impact prediction data, and the output is specific action instructions. Specifically, action instructions (e.g., "evacuate to the nearest evacuation shelter" or "move to a safe location") are automatically generated according to the risk level.

[0708] Step 5:

[0709] The server uses the action instruction notification means to instantly send the generated action instructions to each user's device. The input is the generated action instruction, and the output is a notification to each user's device. Specifically, the action instruction is sent to each device and notified to the user as a push notification or a voice alert.

[0710] Step 6:

[0711] The terminal uses a data caching means to cache the latest data sent from the server in case of a communication interruption. The input is important data from the server, and the output is cached data. Specifically, the latest disaster information and action instructions are periodically saved locally to prepare for communication interruptions.

[0712] Step 7:

[0713] The user takes appropriate action based on the received action instructions. The input is an action instruction notification from the device, and the output is a change in the user's behavior. Specifically, the user takes specific action, such as following the evacuation instructions displayed on the device and moving to a safe place.

[0714] 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.

[0715] overview

[0716] This invention combines a system that provides optimal action instructions to individuals based on their location information when a disaster occurs with an emotion engine that recognizes the user's emotions. This allows the content and display method of action instructions to be optimized according to the user's emotional state, providing more effective support.

[0717] Key Components

[0718] 1. Location information acquisition means

[0719] The device is equipped with a function to acquire the user's current location. Using GPS or Wi-Fi location services, the device periodically sends location data to a server.

[0720] 2. Disaster data collection methods

[0721] The server collects information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, and evacuation shelter information.

[0722] 3. Data Analysis Methods

[0723] The server analyzes the collected disaster data and user location information, and uses AI algorithms to predict the impact of the disaster, thereby generating optimal action instructions for each individual user.

[0724] 4. Action instruction generation means

[0725] The server generates specific instructions for each user based on the analysis results, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[0726] 5. Action instruction notification means

[0727] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via push notification or alarm.

[0728] 6. Data Caching Methods

[0729] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[0730] 7. Family Information Tracking Methods

[0731] The server tracks the location of family members based on pre-registered family information in the event of a disaster and sends that information to the user's device, which then displays their location and helps users confirm their safety.

[0732] 8. Emotion Engine

[0733] The device is equipped with an engine for recognizing the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state.

[0734] 9. Emotional Data Analysis Methods

[0735] The server receives the user's emotional data and integrates it with the disaster data analysis results to optimize behavioral instructions based on the user's emotional state.

[0736] Program processing explanation

[0737] Data collection and analysis

[0738] The server periodically collects disaster data from the Japan Meteorological Agency and local governments and stores it in a database. The server then applies AI algorithms to assess the risk level of each area. Based on the analysis results, it generates optimal action instructions taking into account the user's location information.

[0739] Action instruction generation and notification

[0740] The server generates specific instructions for each user based on the analysis results. The instructions are then immediately sent to the user's device. The device then notifies the user of the received instructions via push notification or voice alert.

[0741] Data caching and communication interruption support

[0742] The server caches the latest important data on each user's device in preparation for a disaster. Even if communication is interrupted, the device can provide the user with instructions based on the cached data. For example, when communication is interrupted, the device displays the message "Displaying evacuation instructions based on data cached by this device."

[0743] Family Tracking

[0744] Users register their family information in advance on the app's settings screen. When a disaster occurs, the server periodically obtains and analyzes the family's location information. The latest location information is sent to the user's device, which then displays a message such as, "The family is currently evacuated to XX Elementary School."

[0745] Implementing the Emotion Engine

[0746] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state. For example, if the user is feeling anxious, it will recognize this and send emotional data to the server.

[0747] Emotional data analysis and optimization of behavioral instructions

[0748] The server receives the user's emotional data and integrates it with disaster data analysis results to optimize instructions. For example, if the user is in a panic, the server will adjust the notification display (such as font size and audio level) to provide detailed evacuation instructions along with a message to "stay calm."

[0749] Specific examples

[0750] Example 1: An earthquake occurs and the user is anxious.

[0751] The server acquires data on the epicenter and seismic intensity, and uses AI analysis to predict the extent of the impact. User A's device uses an emotion engine to send a message to the server indicating that User A is in an anxious state. Based on the analysis results and emotion data, the server generates action instructions such as "Please evacuate slowly to the nearest evacuation shelter" and sends them to User A's device. The device then notifies User A of these instructions in large, easy-to-read letters.

[0752] Example 2: A tsunami warning is issued and the user is in a panic

[0753] The server receives the tsunami warning information and generates an action instruction for User B, who lives in a coastal area, saying, "Please evacuate to higher ground." Meanwhile, the device notifies the server that User B is in a panic. The server takes into account the emotion data and generates a message such as, "Take a deep breath and stay calm. Move slowly to safe higher ground," and sends it to User B's device.

[0754] The processing flow will be explained below.

[0755] Step 1:

[0756] The server periodically collects disaster data from various data sources (Japan Meteorological Agency, local governments, etc.). Specifically, the server uses APIs to obtain the latest data such as epicenter information, tsunami warnings, and flood forecasts from the Japan Meteorological Agency, and also collects location information of evacuation centers from local governments.

[0757] Step 2:

[0758] The server stores the collected disaster data in a database, updates the database, and manages all information appropriately.

[0759] Step 3:

[0760] The server analyzes the collected disaster data using AI algorithms, which allows the server to assess the risk level of each area and generate predictions.

[0761] Step 4:

[0762] The server obtains the user's location information from the device and generates optimal instructions for each user based on that location information. The server takes into account the analysis results and creates specific instructions such as "evacuate to the nearest evacuation shelter" or "stay at home."

[0763] Step 5:

[0764] The server immediately sends the generated action instructions to each user's device. The server uses push notifications to send localized action instructions to each device.

[0765] Step 6:

[0766] The device notifies the user of the action instructions received from the server, displaying instructions such as "Please evacuate" or "Please stay at home" using pop-up notifications and audio alerts.

[0767] Step 7:

[0768] When a disaster is predicted, the server caches the latest important data on each user's device, allowing the device to provide action instructions based on the cached data even if communication is interrupted.

[0769] Step 8:

[0770] If communication is interrupted, the device will automatically load cached data and display instructions to the user. The device will continuously display specific instructions along with the message "Displaying evacuation instructions based on data cached by this device."

[0771] Step 9:

[0772] Users enter their family members' information in advance through the app's settings screen, including their names, contact details, and location sharing settings.

[0773] Step 10:

[0774] When a disaster occurs, the server periodically obtains the location information of pre-registered family members, tracks their location in real time, and collects and analyzes the latest data.

[0775] Step 11:

[0776] The server sends updated family location information to each user's device. The server periodically sends family location information to each user's device so that the user can check the information.

[0777] Step 12:

[0778] The device displays the location information of the family sent from the server to the user, and displays a message such as "The family is currently evacuated to △△ Elementary School" to allow the user to confirm the safety of their family.

[0779] Step 13:

[0780] The device uses an emotion engine to recognize the user's emotions by analyzing the user's facial expressions, voice, and other biometric signals. The emotion engine determines the user's emotional state (e.g., anxiety, fear, panic, etc.).

[0781] Step 14:

[0782] The device sends the recognized emotion data to the server, which receives the emotion data and integrates it with the disaster data analysis results.

[0783] Step 15:

[0784] The server optimizes the analysis results and action instructions based on the emotional data. For example, if the user is in a panic state, it will add a message such as "Please stay calm."

[0785] Step 16:

[0786] The server sends optimized behavioral instructions to each user's device, which then displays appropriate instructions according to the user's emotional state, helping the user to act calmly.

[0787] The above is a detailed description of the processing steps of the disaster prevention system that combines the emotion engine. This system provides appropriate action instructions that take into account the user's emotional state, thereby more effectively ensuring the user's safety in the event of a disaster.

[0788] Example 2

[0789] 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."

[0790] Conventional disaster response systems have been effective to a certain extent in providing action instructions based on the user's location information and disaster data, but because they do not take the user's emotional state into consideration, there are issues with the effectiveness of the action instructions being insufficient in situations where users are prone to stress or panic. There is also the problem that it is difficult to provide appropriate action instructions to users when communication is cut off.

[0791] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impacts based on the collected disaster data, means for generating optimal actions for individual users based on the predicted impacts, means for transmitting the generated action instructions to each user's terminal, means for analyzing the emotional state of the user, and means for optimizing the action instructions taking the emotional state into consideration. This makes it possible to provide optimal action instructions according to the emotional state, reduce stress and panic in users when a disaster occurs, and provide appropriate action instructions even when communication is interrupted.

[0792] "Location information acquisition means" refers to a function for acquiring the user's current location, and collects location data using technologies such as GPS and Wi-Fi.

[0793] "Disaster data collection means" refers to the function for collecting disaster data from the Japan Meteorological Agency and local governments in real time, including information on earthquakes, tsunamis, floods, etc.

[0794] "Impact prediction means" refers to a function that uses an AI algorithm to predict impact based on collected disaster data, and analyzes the user's location information and disaster data to assess the risk level.

[0795] The "action instruction generation means" refers to a function that generates optimal action instructions for each user based on the impact prediction, and specifically includes guidance on evacuation sites and instructions to stay at home.

[0796] The "action instruction sending means" refers to a function for sending the generated action instructions to each user's terminal, and aims to quickly deliver the action instructions to the user.

[0797] "Emotional state analysis means" refers to a function for analyzing the user's emotional state, which determines emotions by analyzing the user's facial expressions, voice, and biological signals.

[0798] The "action instruction optimization means" refers to a function that takes into account the user's emotional state and appropriately adjusts action instructions accordingly, changing the content and display format of notifications based on emotional data.

[0799] "Push notification" refers to a method in which a server instantly sends information to a device to notify the user, and is a means of quickly delivering important information and instructions for action.

[0800] "Data caching means" refers to a function that stores the latest important data on the terminal in preparation for communication interruptions, enabling appropriate action instructions to be provided even in the event of a disaster.

[0801] overview

[0802] This invention combines a system that provides optimal action instructions based on the user's location information when a disaster occurs with an emotion engine that recognizes the user's emotional state. This enables the content and display method of action instructions to be optimized according to the user's emotional state, making it possible to provide more effective support.

[0803] System Program Overview

[0804] The system includes a location information acquisition means, a disaster data collection means, an impact prediction means, an action instruction generation means, an action instruction transmission means, an emotional state analysis means, and an action instruction optimization means.

[0805] 1. Location information acquisition means

[0806] The device uses GPS and Wi-Fi location services to obtain the user's current location, and periodically sends the obtained location data to a server.

[0807] 2. Disaster data collection methods

[0808] The server collects disaster data on earthquakes, tsunamis, floods, etc. in real time from the Japan Meteorological Agency and local governments, and stores this data in a database.

[0809] 3. Impact prediction methods

[0810] The server applies AI algorithms based on the collected disaster data and user location information to assess risk levels and predict the impact of disasters, generating optimal action instructions for each user.

[0811] 4. Action instruction generation means

[0812] Based on the predicted impact, the server generates specific instructions for each user, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[0813] 5. Means of sending instructions for action

[0814] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions using push notifications or voice alerts.

[0815] 6. Emotional state analysis method

[0816] The device is equipped with an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state. The determined emotion data is then sent to a server.

[0817] 7. Action Instruction Optimization Method

[0818] The server receives the user's emotional data and integrates it with disaster data analysis results to optimize instructions. For example, if the user is in a panic, it will provide detailed evacuation instructions along with a message such as "Please stay calm." The notification display method (font size, audio level, etc.) is also adjusted according to the emotional data.

[0819] Specific examples

[0820] Example 1: An earthquake occurs and the user is anxious.

[0821] The server collects data on the epicenter and seismic intensity, and uses AI analysis to predict the extent of the impact.

[0822] The terminal transmits to the server, via the emotion engine, that User A is in an anxious state.

[0823] Based on the analysis results and emotion data, the server generates action instructions such as "Please evacuate slowly to the nearest evacuation shelter" and sends them to User A's device.

[0824] The terminal notifies User A of this instruction in large, easy-to-read letters.

[0825] Example 2: A tsunami warning is issued and the user is in a panic

[0826] The server receives tsunami warning information and generates an action instruction for User B who lives in a coastal area, such as "Evacuate to higher ground."

[0827] The device detects that User B is in a panic state using its emotion engine and notifies the server.

[0828] The server takes into consideration the emotion data and generates an action instruction such as "Take a deep breath and calm down. Move slowly to safe, higher ground," and sends it to User B's terminal.

[0829] The terminal displays the received instruction together with a voice message, providing User B with a sense of security.

[0830] Prompt Sentence Examples

[0831] Below are some example prompts to input to a generative AI model:

[0832] "Earthquake information has been obtained. The epicenter is in Tokyo, with a seismic intensity of 6+. The user is currently in Ota Ward and is feeling uneasy. Please generate the most appropriate instructions for action."

[0833]

[0834] "A tsunami warning has been issued. The user lives in Yokohama and is currently in a state of panic. Please take a deep breath and generate a calming message and evacuation instructions."

[0835] This will realize a system that can provide users with prompt and effective support in the event of a disaster and provide optimal action instructions according to their emotional state.

[0836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0837] Step 1:

[0838] Location information collection

[0839] Input: User's current location (GPS data and / or Wi-Fi location).

[0840] Specific operation: The device periodically collects the user's current location. Specifically, it obtains location information using GPS and Wi-Fi.

[0841] Output: Send the acquired location data to the server.

[0842] Step 2:

[0843] Disaster data collection

[0844] Input: Disaster information (earthquakes, tsunamis, floods, etc.) from the Japan Meteorological Agency and local governments.

[0845] Specific operation: The server collects real-time disaster data through APIs from the Japan Meteorological Agency and local governments.

[0846] Output: Collected disaster data is stored in a database.

[0847] Step 3:

[0848] Data analysis

[0849] Input: Location information from step 1 and disaster data from step 2.

[0850] How it works: The server uses AI algorithms to analyze location information and disaster data, and then evaluates the risk level for each user's current location.

[0851] Output: Generate risk assessment results according to each user's location.

[0852] Step 4:

[0853] Collecting Emotional Data

[0854] Input: User's facial, voice, and biometric data.

[0855] Specific operation: The device uses an emotion engine to analyze the user's facial expressions, voice, and biometric signals to determine their current emotional state.

[0856] Output: The determined emotion data is sent to the server.

[0857] Step 5:

[0858] Generate action instructions

[0859] Input: Risk assessment results from step 3 and sentiment data from step 4.

[0860] Specific operation: The server integrates the risk assessment results and emotional data, and generates optimal instructions for action using an AI algorithm. Specifically, it creates messages and instructions for action that are tailored to the user's emotional state.

[0861] Output: Generate optimized action instructions for each user.

[0862] Step 6:

[0863] Sending action instructions and notifications

[0864] Input: Action instructions generated in step 5.

[0865] Specific operation: The server immediately sends the generated action instructions to the user's device, and the device notifies the user of the received action instructions via push notification or voice alert.

[0866] Output: Instructions for action sent to the user's device.

[0867] Step 7:

[0868] Data Cache

[0869] Input: Latest critical data (disaster data from Step 2 and action instructions from Step 5).

[0870] Specific operation: The device caches the latest important data in preparation for communication interruptions, making it possible to respond even in the event of a disaster.

[0871] Output: Cached sensitive data.

[0872] Step 8:

[0873] Use of cached data

[0874] Input: State when communication is interrupted.

[0875] Specific operation: If communication is interrupted, the device will use cached data to provide instructions to the user, specifically displaying the message "Providing evacuation instructions based on data cached by this device."

[0876] Output: Action instructions provided even in the event of communication blackout.

[0877] Step 9:

[0878] Family Tracking

[0879] Input: Family information registered by the user.

[0880] Specific operation: Users register their family members' information in advance on the app's settings screen. When a disaster occurs, the server periodically obtains the family members' location information and sends it to the user's device.

[0881] Output: Location information and updates of family members.

[0882] Step 10:

[0883] Family location notifications

[0884] Input: Family location information from step 9.

[0885] Specific operation: The device notifies the user of the latest location information, saying, "Your family has evacuated to △△ Elementary School."

[0886] Output: Family location information displayed on the user's device.

[0887] (Application example 2)

[0888] 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."

[0889] Conventional disaster response systems can generate action instructions based on a user's location information and disaster information, but they cannot take the user's emotional state into consideration. As a result, if a user is in a state of panic or anxiety, appropriate action instructions cannot be provided quickly, making it difficult to evacuate efficiently and ensure safety. Furthermore, if the information notification method is not appropriate for the user's emotional state, there is also the problem that instructions cannot be effectively conveyed. The present invention aims to solve these problems and support rapid and appropriate evacuation by providing optimal action instructions based on the user's emotional state during a disaster.

[0890] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impact based on the collected disaster data, means for analyzing the emotional state using an emotion engine that recognizes the user's emotions, means for generating optimal actions for each user based on the predicted impact and emotional state, and means for transmitting the generated action instructions to each user's terminal. As a result, optimal action instructions are generated taking into account the user's location information and emotional state when a disaster occurs, enabling quick and appropriate evacuation and safety assurance.

[0891] "Means for acquiring location information" refers to a function that collects the user's current location using technologies such as GPS or Wi-Fi and transmits it to a server.

[0892] "Means for collecting disaster data" refers to the function of acquiring disaster-related information provided by the Japan Meteorological Agency and local governments in real time and storing it in a database.

[0893] "Means for predicting impact based on collected disaster data" refers to a function that analyzes collected disaster data and uses an AI algorithm to predict the impact of a disaster on specific regions or users.

[0894] The "means for analyzing the emotional state by utilizing an emotion engine that recognizes the user's emotions" is an engine that analyzes the user's facial expressions, voice, and other biometric signals to determine the emotional state at that time.

[0895] The "means for generating optimal behavior for each individual user" is a function that generates specific and appropriate behavioral instructions for each user based on the predicted impact results and emotional state.

[0896] The "means for transmitting generated action instructions to each user's terminal" is a function for instantly transmitting action instructions generated by the server to the user's terminal such as a smartphone or smart glasses.

[0897] The "means for notifying the user of the transmitted action instruction" is a function for notifying the user of the received action instruction through a push notification or a voice alert.

[0898] The "means for displaying the notified action instruction to the user" is a function for displaying the received action instruction on the terminal screen of the user.

[0899] The "means for optimizing the display method of action instructions according to the emotional state of the user" is a function for adjusting the display method of action instructions (character size, audio level, etc.) based on the emotional state of the user.

[0900] "Means for caching the latest data on the terminal when a disaster occurs" is a function that temporarily stores important data on the terminal when a disaster occurs.

[0901] The "means for displaying action instructions based on cached data when communication is interrupted" is a function that provides action instructions to the user using cached data when communication is interrupted.

[0902] MODE FOR CARRYING OUT THE INVENTION

[0903] overview

[0904] This invention combines a system that provides optimal action instructions to individuals based on their location information when a disaster occurs with an emotion engine that recognizes the user's emotions. This system optimizes the content and display method of action instructions according to the user's emotional state, making it possible to provide more effective support.

[0905] Main Components and Processing

[0906] The main components required for this system are as follows:

[0907] Location information acquisition means

[0908] The server collects location information from users' devices, such as smartphones and smart glasses, using GPS and Wi-Fi, and periodically transmits this location information to the server.

[0909] Disaster data collection methods

[0910] The server collects disaster information from the Japan Meteorological Agency and local governments, including earthquake, tsunami, flood, and evacuation shelter information. The collected data is stored on the server.

[0911] Impact prediction methods

[0912] The server analyzes the collected disaster data using AI algorithms (such as TensorFlow and PyTorch) to predict the impact of the disaster on specific areas and users.

[0913] emotion recognition means

[0914] The device is equipped with an emotion engine (such as Affectiva SDK or Microsoft Azure Face API) that recognizes the user's emotions. It uses the camera and microphone to analyze the user's facial expressions and voice to determine their current emotional state.

[0915] Action instruction generation means

[0916] The server generates specific and appropriate action instructions for each user based on the impact prediction results and the user's emotional state, including evacuation route guidance.

[0917] Action instruction notification means

[0918] The server immediately sends the generated action instructions to the user's device, which then notifies the user of the instructions via push notification or audio alert.

[0919] Data caching method

[0920] The device temporarily stores (caches) the latest data sent from the server, and can provide action instructions based on the cached data even if communication is interrupted.

[0921] Notification optimization method based on emotional state

[0922] The server adjusts the display method of action instructions (such as font size and audio level) according to the user's emotions. For users in a panic state, it adjusts visual and auditory elements to convey calming messages.

[0923] Specific examples

[0924] Example 1: An earthquake occurs and the user is anxious.

[0925] The server obtains data on the epicenter and seismic intensity, and predicts the extent of the impact using AI analysis.

[0926] The emotion recognition engine on User A's device determines that he is in an anxious state.

[0927] Based on the analysis results and emotion data, the server generates an action instruction such as "Please evacuate slowly to the nearest evacuation shelter" and sends this to User A's device.

[0928] The device will notify User A of this instruction in large letters and via a push notification.

[0929] Prompt Sentence Examples

[0930] Regarding apps that provide instructions for users to take action in the event of a disaster:

[0931] The system acquires the user's location information and combines it with disaster data to generate optimal instructions for action.

[0932] An emotion recognition engine is used to optimize the content of instructions and notification methods according to the user's emotional state.

[0933] Example 1: If an earthquake occurs and the user is anxious, instruct them to "evacuate slowly to the nearest evacuation shelter."

[0934] Example 2: A tsunami warning is issued and the user is in a panic

[0935] The server receives tsunami warning information and generates evacuation instructions to User B who lives in a coastal area to move to higher ground.

[0936] The device's emotion recognition engine determines that User B is in a panic.

[0937] The server generates a message saying, "Take a deep breath and calm down. Move slowly to safe, higher ground," and sends it to User B's terminal.

[0938] The device will display this message with an audio alert and a visual notification.

[0939] Prompt Sentence Examples

[0940] Regarding the Security Assistant app:

[0941] When a disaster occurs, optimal action instructions are generated based on the user's location information and emotional state.

[0942] Example 2: If a tsunami warning is issued and the user is panicking, audio and visual notifications will be provided to instruct them to "take a deep breath and stay calm. Move slowly to higher ground."

[0943] In this way, the present invention integrates the user's emotional state and location information when a disaster occurs, and provides optimal action instructions, thereby helping the user to respond quickly and accurately.

[0944] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0945] Step 1:

[0946] The server collects disaster data in real time from the Japan Meteorological Agency and local governments. At this time, information on earthquakes, tsunamis, floods, evacuation shelters, etc. is stored in a database and analyzed using AI models (e.g., TensorFlow, PyTorch).

[0947] Input: Disaster data from the Japan Meteorological Agency and local governments

[0948] Output: Storing disaster data in a database

[0949] Step 2:

[0950] The device acquires the user's location information via GPS or Wi-Fi and periodically sends it to the server, thereby updating the user's current location in real time.

[0951] Input: GPS or Wi-Fi location

[0952] Output: Send user location to server

[0953] Step 3:

[0954] The server uses AI algorithms to predict the impact of disasters based on collected disaster data and user location information, and this analysis assesses the risk level of a specific area.

[0955] Input: Disaster data, user location information

[0956] Output: Risk assessment results by region

[0957] Step 4:

[0958] The device's emotion recognition engine collects emotional data from the user's facial expressions and voice via the camera and microphone, analyzes their current emotional state, and sends this data to a server.

[0959] Input: User's facial expression and voice data

[0960] Output: Emotional state sent to the server

[0961] Step 5:

[0962] The server integrates the impact prediction results with the emotional data and uses an AI algorithm to generate optimal behavioral instructions for each individual user. These behavioral instructions are specific and include responses based on the user's emotional state.

[0963] Input: Regional risk assessment results, emotional state data

[0964] Output: Optimal action instructions

[0965] Step 6:

[0966] The server immediately sends the generated action instructions to each user's device, which then notifies the user of the instructions via push notification or voice alert.

[0967] Input: Optimal action instructions

[0968] Output: Notification of action instructions to the user device

[0969] Step 7:

[0970] The device caches the latest important data, so even if communication is interrupted, instructions can be displayed based on the cached data.

[0971] Input: Latest critical data from server

[0972] Output: Caching data

[0973] Step 8:

[0974] The device displays the received action instructions to the user, and the display method (character size, audio level, etc.) of the action instructions is optimized according to the user's emotional state.

[0975] Input: Action instructions, emotional state data

[0976] Output: Display of optimized action instructions

[0977] Step 9:

[0978] The user begins evacuation according to the instructions. The user's device continues to collect emotional state and location information and transmits it to the server.

[0979] Input: Evacuation behavior

[0980] Output: Continuous emotional state data and location information sent to a server

[0981] 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.

[0982] 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.

[0983] 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.

[0984] [Third embodiment]

[0985] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0986] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0987] 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).

[0988] 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.

[0989] 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.

[0990] 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).

[0991] 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.

[0992] 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.

[0993] 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.

[0994] 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.

[0995] 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.

[0996] 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."

[0997] overview

[0998] This invention is a system that enables individual users to take the most appropriate action quickly and accurately in the event of a disaster. This system comprehensively analyzes location information, disaster data, hazard maps, local government information, etc., and provides specific instructions to each user. It also has a function to provide instructions even when communication is cut off, and a tracking function to confirm the safety of family members.

[0999] Key Components

[1000] 1. Location information acquisition means

[1001] The device is equipped with a function to acquire the user's current location. Using GPS or Wi-Fi location services, the device periodically sends location data to a server.

[1002] 2. Disaster data collection methods

[1003] The server collects information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, and evacuation shelter information.

[1004] 3. Data Analysis Methods

[1005] The server analyzes the collected disaster data and user location information, and uses AI algorithms to predict the impact of the disaster, thereby generating optimal action instructions for each individual user.

[1006] 4. Action instruction generation means

[1007] The server generates specific instructions for each user based on the analysis results, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[1008] 5. Action instruction notification means

[1009] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via push notification or alarm.

[1010] 6. Data Caching Methods

[1011] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[1012] 7. Family Information Tracking Methods

[1013] The server tracks the location of family members based on pre-registered family information in the event of a disaster and sends that information to the user's device, which then displays their location and helps users confirm their safety.

[1014] Program processing explanation

[1015] Data collection and analysis

[1016] The server collects disaster data from the Japan Meteorological Agency and local governments at preset intervals, then analyzes the data using AI algorithms to predict the risk level for each area.

[1017] Action instruction generation and notification

[1018] Based on the analysis results, the server generates optimal instructions for each user and immediately sends them to the device, which then notifies the user of the received instructions via pop-up notifications or audio alerts.

[1019] Data caching and communication interruption support

[1020] The server caches the latest important data on each user's device in preparation for a disaster. Even if communication is interrupted, the device will display instructions based on the cached data.

[1021] Family Tracking

[1022] Users register their family members' information in the app in advance. When a disaster occurs, the server periodically obtains the family members' location information and sends it to the device. The device then displays the family members' location information to the user, helping them confirm their safety.

[1023] Specific examples

[1024] Example 1: When an earthquake occurs

[1025] The server obtains data on the epicenter and seismic intensity from the Japan Meteorological Agency and predicts the extent of the impact through AI analysis. If the area where User A is located is affected, the server generates an action instruction such as "Please evacuate to the nearest evacuation shelter" and sends it to User A's device. The device notifies User A of this instruction via a pop-up notification and a voice alert.

[1026] Example 2: When a tsunami warning is issued

[1027] The server acquires the tsunami warning information and generates an action instruction for User B living in a coastal area, such as "Please evacuate to higher ground," based on the analysis results. The device immediately notifies User B of this action instruction via push notification.

[1028] The processing flow will be explained below.

[1029] Step 1:

[1030] The server periodically collects real-time disaster data from various data sources (Japan Meteorological Agency, local governments, geographic information systems, etc.). The server uses APIs to obtain earthquake epicenter data, tsunami warnings, flood forecasts, etc. from the Japan Meteorological Agency, and also collects evacuation center location information from local governments.

[1031] Step 2:

[1032] The server stores the collected disaster data in a database and performs a comprehensive analysis using AI algorithms. The server overlays the acquired epicenter data with a hazard map to evaluate the risk level of each area. Different analysis methods are used for each type of disaster (earthquake, tsunami, flood, etc.).

[1033] Step 3:

[1034] The server uses each user's data, including their location information, to generate optimal instructions for each user based on the analysis results. For example, if evacuation is necessary, the server generates instructions such as "Please evacuate to the nearest evacuation shelter."

[1035] Step 4:

[1036] The server immediately sends the generated action instructions to each user's device using push notifications, with different notifications sent to each user.

[1037] Step 5:

[1038] The device notifies the user of the action instructions received from the server, displaying specific instructions such as "Please evacuate" or "Please stay at home" using pop-up notifications and audio alerts.

[1039] Step 6:

[1040] When a disaster is predicted, the server caches the latest important data on each user's device, so that even if communication is interrupted, the device can provide users with action instructions based on the cached data.

[1041] Step 7:

[1042] If communication is interrupted, the device will automatically load the cached data and display instructions to the user. The device will continuously display specific instructions along with the message "Displaying evacuation instructions based on data cached by this device."

[1043] Step 8:

[1044] Users can register their family members' information (such as names, contact information, and location sharing settings) in advance on the app's settings screen, which allows the system to help confirm the safety of family members in the event of a disaster.

[1045] Step 9:

[1046] The server periodically acquires and analyzes the location information of pre-registered family members in the event of a disaster. The location information of family members is tracked in real time, and the latest data is sent to the device.

[1047] Step 10:

[1048] The device displays the location information of the family sent from the server to the user, and displays a message such as "The family is currently evacuated to △△ Elementary School" to allow the user to confirm the safety of their family.

[1049] The above is a detailed explanation of the program's processing steps. This system provides support to users so that they can act quickly and appropriately in the event of a disaster.

[1050] Example 1

[1051] 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."

[1052] Conventional disaster prevention systems have problems with insufficient collection and analysis of disaster information and difficulty in assessing the situation in real time, making it difficult to provide prompt instructions for action. Furthermore, when communications are cut off, it is difficult to provide appropriate information, making it difficult to adequately confirm the safety of family members.

[1053] 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.

[1054] In this invention, the server includes a means for acquiring location information, a means for collecting disaster data, a means for predicting impacts based on the collected disaster data and location information, a means for generating optimal action instructions for individual users based on the predicted impacts, a means for transmitting the generated action instructions to each user's device, and a means for tracking the location information of family members and confirming their safety. This enables prompt and accurate action instructions to be provided in the event of a disaster and appropriate support even when communication is interrupted. It also enables the safety of family members to be confirmed in real time.

[1055] "Means for acquiring location information" refers to the technology or device used to accurately acquire the user's current location. Specifically, this includes GPS and Wi-Fi location services.

[1056] "Means of collecting disaster data" refers to technologies and devices for collecting disaster information on earthquakes, tsunamis, floods, etc. in real time provided by the Japan Meteorological Agency and local governments.

[1057] "Means for predicting impacts" refers to technologies and devices that use AI algorithms and other methods to analyze risk levels in specific areas based on collected disaster data and location information, and predict future impacts.

[1058] "Means for generating action instructions" refers to technology or devices that create information to instruct each user on the optimal action based on the predicted impact of the disaster. Examples include evacuation advisories and standby instructions.

[1059] The "means for transmitting action instructions" refers to a technique or device for transmitting the generated action instructions to each user's terminal in real time via a communication network.

[1060] The "means for notifying an action instruction" refers to a technique or device for immediately notifying the user of an action instruction received by the terminal. Specifically, this includes pop-up notifications, audio alerts, vibrations, etc.

[1061] The "means for displaying action instructions" refers to a technique or device that displays the notified action instructions on the screen of the terminal so that the user can visually confirm them.

[1062] "Data caching means" refers to technology or devices that temporarily store the latest information on a device when a disaster occurs. This cached data allows instructions to be provided even if communication is interrupted.

[1063] The "means for displaying action instructions when communication is interrupted" refers to a technique or device for displaying appropriate action instructions to the user based on cached data even when communication is interrupted.

[1064] "Means for tracking family location information" refers to technology or devices that track the location information of pre-registered family members in the event of a disaster and ensure their safety.

[1065] "Means for confirming safety" refers to technologies and devices that use family location information to enable users to check the safety status of their family members in real time.

[1066] MODE FOR CARRYING OUT THE INVENTION

[1067] overview

[1068] This invention is a support system that enables individual users to take the most appropriate action quickly and accurately when a disaster occurs. This system comprehensively analyzes location information, disaster data, hazard maps, and local government information, and provides specific instructions to each user. It also has a function to provide instructions even when communication is cut off, and a tracking function to confirm the safety of family members.

[1069] Key Components

[1070] 1. Location information acquisition means:

[1071] The device uses GPS and Wi-Fi location services to accurately determine the user's current location. The device periodically sends location data to a server.

[1072] Example: When a user carries a smartphone, it obtains the current latitude and longitude every minute and sends that data to a server.

[1073] 2. Disaster data collection methods:

[1074] The server collects real-time disaster information on earthquakes, tsunamis, floods, and other disasters provided by the Japan Meteorological Agency and local governments.

[1075] Example: When the Japan Meteorological Agency issues a new earthquake alert, the server collects the data immediately.

[1076] 3. Data analysis methods:

[1077] The server analyzes the collected disaster data and location information using an AI algorithm, which predicts the risk level for the user's location.

[1078] Example: The server uses data on the epicenter of an earthquake to determine whether the user's current location is safe.

[1079] 4. Action instruction generation means:

[1080] The server generates specific instructions for each user based on the results of the data analysis. For example, if evacuation is necessary, the server generates instructions such as "Please evacuate to the nearest evacuation shelter."

[1081] Example: If user A is in an area at risk of a tsunami, the server generates an action instruction such as "Please evacuate to higher ground."

[1082] 5. Action instruction notification means:

[1083] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via a pop-up notification, audio alert, or vibration.

[1084] Example: User B receives a voice alert saying "Please evacuate."

[1085] 6. Data caching methods:

[1086] The device caches the latest important data received from the server in case of a communication interruption, allowing the device to provide the user with action instructions even if communication is interrupted.

[1087] Example: When communication is lost, the device displays the cached "shelter location information."

[1088] 7. Family Information Tracking Methods:

[1089] Users register their family members' information in advance in the app. When a disaster occurs, the server periodically obtains their family members' location information and sends it to the user's device. The device then displays their location and helps users confirm their safety.

[1090] Example: The server verifies the location of User C's family and notifies User C's device that "the family has safely arrived at the evacuation shelter."

[1091] The specific hardware and software used

[1092] Hardware: Smartphones, tablets, servers, GPS modules, communication network equipment

[1093] Software: Location services, real-time data collection software, AI analysis algorithms, notification systems, cache management software

[1094] Explanation of operation using concrete examples

[1095] If an earthquake occurs:

[1096] 1. The server obtains data on the epicenter and intensity of the earthquake from the Japan Meteorological Agency.

[1097] 2. The device sends the user's new location information to the server.

[1098] 3. The server uses AI analysis to predict the impact from the epicenter and determines that the area where User A is located is dangerous.

[1099] 4. The server generates an action instruction such as "Please evacuate to the nearest evacuation shelter" and sends it to User A's device.

[1100] 5. The device notifies User A with a pop-up notification and a voice alert.

[1101] If a tsunami warning is issued:

[1102] 1. The server obtains tsunami warning information.

[1103] 2. The server checks the location information of user B, who lives in a coastal area, and generates an instruction to "evacuate to higher ground."

[1104] 3. The server immediately sends this action instruction to User B's device.

[1105] 4. The device immediately notifies User B via push notification and vibration.

[1106] Example prompt statement

[1107] Examples of prompts to illustrate specific usage scenarios are:

[1108] When an earthquake occurs, User A receives instructions via his smartphone saying, "Please evacuate to the nearest evacuation shelter." Based on these instructions, he quickly begins evacuation.

[1109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1110] Step 1: Obtaining location information

[1111] The device obtains the user's current location. Specifically, it uses GPS or Wi-Fi location services. As input, a trigger occurs that causes the device to periodically obtain the user's current location. As output, the obtained latitude and longitude data is generated. The device then sends this location data to the server.

[1112] Specific operation: The device obtains the user's current latitude and longitude every minute and sends them to the server.

[1113] Step 2: Collect disaster data

[1114] The server collects disaster data in real time from the Japan Meteorological Agency and local governments. As input, new disaster information is acquired from the servers of the Japan Meteorological Agency and local governments. As output, data such as earthquake epicenters, tsunami warnings, and flood forecasts are stored on the server.

[1115] Specific operation: The server periodically calls the Japan Meteorological Agency's API to obtain the latest earthquake data and stores it in a database.

[1116] Step 3: Analyze the data

[1117] The server uses an AI algorithm to analyze the collected disaster data and received location information. The disaster data and location information are provided as input. The output is a risk level index generated as a result of the analysis. Based on this risk level, a basis for optimal action instructions for each user is created.

[1118] How it works: The server uses an AI model to analyze earthquake data and the user's location to calculate the risk level for the user's location.

[1119] Step 4: Generate action instructions

[1120] The server generates specific instructions for each user based on the data analysis results. The risk level indicator is used as input. Instructions for each user are generated as output. These instructions include, for example, "Please evacuate to the nearest evacuation shelter" or "Please stay at home."

[1121] Specific operation: Based on the risk level indicator, the server generates action instructions such as "Evacuate to higher ground" for users in areas where a tsunami warning has been issued.

[1122] Step 5: Notification of instructions to act

[1123] The server immediately sends the generated action instructions to each user's device. The device notifies the user of the received action instructions via a pop-up notification or a voice alert. The action instruction data is sent from the server as input. The notification itself is sent as output.

[1124] Specific operation: The server sends evacuation instructions to the user's device, which then issues a pop-up notification and an audio alert.

[1125] Step 6: Caching Data

[1126] The terminal caches the most recent important data received from the server in case of a communication interruption. The most recent action instruction data is cached as input. The cached data to be used when communication is interrupted is obtained as output.

[1127] Specific operation: The device locally stores the "shelter location information" received from the server and prepares to display it when communication is lost.

[1128] Step 7: Tracking Family Information

[1129] Users register their family members' information in the app in advance. When a disaster occurs, the server periodically obtains the location information of the registered family members and sends it to the user's device. The family members' location information is sent to the server as input. The family members' location data is displayed on the user's device as output.

[1130] Specific operation: The server periodically obtains the family's location data and notifies User C that the family has safely arrived at the evacuation shelter.

[1131] (Application example 1)

[1132] 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."

[1133] In the event of a disaster, it is extremely important to quickly and accurately ensure the safety of workers and machinery in logistics centers. However, conventional systems have insufficient disaster information collection and analysis, making it impossible to track location information in real time or generate and notify appropriate action instructions. Furthermore, there is a lack of a way to provide accurate action instructions even in the event of a communications outage. This can lead to reduced efficiency in logistics operations and delays in ensuring the safety of workers.

[1134] 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.

[1135] In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impacts based on the collected disaster data, means for generating optimal actions for individual users based on the predicted impacts, means for transmitting the generated action instructions to each user's terminal, means for acquiring and notifying location information of equipment and workers in the logistics center, and means for predicting the impacts on the logistics center in the event of a disaster and generating optimal action instructions. This makes it possible to quickly and accurately ensure the safety of workers and equipment in the logistics center even in the event of a disaster and prevent a decline in work efficiency.

[1136] "Location information acquisition means" refers to technologies or devices used to determine the current location of a user or device, including GPS functionality and Wi-Fi location services.

[1137] "Means of collecting disaster data" refers to systems and services for collecting disaster-related information provided by official organizations such as the Japan Meteorological Agency and local governments.

[1138] The "means for predicting impact" is a system that uses AI algorithms and analytical technology to estimate the impact of a disaster based on collected disaster data and location information.

[1139] The "means for generating optimal behavior" is an algorithm or program that determines and generates the best behavior that a user or device should take based on the predicted impact.

[1140] The "means for transmitting behavioral instructions to each user's terminal" refers to a communication technology or protocol for transmitting the generated behavioral instructions to each user's electronic device.

[1141] "Means for acquiring and notifying location information of equipment and workers within a logistics center" refers to a system for identifying the current location of machines and workers used within a logistics center and informing managers and other relevant parties.

[1142] The "means of predicting the impact on logistics centers in the event of a disaster and generating optimal action instructions" is a system that analyzes risks at logistics centers in the event of a disaster and determines safe and effective response measures based on that analysis.

[1143] overview

[1144] This invention is a system for quickly and accurately ensuring the safety of workers and equipment in a logistics center in the event of a disaster. This system comprehensively utilizes location information, disaster data, risk analysis data, etc. to provide specific action instructions to individual users. It also has the ability to provide instructions even when communication is interrupted, and the ability to track locations.

[1145] Key Components

[1146] 1. Location information acquisition means

[1147] The server periodically collects the current locations of equipment and workers within the logistics center using GPS and Wi-Fi location information services, making it possible to grasp overall location information within the logistics center.

[1148] 2. Disaster data collection methods

[1149] The server collects disaster information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, etc. This data is periodically retrieved by the server using an API.

[1150] 3. Data Analysis Methods

[1151] The server analyzes the collected disaster data and location information and uses generative AI models and algorithms to predict the impact of the disaster, thereby identifying the risk level for each piece of equipment and worker.

[1152] 4. Action instruction generation means

[1153] Based on the analysis results, the server generates optimal action instructions for each piece of equipment and worker in the logistics center, including instructions for safe evacuation routes and instructions to suspend work.

[1154] 5. Action instruction notification means

[1155] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received action instructions via a pop-up notification or a voice alert.

[1156] 6. Data Caching Methods

[1157] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[1158] Operational flow and concrete examples

[1159] Server-side processing

[1160] The server periodically obtains location information within the logistics center using the location information acquisition means. It also collects real-time disaster information from outside using the disaster data collection means. Based on this data, it predicts the impact of the disaster using the data analysis means. For example, if an earthquake occurs nearby, the server analyzes the location of the epicenter and the logistics center and predicts the impact. Next, based on the predicted impact, the action instruction generation means generates optimal action instructions for each piece of equipment and worker. These action instructions relate to safe evacuation routes within the logistics center, interruptions to work, etc.

[1161] Terminal side processing

[1162] When the device receives the action instructions sent from the server, it notifies the user of the contents. The notification is displayed as a pop-up or a sound alert. The device also uses a data caching means to cache the latest important data. Even if communication is interrupted, the device can display the action instructions based on the cached data.

[1163] Specific examples

[1164] Example 1: High flood risk

[1165] If a logistics center is located near a river, the server obtains flood information from the Japan Meteorological Agency and predicts the impact. If the risk is deemed high, the server generates an action instruction to "evacuate to higher ground" and notifies the worker's device. The device immediately notifies the worker of this instruction via a voice alert.

[1166] Example 2: Structural risk of a logistics center due to an earthquake

[1167] If a major earthquake occurs nearby, the server analyzes data on the epicenter and the location of the logistics center to predict the impact. If it determines that the impact on the entire logistics center will be significant, the server generates an action instruction stating, "We recommend you evacuate outside the building," and notifies the user's device. This allows forklifts and workers to quickly evacuate outside the building.

[1168] Prompt Sentence Examples

[1169] Please describe the detailed features of the "Disaster Response Logistics Management App." Please include the following elements:

[1170] Location information acquisition function

[1171] Disaster data collection function

[1172] Data analysis function

[1173] Action instruction generation function

[1174] Action instruction notification function

[1175] Data cache function

[1176] Worker tracking function

[1177] Specific examples include instructions for action in cases of high flood risk or earthquake risk.

[1178] As described above, by using this system, it is possible to ensure the safety of workers and equipment within the logistics center and prevent a decline in work efficiency.

[1179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1180] Step 1:

[1181] The server periodically collects the current locations of equipment and workers in the logistics center using location information acquisition means. The input is GPS data of the equipment and workers, and the output is a real-time location information list. Specifically, the server receives the GPS coordinates sent from each device and stores them in a list.

[1182] Step 2:

[1183] The server uses disaster data collection tools to collect disaster information in real time from the Japan Meteorological Agency and local governments. The input is disaster data from the public API, and the output is a disaster information database. Specifically, the weather information and disaster notifications obtained via the API are stored in the database and updated as needed.

[1184] Step 3:

[1185] The server uses data analysis tools to analyze the collected disaster data and location information. The input is a disaster information database and a location information list, and the output is impact prediction data. Specifically, a generative AI model is used to run an algorithm that predicts the impact of a disaster based on the location information of each piece of equipment and worker.

[1186] Step 4:

[1187] The server uses the action instruction generation means to generate optimal action instructions for each piece of equipment and worker in the logistics center based on the prediction results. The input is impact prediction data, and the output is specific action instructions. Specifically, action instructions (e.g., "evacuate to the nearest evacuation shelter" or "move to a safe location") are automatically generated according to the risk level.

[1188] Step 5:

[1189] The server uses the action instruction notification means to instantly send the generated action instructions to each user's device. The input is the generated action instruction, and the output is a notification to each user's device. Specifically, the action instruction is sent to each device and notified to the user as a push notification or a voice alert.

[1190] Step 6:

[1191] The terminal uses a data caching means to cache the latest data sent from the server in case of a communication interruption. The input is important data from the server, and the output is cached data. Specifically, the latest disaster information and action instructions are periodically saved locally to prepare for communication interruptions.

[1192] Step 7:

[1193] The user takes appropriate action based on the received action instructions. The input is an action instruction notification from the device, and the output is a change in the user's behavior. Specifically, the user takes specific action, such as following the evacuation instructions displayed on the device and moving to a safe place.

[1194] 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.

[1195] overview

[1196] This invention combines a system that provides optimal action instructions to individuals based on their location information when a disaster occurs with an emotion engine that recognizes the user's emotions. This allows the content and display method of action instructions to be optimized according to the user's emotional state, providing more effective support.

[1197] Key Components

[1198] 1. Location information acquisition means

[1199] The device is equipped with a function to acquire the user's current location. Using GPS or Wi-Fi location services, the device periodically sends location data to a server.

[1200] 2. Disaster data collection methods

[1201] The server collects information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, and evacuation shelter information.

[1202] 3. Data Analysis Methods

[1203] The server analyzes the collected disaster data and user location information, and uses AI algorithms to predict the impact of the disaster, thereby generating optimal action instructions for each individual user.

[1204] 4. Action instruction generation means

[1205] The server generates specific instructions for each user based on the analysis results, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[1206] 5. Action instruction notification means

[1207] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via push notification or alarm.

[1208] 6. Data Caching Methods

[1209] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[1210] 7. Family Information Tracking Methods

[1211] The server tracks the location of family members based on pre-registered family information in the event of a disaster and sends that information to the user's device, which then displays their location and helps users confirm their safety.

[1212] 8. Emotion Engine

[1213] The device is equipped with an engine for recognizing the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state.

[1214] 9. Emotional Data Analysis Methods

[1215] The server receives the user's emotional data and integrates it with the disaster data analysis results to optimize behavioral instructions based on the user's emotional state.

[1216] Program processing explanation

[1217] Data collection and analysis

[1218] The server periodically collects disaster data from the Japan Meteorological Agency and local governments and stores it in a database. The server then applies AI algorithms to assess the risk level of each area. Based on the analysis results, it generates optimal action instructions taking into account the user's location information.

[1219] Action instruction generation and notification

[1220] The server generates specific instructions for each user based on the analysis results. The instructions are then immediately sent to the user's device. The device then notifies the user of the received instructions via push notification or voice alert.

[1221] Data caching and communication interruption support

[1222] The server caches the latest important data on each user's device in preparation for a disaster. Even if communication is interrupted, the device can provide the user with instructions based on the cached data. For example, when communication is interrupted, the device displays the message "Displaying evacuation instructions based on data cached by this device."

[1223] Family Tracking

[1224] Users register their family information in advance on the app's settings screen. When a disaster occurs, the server periodically obtains and analyzes the family's location information. The latest location information is sent to the user's device, which then displays a message such as, "The family is currently evacuated to XX Elementary School."

[1225] Implementing the Emotion Engine

[1226] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state. For example, if the user is feeling anxious, it will recognize this and send emotional data to the server.

[1227] Emotional data analysis and optimization of behavioral instructions

[1228] The server receives the user's emotional data and integrates it with disaster data analysis results to optimize instructions. For example, if the user is in a panic, the server will adjust the notification display (such as font size and audio level) to provide detailed evacuation instructions along with a message to "stay calm."

[1229] Specific examples

[1230] Example 1: An earthquake occurs and the user is anxious.

[1231] The server acquires data on the epicenter and seismic intensity, and uses AI analysis to predict the extent of the impact. User A's device uses an emotion engine to send a message to the server indicating that User A is in an anxious state. Based on the analysis results and emotion data, the server generates action instructions such as "Please evacuate slowly to the nearest evacuation shelter" and sends them to User A's device. The device then notifies User A of these instructions in large, easy-to-read letters.

[1232] Example 2: A tsunami warning is issued and the user is in a panic

[1233] The server receives the tsunami warning information and generates an action instruction for User B, who lives in a coastal area, saying, "Please evacuate to higher ground." Meanwhile, the device notifies the server that User B is in a panic. The server takes into account the emotion data and generates a message such as, "Take a deep breath and stay calm. Move slowly to safe higher ground," and sends it to User B's device.

[1234] The processing flow will be explained below.

[1235] Step 1:

[1236] The server periodically collects disaster data from various data sources (Japan Meteorological Agency, local governments, etc.). Specifically, the server uses APIs to obtain the latest data such as epicenter information, tsunami warnings, and flood forecasts from the Japan Meteorological Agency, and also collects location information of evacuation centers from local governments.

[1237] Step 2:

[1238] The server stores the collected disaster data in a database, updates the database, and manages all information appropriately.

[1239] Step 3:

[1240] The server analyzes the collected disaster data using AI algorithms, which allows the server to assess the risk level of each area and generate predictions.

[1241] Step 4:

[1242] The server obtains the user's location information from the device and generates optimal instructions for each user based on that location information. The server takes into account the analysis results and creates specific instructions such as "evacuate to the nearest evacuation shelter" or "stay at home."

[1243] Step 5:

[1244] The server immediately sends the generated action instructions to each user's device. The server uses push notifications to send localized action instructions to each device.

[1245] Step 6:

[1246] The device notifies the user of the action instructions received from the server, displaying instructions such as "Please evacuate" or "Please stay at home" using pop-up notifications and audio alerts.

[1247] Step 7:

[1248] When a disaster is predicted, the server caches the latest important data on each user's device, allowing the device to provide action instructions based on the cached data even if communication is interrupted.

[1249] Step 8:

[1250] If communication is interrupted, the device will automatically load cached data and display instructions to the user. The device will continuously display specific instructions along with the message "Displaying evacuation instructions based on data cached by this device."

[1251] Step 9:

[1252] Users enter their family members' information in advance through the app's settings screen, including their names, contact details, and location sharing settings.

[1253] Step 10:

[1254] When a disaster occurs, the server periodically obtains the location information of pre-registered family members, tracks their location in real time, and collects and analyzes the latest data.

[1255] Step 11:

[1256] The server sends updated family location information to each user's device. The server periodically sends family location information to each user's device so that the user can check the information.

[1257] Step 12:

[1258] The device displays the location information of the family sent from the server to the user, and displays a message such as "The family is currently evacuated to △△ Elementary School" to allow the user to confirm the safety of their family.

[1259] Step 13:

[1260] The device uses an emotion engine to recognize the user's emotions by analyzing the user's facial expressions, voice, and other biometric signals. The emotion engine determines the user's emotional state (e.g., anxiety, fear, panic, etc.).

[1261] Step 14:

[1262] The device sends the recognized emotion data to the server, which receives the emotion data and integrates it with the disaster data analysis results.

[1263] Step 15:

[1264] The server optimizes the analysis results and action instructions based on the emotional data. For example, if the user is in a panic state, it will add a message such as "Please stay calm."

[1265] Step 16:

[1266] The server sends optimized behavioral instructions to each user's device, which then displays appropriate instructions according to the user's emotional state, helping the user to act calmly.

[1267] The above is a detailed description of the processing steps of the disaster prevention system that combines the emotion engine. This system provides appropriate action instructions that take into account the user's emotional state, thereby more effectively ensuring the user's safety in the event of a disaster.

[1268] Example 2

[1269] 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."

[1270] Conventional disaster response systems have been effective to a certain extent in providing action instructions based on the user's location information and disaster data, but because they do not take the user's emotional state into consideration, there are issues with the effectiveness of the action instructions being insufficient in situations where users are prone to stress or panic. There is also the problem that it is difficult to provide appropriate action instructions to users when communication is cut off.

[1271] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impacts based on the collected disaster data, means for generating optimal actions for individual users based on the predicted impacts, means for transmitting the generated action instructions to each user's terminal, means for analyzing the emotional state of the user, and means for optimizing the action instructions taking the emotional state into consideration. This makes it possible to provide optimal action instructions according to the emotional state, reduce stress and panic in users when a disaster occurs, and provide appropriate action instructions even when communication is interrupted.

[1272] "Location information acquisition means" refers to a function for acquiring the user's current location, and collects location data using technologies such as GPS and Wi-Fi.

[1273] "Disaster data collection means" refers to the function for collecting disaster data from the Japan Meteorological Agency and local governments in real time, including information on earthquakes, tsunamis, floods, etc.

[1274] "Impact prediction means" refers to a function that uses an AI algorithm to predict impact based on collected disaster data, and analyzes the user's location information and disaster data to assess the risk level.

[1275] The "action instruction generation means" refers to a function that generates optimal action instructions for each user based on the impact prediction, and specifically includes guidance on evacuation sites and instructions to stay at home.

[1276] The "action instruction sending means" refers to a function for sending the generated action instructions to each user's terminal, and aims to quickly deliver the action instructions to the user.

[1277] "Emotional state analysis means" refers to a function for analyzing the user's emotional state, which determines emotions by analyzing the user's facial expressions, voice, and biological signals.

[1278] The "action instruction optimization means" refers to a function that takes into account the user's emotional state and appropriately adjusts action instructions accordingly, changing the content and display format of notifications based on emotional data.

[1279] "Push notification" refers to a method in which a server instantly sends information to a device to notify the user, and is a means of quickly delivering important information and instructions for action.

[1280] "Data caching means" refers to a function that stores the latest important data on the terminal in preparation for communication interruptions, enabling appropriate action instructions to be provided even in the event of a disaster.

[1281] overview

[1282] This invention combines a system that provides optimal action instructions based on the user's location information when a disaster occurs with an emotion engine that recognizes the user's emotional state. This enables the content and display method of action instructions to be optimized according to the user's emotional state, making it possible to provide more effective support.

[1283] System Program Overview

[1284] The system includes a location information acquisition means, a disaster data collection means, an impact prediction means, an action instruction generation means, an action instruction transmission means, an emotional state analysis means, and an action instruction optimization means.

[1285] 1. Location information acquisition means

[1286] The device uses GPS and Wi-Fi location services to obtain the user's current location, and periodically sends the obtained location data to a server.

[1287] 2. Disaster data collection methods

[1288] The server collects disaster data on earthquakes, tsunamis, floods, etc. in real time from the Japan Meteorological Agency and local governments, and stores this data in a database.

[1289] 3. Impact prediction methods

[1290] The server applies AI algorithms based on the collected disaster data and user location information to assess risk levels and predict the impact of disasters, generating optimal action instructions for each user.

[1291] 4. Action instruction generation means

[1292] Based on the predicted impact, the server generates specific instructions for each user, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[1293] 5. Means of sending instructions for action

[1294] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions using push notifications or voice alerts.

[1295] 6. Emotional state analysis method

[1296] The device is equipped with an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state. The determined emotion data is then sent to a server.

[1297] 7. Action Instruction Optimization Method

[1298] The server receives the user's emotional data and integrates it with disaster data analysis results to optimize instructions. For example, if the user is in a panic, it will provide detailed evacuation instructions along with a message such as "Please stay calm." The notification display method (font size, audio level, etc.) is also adjusted according to the emotional data.

[1299] Specific examples

[1300] Example 1: An earthquake occurs and the user is anxious.

[1301] The server collects data on the epicenter and seismic intensity, and uses AI analysis to predict the extent of the impact.

[1302] The terminal transmits to the server, via the emotion engine, that User A is in an anxious state.

[1303] Based on the analysis results and emotion data, the server generates action instructions such as "Please evacuate slowly to the nearest evacuation shelter" and sends them to User A's device.

[1304] The terminal notifies User A of this instruction in large, easy-to-read letters.

[1305] Example 2: A tsunami warning is issued and the user is in a panic

[1306] The server receives tsunami warning information and generates an action instruction for User B who lives in a coastal area, such as "Evacuate to higher ground."

[1307] The device detects that User B is in a panic state using its emotion engine and notifies the server.

[1308] The server takes into consideration the emotion data and generates an action instruction such as "Take a deep breath and calm down. Move slowly to safe, higher ground," and sends it to User B's terminal.

[1309] The terminal displays the received instruction together with a voice message, providing User B with a sense of security.

[1310] Prompt Sentence Examples

[1311] Below are some example prompts to input to a generative AI model:

[1312] "Earthquake information has been obtained. The epicenter is in Tokyo, with a seismic intensity of 6+. The user is currently in Ota Ward and is feeling uneasy. Please generate the most appropriate instructions for action."

[1313]

[1314] "A tsunami warning has been issued. The user lives in Yokohama and is currently in a state of panic. Please take a deep breath and generate a calming message and evacuation instructions."

[1315] This will realize a system that can provide users with prompt and effective support in the event of a disaster and provide optimal action instructions according to their emotional state.

[1316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1317] Step 1:

[1318] Location information collection

[1319] Input: User's current location (GPS data and / or Wi-Fi location).

[1320] Specific operation: The device periodically collects the user's current location. Specifically, it obtains location information using GPS and Wi-Fi.

[1321] Output: Send the acquired location data to the server.

[1322] Step 2:

[1323] Disaster data collection

[1324] Input: Disaster information (earthquakes, tsunamis, floods, etc.) from the Japan Meteorological Agency and local governments.

[1325] Specific operation: The server collects real-time disaster data through APIs from the Japan Meteorological Agency and local governments.

[1326] Output: Collected disaster data is stored in a database.

[1327] Step 3:

[1328] Data analysis

[1329] Input: Location information from step 1 and disaster data from step 2.

[1330] How it works: The server uses AI algorithms to analyze location information and disaster data, and then evaluates the risk level for each user's current location.

[1331] Output: Generate risk assessment results according to each user's location.

[1332] Step 4:

[1333] Collecting Emotional Data

[1334] Input: User's facial, voice, and biometric data.

[1335] Specific operation: The device uses an emotion engine to analyze the user's facial expressions, voice, and biometric signals to determine their current emotional state.

[1336] Output: The determined emotion data is sent to the server.

[1337] Step 5:

[1338] Generate action instructions

[1339] Input: Risk assessment results from step 3 and sentiment data from step 4.

[1340] Specific operation: The server integrates the risk assessment results and emotional data, and generates optimal instructions for action using an AI algorithm. Specifically, it creates messages and instructions for action that are tailored to the user's emotional state.

[1341] Output: Generate optimized action instructions for each user.

[1342] Step 6:

[1343] Sending action instructions and notifications

[1344] Input: Action instructions generated in step 5.

[1345] Specific operation: The server immediately sends the generated action instructions to the user's device, and the device notifies the user of the received action instructions via push notification or voice alert.

[1346] Output: Instructions for action sent to the user's device.

[1347] Step 7:

[1348] Data Cache

[1349] Input: Latest critical data (disaster data from Step 2 and action instructions from Step 5).

[1350] Specific operation: The device caches the latest important data in preparation for communication interruptions, making it possible to respond even in the event of a disaster.

[1351] Output: Cached sensitive data.

[1352] Step 8:

[1353] Use of cached data

[1354] Input: State when communication is interrupted.

[1355] Specific operation: If communication is interrupted, the device will use cached data to provide instructions to the user, specifically displaying the message "Providing evacuation instructions based on data cached by this device."

[1356] Output: Action instructions provided even in the event of communication blackout.

[1357] Step 9:

[1358] Family Tracking

[1359] Input: Family information registered by the user.

[1360] Specific operation: Users register their family members' information in advance on the app's settings screen. When a disaster occurs, the server periodically obtains the family members' location information and sends it to the user's device.

[1361] Output: Location information and updates of family members.

[1362] Step 10:

[1363] Family location notifications

[1364] Input: Family location information from step 9.

[1365] Specific operation: The device notifies the user of the latest location information, saying, "Your family has evacuated to △△ Elementary School."

[1366] Output: Family location information displayed on the user's device.

[1367] (Application example 2)

[1368] 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."

[1369] Conventional disaster response systems can generate action instructions based on a user's location information and disaster information, but they cannot take the user's emotional state into consideration. As a result, if a user is in a state of panic or anxiety, appropriate action instructions cannot be provided quickly, making it difficult to evacuate efficiently and ensure safety. Furthermore, if the information notification method is not appropriate for the user's emotional state, there is also the problem that instructions cannot be effectively conveyed. The present invention aims to solve these problems and support rapid and appropriate evacuation by providing optimal action instructions based on the user's emotional state during a disaster.

[1370] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impact based on the collected disaster data, means for analyzing the emotional state using an emotion engine that recognizes the user's emotions, means for generating optimal actions for each user based on the predicted impact and emotional state, and means for transmitting the generated action instructions to each user's terminal. As a result, optimal action instructions are generated taking into account the user's location information and emotional state when a disaster occurs, enabling quick and appropriate evacuation and safety assurance.

[1371] "Means for acquiring location information" refers to a function that collects the user's current location using technologies such as GPS or Wi-Fi and transmits it to a server.

[1372] "Means for collecting disaster data" refers to the function of acquiring disaster-related information provided by the Japan Meteorological Agency and local governments in real time and storing it in a database.

[1373] "Means for predicting impact based on collected disaster data" refers to a function that analyzes collected disaster data and uses an AI algorithm to predict the impact of a disaster on specific regions or users.

[1374] The "means for analyzing the emotional state by utilizing an emotion engine that recognizes the user's emotions" is an engine that analyzes the user's facial expressions, voice, and other biometric signals to determine the emotional state at that time.

[1375] The "means for generating optimal behavior for each individual user" is a function that generates specific and appropriate behavioral instructions for each user based on the predicted impact results and emotional state.

[1376] The "means for transmitting generated action instructions to each user's terminal" is a function for instantly transmitting action instructions generated by the server to the user's terminal such as a smartphone or smart glasses.

[1377] The "means for notifying the user of the transmitted action instruction" is a function for notifying the user of the received action instruction through a push notification or a voice alert.

[1378] The "means for displaying the notified action instruction to the user" is a function for displaying the received action instruction on the terminal screen of the user.

[1379] The "means for optimizing the display method of action instructions according to the emotional state of the user" is a function for adjusting the display method of action instructions (character size, audio level, etc.) based on the emotional state of the user.

[1380] "Means for caching the latest data on the terminal when a disaster occurs" is a function that temporarily stores important data on the terminal when a disaster occurs.

[1381] The "means for displaying action instructions based on cached data when communication is interrupted" is a function that provides action instructions to the user using cached data when communication is interrupted.

[1382] MODE FOR CARRYING OUT THE INVENTION

[1383] overview

[1384] This invention combines a system that provides optimal action instructions to individuals based on their location information when a disaster occurs with an emotion engine that recognizes the user's emotions. This system optimizes the content and display method of action instructions according to the user's emotional state, making it possible to provide more effective support.

[1385] Main Components and Processing

[1386] The main components required for this system are as follows:

[1387] Location information acquisition means

[1388] The server collects location information from users' devices, such as smartphones and smart glasses, using GPS and Wi-Fi, and periodically transmits this location information to the server.

[1389] Disaster data collection methods

[1390] The server collects disaster information from the Japan Meteorological Agency and local governments, including earthquake, tsunami, flood, and evacuation shelter information. The collected data is stored on the server.

[1391] Impact prediction methods

[1392] The server analyzes the collected disaster data using AI algorithms (such as TensorFlow and PyTorch) to predict the impact of the disaster on specific areas and users.

[1393] emotion recognition means

[1394] The device is equipped with an emotion engine (such as Affectiva SDK or Microsoft Azure Face API) that recognizes the user's emotions. It uses the camera and microphone to analyze the user's facial expressions and voice to determine their current emotional state.

[1395] Action instruction generation means

[1396] The server generates specific and appropriate action instructions for each user based on the impact prediction results and the user's emotional state, including evacuation route guidance.

[1397] Action instruction notification means

[1398] The server immediately sends the generated action instructions to the user's device, which then notifies the user of the instructions via push notification or audio alert.

[1399] Data caching method

[1400] The device temporarily stores (caches) the latest data sent from the server, and can provide action instructions based on the cached data even if communication is interrupted.

[1401] Notification optimization method based on emotional state

[1402] The server adjusts the display method of action instructions (such as font size and audio level) according to the user's emotions. For users in a panic state, it adjusts visual and auditory elements to convey calming messages.

[1403] Specific examples

[1404] Example 1: An earthquake occurs and the user is anxious.

[1405] The server obtains data on the epicenter and seismic intensity, and predicts the extent of the impact using AI analysis.

[1406] The emotion recognition engine on User A's device determines that he is in an anxious state.

[1407] Based on the analysis results and emotion data, the server generates an action instruction such as "Please evacuate slowly to the nearest evacuation shelter" and sends this to User A's device.

[1408] The device will notify User A of this instruction in large letters and via a push notification.

[1409] Prompt Sentence Examples

[1410] Regarding apps that provide instructions for users to take action in the event of a disaster:

[1411] The system acquires the user's location information and combines it with disaster data to generate optimal instructions for action.

[1412] An emotion recognition engine is used to optimize the content of instructions and notification methods according to the user's emotional state.

[1413] Example 1: If an earthquake occurs and the user is anxious, instruct them to "evacuate slowly to the nearest evacuation shelter."

[1414] Example 2: A tsunami warning is issued and the user is in a panic

[1415] The server receives tsunami warning information and generates evacuation instructions to User B who lives in a coastal area to move to higher ground.

[1416] The device's emotion recognition engine determines that User B is in a panic.

[1417] The server generates a message saying, "Take a deep breath and calm down. Move slowly to safe, higher ground," and sends it to User B's terminal.

[1418] The device will display this message with an audio alert and a visual notification.

[1419] Prompt Sentence Examples

[1420] Regarding the Security Assistant app:

[1421] When a disaster occurs, optimal action instructions are generated based on the user's location information and emotional state.

[1422] Example 2: If a tsunami warning is issued and the user is panicking, audio and visual notifications will be provided to instruct them to "take a deep breath and stay calm. Move slowly to higher ground."

[1423] In this way, the present invention integrates the user's emotional state and location information when a disaster occurs, and provides optimal action instructions, thereby helping the user to respond quickly and accurately.

[1424] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1425] Step 1:

[1426] The server collects disaster data in real time from the Japan Meteorological Agency and local governments. At this time, information on earthquakes, tsunamis, floods, evacuation shelters, etc. is stored in a database and analyzed using AI models (e.g., TensorFlow, PyTorch).

[1427] Input: Disaster data from the Japan Meteorological Agency and local governments

[1428] Output: Storing disaster data in a database

[1429] Step 2:

[1430] The device acquires the user's location information via GPS or Wi-Fi and periodically sends it to the server, thereby updating the user's current location in real time.

[1431] Input: GPS or Wi-Fi location

[1432] Output: Send user location to server

[1433] Step 3:

[1434] The server uses AI algorithms to predict the impact of disasters based on collected disaster data and user location information, and this analysis assesses the risk level of a specific area.

[1435] Input: Disaster data, user location information

[1436] Output: Risk assessment results by region

[1437] Step 4:

[1438] The device's emotion recognition engine collects emotional data from the user's facial expressions and voice via the camera and microphone, analyzes their current emotional state, and sends this data to a server.

[1439] Input: User's facial expression and voice data

[1440] Output: Emotional state sent to the server

[1441] Step 5:

[1442] The server integrates the impact prediction results with the emotional data and uses an AI algorithm to generate optimal behavioral instructions for each individual user. These behavioral instructions are specific and include responses based on the user's emotional state.

[1443] Input: Regional risk assessment results, emotional state data

[1444] Output: Optimal action instructions

[1445] Step 6:

[1446] The server immediately sends the generated action instructions to each user's device, which then notifies the user of the instructions via push notification or voice alert.

[1447] Input: Optimal action instructions

[1448] Output: Notification of action instructions to the user device

[1449] Step 7:

[1450] The device caches the latest important data, so even if communication is interrupted, instructions can be displayed based on the cached data.

[1451] Input: Latest critical data from server

[1452] Output: Caching data

[1453] Step 8:

[1454] The device displays the received action instructions to the user, and the display method (character size, audio level, etc.) of the action instructions is optimized according to the user's emotional state.

[1455] Input: Action instructions, emotional state data

[1456] Output: Display of optimized action instructions

[1457] Step 9:

[1458] The user begins evacuation according to the instructions. The user's device continues to collect emotional state and location information and transmits it to the server.

[1459] Input: Evacuation behavior

[1460] Output: Continuous emotional state data and location information sent to a server

[1461] 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.

[1462] 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.

[1463] 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.

[1464] [Fourth embodiment]

[1465] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1466] 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.

[1467] 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).

[1468] 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.

[1469] 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.

[1470] 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).

[1471] 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.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] 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.

[1476] 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.

[1477] 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."

[1478] overview

[1479] This invention is a system that enables individual users to take the most appropriate action quickly and accurately in the event of a disaster. This system comprehensively analyzes location information, disaster data, hazard maps, local government information, etc., and provides specific instructions to each user. It also has a function to provide instructions even when communication is cut off, and a tracking function to confirm the safety of family members.

[1480] Key Components

[1481] 1. Location information acquisition means

[1482] The device is equipped with a function to acquire the user's current location. Using GPS or Wi-Fi location services, the device periodically sends location data to a server.

[1483] 2. Disaster data collection methods

[1484] The server collects information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, and evacuation shelter information.

[1485] 3. Data Analysis Methods

[1486] The server analyzes the collected disaster data and user location information, and uses AI algorithms to predict the impact of the disaster, thereby generating optimal action instructions for each individual user.

[1487] 4. Action instruction generation means

[1488] The server generates specific instructions for each user based on the analysis results, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[1489] 5. Action instruction notification means

[1490] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via push notification or alarm.

[1491] 6. Data Caching Methods

[1492] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[1493] 7. Family Information Tracking Methods

[1494] The server tracks the location of family members based on pre-registered family information in the event of a disaster and sends that information to the user's device, which then displays their location and helps users confirm their safety.

[1495] Program processing explanation

[1496] Data collection and analysis

[1497] The server collects disaster data from the Japan Meteorological Agency and local governments at preset intervals, then analyzes the data using AI algorithms to predict the risk level for each area.

[1498] Action instruction generation and notification

[1499] Based on the analysis results, the server generates optimal instructions for each user and immediately sends them to the device, which then notifies the user of the received instructions via pop-up notifications or audio alerts.

[1500] Data caching and communication interruption support

[1501] The server caches the latest important data on each user's device in preparation for a disaster. Even if communication is interrupted, the device will display instructions based on the cached data.

[1502] Family Tracking

[1503] Users register their family members' information in the app in advance. When a disaster occurs, the server periodically obtains the family members' location information and sends it to the device. The device then displays the family members' location information to the user, helping them confirm their safety.

[1504] Specific examples

[1505] Example 1: When an earthquake occurs

[1506] The server obtains data on the epicenter and seismic intensity from the Japan Meteorological Agency and predicts the extent of the impact through AI analysis. If the area where User A is located is affected, the server generates an action instruction such as "Please evacuate to the nearest evacuation shelter" and sends it to User A's device. The device notifies User A of this instruction via a pop-up notification and a voice alert.

[1507] Example 2: When a tsunami warning is issued

[1508] The server acquires the tsunami warning information and generates an action instruction for User B living in a coastal area, such as "Please evacuate to higher ground," based on the analysis results. The device immediately notifies User B of this action instruction via push notification.

[1509] The processing flow will be explained below.

[1510] Step 1:

[1511] The server periodically collects real-time disaster data from various data sources (Japan Meteorological Agency, local governments, geographic information systems, etc.). The server uses APIs to obtain earthquake epicenter data, tsunami warnings, flood forecasts, etc. from the Japan Meteorological Agency, and also collects evacuation center location information from local governments.

[1512] Step 2:

[1513] The server stores the collected disaster data in a database and performs a comprehensive analysis using AI algorithms. The server overlays the acquired epicenter data with a hazard map to evaluate the risk level of each area. Different analysis methods are used for each type of disaster (earthquake, tsunami, flood, etc.).

[1514] Step 3:

[1515] The server uses each user's data, including their location information, to generate optimal instructions for each user based on the analysis results. For example, if evacuation is necessary, the server generates instructions such as "Please evacuate to the nearest evacuation shelter."

[1516] Step 4:

[1517] The server immediately sends the generated action instructions to each user's device using push notifications, with different notifications sent to each user.

[1518] Step 5:

[1519] The device notifies the user of the action instructions received from the server, displaying specific instructions such as "Please evacuate" or "Please stay at home" using pop-up notifications and audio alerts.

[1520] Step 6:

[1521] When a disaster is predicted, the server caches the latest important data on each user's device, so that even if communication is interrupted, the device can provide users with action instructions based on the cached data.

[1522] Step 7:

[1523] If communication is interrupted, the device will automatically load the cached data and display instructions to the user. The device will continuously display specific instructions along with the message "Displaying evacuation instructions based on data cached by this device."

[1524] Step 8:

[1525] Users can register their family members' information (such as names, contact information, and location sharing settings) in advance on the app's settings screen, which allows the system to help confirm the safety of family members in the event of a disaster.

[1526] Step 9:

[1527] The server periodically acquires and analyzes the location information of pre-registered family members in the event of a disaster. The location information of family members is tracked in real time, and the latest data is sent to the device.

[1528] Step 10:

[1529] The device displays the location information of the family sent from the server to the user, and displays a message such as "The family is currently evacuated to △△ Elementary School" to allow the user to confirm the safety of their family.

[1530] The above is a detailed explanation of the program's processing steps. This system provides support to users so that they can act quickly and appropriately in the event of a disaster.

[1531] Example 1

[1532] 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."

[1533] Conventional disaster prevention systems have problems with insufficient collection and analysis of disaster information and difficulty in assessing the situation in real time, making it difficult to provide prompt instructions for action. Furthermore, when communications are cut off, it is difficult to provide appropriate information, making it difficult to adequately confirm the safety of family members.

[1534] 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.

[1535] In this invention, the server includes a means for acquiring location information, a means for collecting disaster data, a means for predicting impacts based on the collected disaster data and location information, a means for generating optimal action instructions for individual users based on the predicted impacts, a means for transmitting the generated action instructions to each user's device, and a means for tracking the location information of family members and confirming their safety. This enables prompt and accurate action instructions to be provided in the event of a disaster and appropriate support even when communication is interrupted. It also enables the safety of family members to be confirmed in real time.

[1536] "Means for acquiring location information" refers to the technology or device used to accurately acquire the user's current location. Specifically, this includes GPS and Wi-Fi location services.

[1537] "Means of collecting disaster data" refers to technologies and devices for collecting disaster information on earthquakes, tsunamis, floods, etc. in real time provided by the Japan Meteorological Agency and local governments.

[1538] "Means for predicting impacts" refers to technologies and devices that use AI algorithms and other methods to analyze risk levels in specific areas based on collected disaster data and location information, and predict future impacts.

[1539] "Means for generating action instructions" refers to technology or devices that create information to instruct each user on the optimal action based on the predicted impact of the disaster. Examples include evacuation advisories and standby instructions.

[1540] The "means for transmitting action instructions" refers to a technique or device for transmitting the generated action instructions to each user's terminal in real time via a communication network.

[1541] The "means for notifying an action instruction" refers to a technique or device for immediately notifying the user of an action instruction received by the terminal. Specifically, this includes pop-up notifications, audio alerts, vibrations, etc.

[1542] The "means for displaying action instructions" refers to a technique or device that displays the notified action instructions on the screen of the terminal so that the user can visually confirm them.

[1543] "Data caching means" refers to technology or devices that temporarily store the latest information on a device when a disaster occurs. This cached data allows instructions to be provided even if communication is interrupted.

[1544] The "means for displaying action instructions when communication is interrupted" refers to a technique or device for displaying appropriate action instructions to the user based on cached data even when communication is interrupted.

[1545] "Means for tracking family location information" refers to technology or devices that track the location information of pre-registered family members in the event of a disaster and ensure their safety.

[1546] "Means for confirming safety" refers to technologies and devices that use family location information to enable users to check the safety status of their family members in real time.

[1547] MODE FOR CARRYING OUT THE INVENTION

[1548] overview

[1549] This invention is a support system that enables individual users to take the most appropriate action quickly and accurately when a disaster occurs. This system comprehensively analyzes location information, disaster data, hazard maps, and local government information, and provides specific instructions to each user. It also has a function to provide instructions even when communication is cut off, and a tracking function to confirm the safety of family members.

[1550] Key Components

[1551] 1. Location information acquisition means:

[1552] The device uses GPS and Wi-Fi location services to accurately determine the user's current location. The device periodically sends location data to a server.

[1553] Example: When a user carries a smartphone, it obtains the current latitude and longitude every minute and sends that data to a server.

[1554] 2. Disaster data collection methods:

[1555] The server collects real-time disaster information on earthquakes, tsunamis, floods, and other disasters provided by the Japan Meteorological Agency and local governments.

[1556] Example: When the Japan Meteorological Agency issues a new earthquake alert, the server collects the data immediately.

[1557] 3. Data analysis methods:

[1558] The server analyzes the collected disaster data and location information using an AI algorithm, which predicts the risk level for the user's location.

[1559] Example: The server uses data on the epicenter of an earthquake to determine whether the user's current location is safe.

[1560] 4. Action instruction generation means:

[1561] The server generates specific instructions for each user based on the results of the data analysis. For example, if evacuation is necessary, the server generates instructions such as "Please evacuate to the nearest evacuation shelter."

[1562] Example: If user A is in an area at risk of a tsunami, the server generates an action instruction such as "Please evacuate to higher ground."

[1563] 5. Action instruction notification means:

[1564] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via a pop-up notification, audio alert, or vibration.

[1565] Example: User B receives a voice alert saying "Please evacuate."

[1566] 6. Data caching methods:

[1567] The device caches the latest important data received from the server in case of a communication interruption, allowing the device to provide the user with action instructions even if communication is interrupted.

[1568] Example: When communication is lost, the device displays the cached "shelter location information."

[1569] 7. Family Information Tracking Methods:

[1570] Users register their family members' information in advance in the app. When a disaster occurs, the server periodically obtains their family members' location information and sends it to the user's device. The device then displays their location and helps users confirm their safety.

[1571] Example: The server verifies the location of User C's family and notifies User C's device that "the family has safely arrived at the evacuation shelter."

[1572] The specific hardware and software used

[1573] Hardware: Smartphones, tablets, servers, GPS modules, communication network equipment

[1574] Software: Location services, real-time data collection software, AI analysis algorithms, notification systems, cache management software

[1575] Explanation of operation using concrete examples

[1576] If an earthquake occurs:

[1577] 1. The server obtains data on the epicenter and intensity of the earthquake from the Japan Meteorological Agency.

[1578] 2. The device sends the user's new location information to the server.

[1579] 3. The server uses AI analysis to predict the impact from the epicenter and determines that the area where User A is located is dangerous.

[1580] 4. The server generates an action instruction such as "Please evacuate to the nearest evacuation shelter" and sends it to User A's device.

[1581] 5. The device notifies User A with a pop-up notification and a voice alert.

[1582] If a tsunami warning is issued:

[1583] 1. The server obtains tsunami warning information.

[1584] 2. The server checks the location information of user B, who lives in a coastal area, and generates an instruction to "evacuate to higher ground."

[1585] 3. The server immediately sends this action instruction to User B's device.

[1586] 4. The device immediately notifies User B via push notification and vibration.

[1587] Example prompt statement

[1588] Examples of prompts to illustrate specific usage scenarios are:

[1589] When an earthquake occurs, User A receives instructions via his smartphone saying, "Please evacuate to the nearest evacuation shelter." Based on these instructions, he quickly begins evacuation.

[1590] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1591] Step 1: Obtaining location information

[1592] The device obtains the user's current location. Specifically, it uses GPS or Wi-Fi location services. As input, a trigger occurs that causes the device to periodically obtain the user's current location. As output, the obtained latitude and longitude data is generated. The device then sends this location data to the server.

[1593] Specific operation: The device obtains the user's current latitude and longitude every minute and sends them to the server.

[1594] Step 2: Collect disaster data

[1595] The server collects disaster data in real time from the Japan Meteorological Agency and local governments. As input, new disaster information is acquired from the servers of the Japan Meteorological Agency and local governments. As output, data such as earthquake epicenters, tsunami warnings, and flood forecasts are stored on the server.

[1596] Specific operation: The server periodically calls the Japan Meteorological Agency's API to obtain the latest earthquake data and stores it in a database.

[1597] Step 3: Analyze the data

[1598] The server uses an AI algorithm to analyze the collected disaster data and received location information. The disaster data and location information are provided as input. The output is a risk level index generated as a result of the analysis. Based on this risk level, a basis for optimal action instructions for each user is created.

[1599] How it works: The server uses an AI model to analyze earthquake data and the user's location to calculate the risk level for the user's location.

[1600] Step 4: Generate action instructions

[1601] The server generates specific instructions for each user based on the data analysis results. The risk level indicator is used as input. Instructions for each user are generated as output. These instructions include, for example, "Please evacuate to the nearest evacuation shelter" or "Please stay at home."

[1602] Specific operation: Based on the risk level indicator, the server generates action instructions such as "Evacuate to higher ground" for users in areas where a tsunami warning has been issued.

[1603] Step 5: Notification of instructions to act

[1604] The server immediately sends the generated action instructions to each user's device. The device notifies the user of the received action instructions via a pop-up notification or a voice alert. The action instruction data is sent from the server as input. The notification itself is sent as output.

[1605] Specific operation: The server sends evacuation instructions to the user's device, which then issues a pop-up notification and an audio alert.

[1606] Step 6: Caching Data

[1607] The terminal caches the most recent important data received from the server in case of a communication interruption. The most recent action instruction data is cached as input. The cached data to be used when communication is interrupted is obtained as output.

[1608] Specific operation: The device locally stores the "shelter location information" received from the server and prepares to display it when communication is lost.

[1609] Step 7: Tracking Family Information

[1610] Users register their family members' information in the app in advance. When a disaster occurs, the server periodically obtains the location information of the registered family members and sends it to the user's device. The family members' location information is sent to the server as input. The family members' location data is displayed on the user's device as output.

[1611] Specific operation: The server periodically obtains the family's location data and notifies User C that the family has safely arrived at the evacuation shelter.

[1612] (Application example 1)

[1613] 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."

[1614] In the event of a disaster, it is extremely important to quickly and accurately ensure the safety of workers and machinery in logistics centers. However, conventional systems have insufficient disaster information collection and analysis, making it impossible to track location information in real time or generate and notify appropriate action instructions. Furthermore, there is a lack of a way to provide accurate action instructions even in the event of a communications outage. This can lead to reduced efficiency in logistics operations and delays in ensuring the safety of workers.

[1615] 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.

[1616] In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impacts based on the collected disaster data, means for generating optimal actions for individual users based on the predicted impacts, means for transmitting the generated action instructions to each user's terminal, means for acquiring and notifying location information of equipment and workers in the logistics center, and means for predicting the impacts on the logistics center in the event of a disaster and generating optimal action instructions. This makes it possible to quickly and accurately ensure the safety of workers and equipment in the logistics center even in the event of a disaster and prevent a decline in work efficiency.

[1617] "Location information acquisition means" refers to technologies or devices used to determine the current location of a user or device, including GPS functionality and Wi-Fi location services.

[1618] "Means of collecting disaster data" refers to systems and services for collecting disaster-related information provided by official organizations such as the Japan Meteorological Agency and local governments.

[1619] The "means for predicting impact" is a system that uses AI algorithms and analytical technology to estimate the impact of a disaster based on collected disaster data and location information.

[1620] The "means for generating optimal behavior" is an algorithm or program that determines and generates the best behavior that a user or device should take based on the predicted impact.

[1621] The "means for transmitting behavioral instructions to each user's terminal" refers to a communication technology or protocol for transmitting the generated behavioral instructions to each user's electronic device.

[1622] "Means for acquiring and notifying location information of equipment and workers within a logistics center" refers to a system for identifying the current location of machines and workers used within a logistics center and informing managers and other relevant parties.

[1623] The "means of predicting the impact on logistics centers in the event of a disaster and generating optimal action instructions" is a system that analyzes risks at logistics centers in the event of a disaster and determines safe and effective response measures based on that analysis.

[1624] overview

[1625] This invention is a system for quickly and accurately ensuring the safety of workers and equipment in a logistics center in the event of a disaster. This system comprehensively utilizes location information, disaster data, risk analysis data, etc. to provide specific action instructions to individual users. It also has the ability to provide instructions even when communication is interrupted, and the ability to track locations.

[1626] Key Components

[1627] 1. Location information acquisition means

[1628] The server periodically collects the current locations of equipment and workers within the logistics center using GPS and Wi-Fi location information services, making it possible to grasp overall location information within the logistics center.

[1629] 2. Disaster data collection methods

[1630] The server collects disaster information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, etc. This data is periodically retrieved by the server using an API.

[1631] 3. Data Analysis Methods

[1632] The server analyzes the collected disaster data and location information and uses generative AI models and algorithms to predict the impact of the disaster, thereby identifying the risk level for each piece of equipment and worker.

[1633] 4. Action instruction generation means

[1634] Based on the analysis results, the server generates optimal action instructions for each piece of equipment and worker in the logistics center, including instructions for safe evacuation routes and instructions to suspend work.

[1635] 5. Action instruction notification means

[1636] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received action instructions via a pop-up notification or a voice alert.

[1637] 6. Data Caching Methods

[1638] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[1639] Operational flow and concrete examples

[1640] Server-side processing

[1641] The server periodically obtains location information within the logistics center using the location information acquisition means. It also collects real-time disaster information from outside using the disaster data collection means. Based on this data, it predicts the impact of the disaster using the data analysis means. For example, if an earthquake occurs nearby, the server analyzes the location of the epicenter and the logistics center and predicts the impact. Next, based on the predicted impact, the action instruction generation means generates optimal action instructions for each piece of equipment and worker. These action instructions relate to safe evacuation routes within the logistics center, interruptions to work, etc.

[1642] Terminal side processing

[1643] When the device receives the action instructions sent from the server, it notifies the user of the contents. The notification is displayed as a pop-up or a sound alert. The device also uses a data caching means to cache the latest important data. Even if communication is interrupted, the device can display the action instructions based on the cached data.

[1644] Specific examples

[1645] Example 1: High flood risk

[1646] If a logistics center is located near a river, the server obtains flood information from the Japan Meteorological Agency and predicts the impact. If the risk is deemed high, the server generates an action instruction to "evacuate to higher ground" and notifies the worker's device. The device immediately notifies the worker of this instruction via a voice alert.

[1647] Example 2: Structural risk of a logistics center due to an earthquake

[1648] If a major earthquake occurs nearby, the server analyzes data on the epicenter and the location of the logistics center to predict the impact. If it determines that the impact on the entire logistics center will be significant, the server generates an action instruction stating, "We recommend you evacuate outside the building," and notifies the user's device. This allows forklifts and workers to quickly evacuate outside the building.

[1649] Prompt Sentence Examples

[1650] Please describe the detailed features of the "Disaster Response Logistics Management App." Please include the following elements:

[1651] Location information acquisition function

[1652] Disaster data collection function

[1653] Data analysis function

[1654] Action instruction generation function

[1655] Action instruction notification function

[1656] Data cache function

[1657] Worker tracking function

[1658] Specific examples include instructions for action in cases of high flood risk or earthquake risk.

[1659] As described above, by using this system, it is possible to ensure the safety of workers and equipment within the logistics center and prevent a decline in work efficiency.

[1660] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1661] Step 1:

[1662] The server periodically collects the current locations of equipment and workers in the logistics center using location information acquisition means. The input is GPS data of the equipment and workers, and the output is a real-time location information list. Specifically, the server receives the GPS coordinates sent from each device and stores them in a list.

[1663] Step 2:

[1664] The server uses disaster data collection tools to collect disaster information in real time from the Japan Meteorological Agency and local governments. The input is disaster data from the public API, and the output is a disaster information database. Specifically, the weather information and disaster notifications obtained via the API are stored in the database and updated as needed.

[1665] Step 3:

[1666] The server uses data analysis tools to analyze the collected disaster data and location information. The input is a disaster information database and a location information list, and the output is impact prediction data. Specifically, a generative AI model is used to run an algorithm that predicts the impact of a disaster based on the location information of each piece of equipment and worker.

[1667] Step 4:

[1668] The server uses the action instruction generation means to generate optimal action instructions for each piece of equipment and worker in the logistics center based on the prediction results. The input is impact prediction data, and the output is specific action instructions. Specifically, action instructions (e.g., "evacuate to the nearest evacuation shelter" or "move to a safe location") are automatically generated according to the risk level.

[1669] Step 5:

[1670] The server uses the action instruction notification means to instantly send the generated action instructions to each user's device. The input is the generated action instruction, and the output is a notification to each user's device. Specifically, the action instruction is sent to each device and notified to the user as a push notification or a voice alert.

[1671] Step 6:

[1672] The terminal uses a data caching means to cache the latest data sent from the server in case of a communication interruption. The input is important data from the server, and the output is cached data. Specifically, the latest disaster information and action instructions are periodically saved locally to prepare for communication interruptions.

[1673] Step 7:

[1674] The user takes appropriate action based on the received action instructions. The input is an action instruction notification from the device, and the output is a change in the user's behavior. Specifically, the user takes specific action, such as following the evacuation instructions displayed on the device and moving to a safe place.

[1675] 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.

[1676] overview

[1677] This invention combines a system that provides optimal action instructions to individuals based on their location information when a disaster occurs with an emotion engine that recognizes the user's emotions. This allows the content and display method of action instructions to be optimized according to the user's emotional state, providing more effective support.

[1678] Key Components

[1679] 1. Location information acquisition means

[1680] The device is equipped with a function to acquire the user's current location. Using GPS or Wi-Fi location services, the device periodically sends location data to a server.

[1681] 2. Disaster data collection methods

[1682] The server collects information in real time from the Japan Meteorological Agency and local governments, including information on earthquake epicenters, tsunami warnings, flood forecasts, and evacuation shelter information.

[1683] 3. Data Analysis Methods

[1684] The server analyzes the collected disaster data and user location information, and uses AI algorithms to predict the impact of the disaster, thereby generating optimal action instructions for each individual user.

[1685] 4. Action instruction generation means

[1686] The server generates specific instructions for each user based on the analysis results, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[1687] 5. Action instruction notification means

[1688] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions via push notification or alarm.

[1689] 6. Data Caching Methods

[1690] The device caches the latest data sent from the server in case communication is interrupted, so the user can receive appropriate instructions even in situations where communication is interrupted.

[1691] 7. Family Information Tracking Methods

[1692] The server tracks the location of family members based on pre-registered family information in the event of a disaster and sends that information to the user's device, which then displays their location and helps users confirm their safety.

[1693] 8. Emotion Engine

[1694] The device is equipped with an engine for recognizing the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state.

[1695] 9. Emotional Data Analysis Methods

[1696] The server receives the user's emotional data and integrates it with the disaster data analysis results to optimize behavioral instructions based on the user's emotional state.

[1697] Program processing explanation

[1698] Data collection and analysis

[1699] The server periodically collects disaster data from the Japan Meteorological Agency and local governments and stores it in a database. The server then applies AI algorithms to assess the risk level of each area. Based on the analysis results, it generates optimal action instructions taking into account the user's location information.

[1700] Action instruction generation and notification

[1701] The server generates specific instructions for each user based on the analysis results. The instructions are then immediately sent to the user's device. The device then notifies the user of the received instructions via push notification or voice alert.

[1702] Data caching and communication interruption support

[1703] The server caches the latest important data on each user's device in preparation for a disaster. Even if communication is interrupted, the device can provide the user with instructions based on the cached data. For example, when communication is interrupted, the device displays the message "Displaying evacuation instructions based on data cached by this device."

[1704] Family Tracking

[1705] Users register their family information in advance on the app's settings screen. When a disaster occurs, the server periodically obtains and analyzes the family's location information. The latest location information is sent to the user's device, which then displays a message such as, "The family is currently evacuated to XX Elementary School."

[1706] Implementing the Emotion Engine

[1707] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state. For example, if the user is feeling anxious, it will recognize this and send emotional data to the server.

[1708] Emotional data analysis and optimization of behavioral instructions

[1709] The server receives the user's emotional data and integrates it with disaster data analysis results to optimize instructions. For example, if the user is in a panic, the server will adjust the notification display (such as font size and audio level) to provide detailed evacuation instructions along with a message to "stay calm."

[1710] Specific examples

[1711] Example 1: An earthquake occurs and the user is anxious.

[1712] The server acquires data on the epicenter and seismic intensity, and uses AI analysis to predict the extent of the impact. User A's device uses an emotion engine to send a message to the server indicating that User A is in an anxious state. Based on the analysis results and emotion data, the server generates action instructions such as "Please evacuate slowly to the nearest evacuation shelter" and sends them to User A's device. The device then notifies User A of these instructions in large, easy-to-read letters.

[1713] Example 2: A tsunami warning is issued and the user is in a panic

[1714] The server receives the tsunami warning information and generates an action instruction for User B, who lives in a coastal area, saying, "Please evacuate to higher ground." Meanwhile, the device notifies the server that User B is in a panic. The server takes into account the emotion data and generates a message such as, "Take a deep breath and stay calm. Move slowly to safe higher ground," and sends it to User B's device.

[1715] The processing flow will be explained below.

[1716] Step 1:

[1717] The server periodically collects disaster data from various data sources (Japan Meteorological Agency, local governments, etc.). Specifically, the server uses APIs to obtain the latest data such as epicenter information, tsunami warnings, and flood forecasts from the Japan Meteorological Agency, and also collects location information of evacuation centers from local governments.

[1718] Step 2:

[1719] The server stores the collected disaster data in a database, updates the database, and manages all information appropriately.

[1720] Step 3:

[1721] The server analyzes the collected disaster data using AI algorithms, which allows the server to assess the risk level of each area and generate predictions.

[1722] Step 4:

[1723] The server obtains the user's location information from the device and generates optimal instructions for each user based on that location information. The server takes into account the analysis results and creates specific instructions such as "evacuate to the nearest evacuation shelter" or "stay at home."

[1724] Step 5:

[1725] The server immediately sends the generated action instructions to each user's device. The server uses push notifications to send localized action instructions to each device.

[1726] Step 6:

[1727] The device notifies the user of the action instructions received from the server, displaying instructions such as "Please evacuate" or "Please stay at home" using pop-up notifications and audio alerts.

[1728] Step 7:

[1729] When a disaster is predicted, the server caches the latest important data on each user's device, allowing the device to provide action instructions based on the cached data even if communication is interrupted.

[1730] Step 8:

[1731] If communication is interrupted, the device will automatically load cached data and display instructions to the user. The device will continuously display specific instructions along with the message "Displaying evacuation instructions based on data cached by this device."

[1732] Step 9:

[1733] Users enter their family members' information in advance through the app's settings screen, including their names, contact details, and location sharing settings.

[1734] Step 10:

[1735] When a disaster occurs, the server periodically obtains the location information of pre-registered family members, tracks their location in real time, and collects and analyzes the latest data.

[1736] Step 11:

[1737] The server sends updated family location information to each user's device. The server periodically sends family location information to each user's device so that the user can check the information.

[1738] Step 12:

[1739] The device displays the location information of the family sent from the server to the user, and displays a message such as "The family is currently evacuated to △△ Elementary School" to allow the user to confirm the safety of their family.

[1740] Step 13:

[1741] The device uses an emotion engine to recognize the user's emotions by analyzing the user's facial expressions, voice, and other biometric signals. The emotion engine determines the user's emotional state (e.g., anxiety, fear, panic, etc.).

[1742] Step 14:

[1743] The device sends the recognized emotion data to the server, which receives the emotion data and integrates it with the disaster data analysis results.

[1744] Step 15:

[1745] The server optimizes the analysis results and action instructions based on the emotional data. For example, if the user is in a panic state, it will add a message such as "Please stay calm."

[1746] Step 16:

[1747] The server sends optimized behavioral instructions to each user's device, which then displays appropriate instructions according to the user's emotional state, helping the user to act calmly.

[1748] The above is a detailed description of the processing steps of the disaster prevention system that combines the emotion engine. This system provides appropriate action instructions that take into account the user's emotional state, thereby more effectively ensuring the user's safety in the event of a disaster.

[1749] Example 2

[1750] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1751] Conventional disaster response systems have been effective to a certain extent in providing action instructions based on the user's location information and disaster data, but because they do not take the user's emotional state into consideration, there are issues with the effectiveness of the action instructions being insufficient in situations where users are prone to stress or panic. There is also the problem that it is difficult to provide appropriate action instructions to users when communication is cut off.

[1752] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impacts based on the collected disaster data, means for generating optimal actions for individual users based on the predicted impacts, means for transmitting the generated action instructions to each user's terminal, means for analyzing the emotional state of the user, and means for optimizing the action instructions taking the emotional state into consideration. This makes it possible to provide optimal action instructions according to the emotional state, reduce stress and panic in users when a disaster occurs, and provide appropriate action instructions even when communication is interrupted.

[1753] "Location information acquisition means" refers to a function for acquiring the user's current location, and collects location data using technologies such as GPS and Wi-Fi.

[1754] "Disaster data collection means" refers to the function for collecting disaster data from the Japan Meteorological Agency and local governments in real time, including information on earthquakes, tsunamis, floods, etc.

[1755] "Impact prediction means" refers to a function that uses an AI algorithm to predict impact based on collected disaster data, and analyzes the user's location information and disaster data to assess the risk level.

[1756] The "action instruction generation means" refers to a function that generates optimal action instructions for each user based on the impact prediction, and specifically includes guidance on evacuation sites and instructions to stay at home.

[1757] The "action instruction sending means" refers to a function for sending the generated action instructions to each user's terminal, and aims to quickly deliver the action instructions to the user.

[1758] "Emotional state analysis means" refers to a function for analyzing the user's emotional state, which determines emotions by analyzing the user's facial expressions, voice, and biological signals.

[1759] The "action instruction optimization means" refers to a function that takes into account the user's emotional state and appropriately adjusts action instructions accordingly, changing the content and display format of notifications based on emotional data.

[1760] "Push notification" refers to a method in which a server instantly sends information to a device to notify the user, and is a means of quickly delivering important information and instructions for action.

[1761] "Data caching means" refers to a function that stores the latest important data on the terminal in preparation for communication interruptions, enabling appropriate action instructions to be provided even in the event of a disaster.

[1762] overview

[1763] This invention combines a system that provides optimal action instructions based on the user's location information when a disaster occurs with an emotion engine that recognizes the user's emotional state. This enables the content and display method of action instructions to be optimized according to the user's emotional state, making it possible to provide more effective support.

[1764] System Program Overview

[1765] The system includes a location information acquisition means, a disaster data collection means, an impact prediction means, an action instruction generation means, an action instruction transmission means, an emotional state analysis means, and an action instruction optimization means.

[1766] 1. Location information acquisition means

[1767] The device uses GPS and Wi-Fi location services to obtain the user's current location, and periodically sends the obtained location data to a server.

[1768] 2. Disaster data collection methods

[1769] The server collects disaster data on earthquakes, tsunamis, floods, etc. in real time from the Japan Meteorological Agency and local governments, and stores this data in a database.

[1770] 3. Impact prediction methods

[1771] The server applies AI algorithms based on the collected disaster data and user location information to assess risk levels and predict the impact of disasters, generating optimal action instructions for each user.

[1772] 4. Action instruction generation means

[1773] Based on the predicted impact, the server generates specific instructions for each user, such as "evacuate to the nearest evacuation shelter" or "stay at home."

[1774] 5. Means of sending instructions for action

[1775] The server immediately sends the generated action instructions to each user's device, and the device notifies the user of the received instructions using push notifications or voice alerts.

[1776] 6. Emotional state analysis method

[1777] The device is equipped with an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions, voice, and other biometric signals to determine their current emotional state. The determined emotion data is then sent to a server.

[1778] 7. Action Instruction Optimization Method

[1779] The server receives the user's emotional data and integrates it with disaster data analysis results to optimize instructions. For example, if the user is in a panic, it will provide detailed evacuation instructions along with a message such as "Please stay calm." The notification display method (font size, audio level, etc.) is also adjusted according to the emotional data.

[1780] Specific examples

[1781] Example 1: An earthquake occurs and the user is anxious.

[1782] The server collects data on the epicenter and seismic intensity, and uses AI analysis to predict the extent of the impact.

[1783] The terminal transmits to the server, via the emotion engine, that User A is in an anxious state.

[1784] Based on the analysis results and emotion data, the server generates action instructions such as "Please evacuate slowly to the nearest evacuation shelter" and sends them to User A's device.

[1785] The terminal notifies User A of this instruction in large, easy-to-read letters.

[1786] Example 2: A tsunami warning is issued and the user is in a panic

[1787] The server receives tsunami warning information and generates an action instruction for User B who lives in a coastal area, such as "Evacuate to higher ground."

[1788] The device detects that User B is in a panic state using its emotion engine and notifies the server.

[1789] The server takes into consideration the emotion data and generates an action instruction such as "Take a deep breath and calm down. Move slowly to safe, higher ground," and sends it to User B's terminal.

[1790] The terminal displays the received instruction together with a voice message, providing User B with a sense of security.

[1791] Prompt Sentence Examples

[1792] Below are some example prompts to input to a generative AI model:

[1793] "Earthquake information has been obtained. The epicenter is in Tokyo, with a seismic intensity of 6+. The user is currently in Ota Ward and is feeling uneasy. Please generate the most appropriate instructions for action."

[1794]

[1795] "A tsunami warning has been issued. The user lives in Yokohama and is currently in a state of panic. Please take a deep breath and generate a calming message and evacuation instructions."

[1796] This will realize a system that can provide users with prompt and effective support in the event of a disaster and provide optimal action instructions according to their emotional state.

[1797] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1798] Step 1:

[1799] Location information collection

[1800] Input: User's current location (GPS data and / or Wi-Fi location).

[1801] Specific operation: The device periodically collects the user's current location. Specifically, it obtains location information using GPS and Wi-Fi.

[1802] Output: Send the acquired location data to the server.

[1803] Step 2:

[1804] Disaster data collection

[1805] Input: Disaster information (earthquakes, tsunamis, floods, etc.) from the Japan Meteorological Agency and local governments.

[1806] Specific operation: The server collects real-time disaster data through APIs from the Japan Meteorological Agency and local governments.

[1807] Output: Collected disaster data is stored in a database.

[1808] Step 3:

[1809] Data analysis

[1810] Input: Location information from step 1 and disaster data from step 2.

[1811] How it works: The server uses AI algorithms to analyze location information and disaster data, and then evaluates the risk level for each user's current location.

[1812] Output: Generate risk assessment results according to each user's location.

[1813] Step 4:

[1814] Collecting Emotional Data

[1815] Input: User's facial, voice, and biometric data.

[1816] Specific operation: The device uses an emotion engine to analyze the user's facial expressions, voice, and biometric signals to determine their current emotional state.

[1817] Output: The determined emotion data is sent to the server.

[1818] Step 5:

[1819] Generate action instructions

[1820] Input: Risk assessment results from step 3 and sentiment data from step 4.

[1821] Specific operation: The server integrates the risk assessment results and emotional data, and generates optimal instructions for action using an AI algorithm. Specifically, it creates messages and instructions for action that are tailored to the user's emotional state.

[1822] Output: Generate optimized action instructions for each user.

[1823] Step 6:

[1824] Sending action instructions and notifications

[1825] Input: Action instructions generated in step 5.

[1826] Specific operation: The server immediately sends the generated action instructions to the user's device, and the device notifies the user of the received action instructions via push notification or voice alert.

[1827] Output: Instructions for action sent to the user's device.

[1828] Step 7:

[1829] Data Cache

[1830] Input: Latest critical data (disaster data from Step 2 and action instructions from Step 5).

[1831] Specific operation: The device caches the latest important data in preparation for communication interruptions, making it possible to respond even in the event of a disaster.

[1832] Output: Cached sensitive data.

[1833] Step 8:

[1834] Use of cached data

[1835] Input: State when communication is interrupted.

[1836] Specific operation: If communication is interrupted, the device will use cached data to provide instructions to the user, specifically displaying the message "Providing evacuation instructions based on data cached by this device."

[1837] Output: Action instructions provided even in the event of communication blackout.

[1838] Step 9:

[1839] Family Tracking

[1840] Input: Family information registered by the user.

[1841] Specific operation: Users register their family members' information in advance on the app's settings screen. When a disaster occurs, the server periodically obtains the family members' location information and sends it to the user's device.

[1842] Output: Location information and updates of family members.

[1843] Step 10:

[1844] Family location notifications

[1845] Input: Family location information from step 9.

[1846] Specific operation: The device notifies the user of the latest location information, saying, "Your family has evacuated to △△ Elementary School."

[1847] Output: Family location information displayed on the user's device.

[1848] (Application example 2)

[1849] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1850] Conventional disaster response systems can generate action instructions based on a user's location information and disaster information, but they cannot take the user's emotional state into consideration. As a result, if a user is in a state of panic or anxiety, appropriate action instructions cannot be provided quickly, making it difficult to evacuate efficiently and ensure safety. Furthermore, if the information notification method is not appropriate for the user's emotional state, there is also the problem that instructions cannot be effectively conveyed. The present invention aims to solve these problems and support rapid and appropriate evacuation by providing optimal action instructions based on the user's emotional state during a disaster.

[1851] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring location information, means for collecting disaster data, means for predicting impact based on the collected disaster data, means for analyzing the emotional state using an emotion engine that recognizes the user's emotions, means for generating optimal actions for each user based on the predicted impact and emotional state, and means for transmitting the generated action instructions to each user's terminal. As a result, optimal action instructions are generated taking into account the user's location information and emotional state when a disaster occurs, enabling quick and appropriate evacuation and safety assurance.

[1852] "Means for acquiring location information" refers to a function that collects the user's current location using technologies such as GPS or Wi-Fi and transmits it to a server.

[1853] "Means for collecting disaster data" refers to the f...

Claims

1. A means for acquiring location information; a means of collecting disaster data; A means of predicting the impact based on collected disaster data, and means for generating optimal actions for individual users based on the predicted impact; means for transmitting the generated action instructions to the terminals of the respective users; A system including:

2. means for notifying a user of the transmitted action instruction; means for displaying the notified action instruction to the user; The system of claim 1 further comprising:

3. A method for caching the latest data on the device when a disaster occurs, a means for displaying action instructions based on cached data when communication is interrupted; The system of claim 1 further comprising:

4. A means of obtaining information about pre-registered family members; A means of providing regular updates on the location of family members; means for transmitting updated location information to the terminal; means for displaying the transmitted family location information to the user; The system of claim 1 further comprising:

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