system

Abstract

We provide the system. [Solution] A disaster prevention AI system that includes means for evaluating the risk of earthquake disasters in specific areas (Tokyo, Kanagawa, Chiba, and Saitama prefectures), means for providing information on evacuation shelters in those areas, and means for guiding users to appropriate evacuation routes.

Description

[Technical Field]

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

[0002] Patent Document 1 discloses a persona chatbot control method executed by at least one processor, the method comprising the steps of: receiving a user utterance; adding the user utterance to a prompt including an instruction associated with a description of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance responding to the user utterance. [Prior Art Literature] [Patent Documents]

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

[0004] When an earthquake disaster occurs, there is a demand to quickly evaluate the risk level of specific regions (Tokyo Prefecture, Kanagawa Prefecture, Chiba Prefecture, Saitama Prefecture) and guide users to appropriate shelters and evacuation routes. [Means for Solving the Problem]

[0005] The present invention provides a disaster prevention AI system including means for evaluating a risk level of a specific region against earthquake disasters, means for providing shelter information of the region, and means for guiding an appropriate evacuation route. The means for evaluating the risk level performs evaluation based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the occurrence time of the earthquake, and the elapsed time after the earthquake occurs. The means for guiding the evacuation route proposes an optimal evacuation route based on information such as current location information, shelter information, road conditions, and traffic conditions. [Brief Description of Drawings]

[0006] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] This is a sequence diagram showing the processing flow of the data processing system in Example 1 of the Form 1 when an emotion engine is combined. [Figure 18] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] This is a sequence diagram showing the processing flow of a data processing system in another embodiment. [Modes for carrying out the invention]

[0007] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0008] First, let's explain the terminology used in the following explanation.

[0009] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (TENSOR PROCESSING UNIT®).

[0010] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0011] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), magnetic tape, and the like.

[0012] In the following embodiments, a signed communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), and the like.

[0013] 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. In addition, in the present specification, when three or more matters are expressed by connecting them with "and / or", the same concept as that for "A and / or B" applies.

[0014] [First Embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. A server is mentioned as an example of the data processing device 12.

[0017] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0019] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0020] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] The disaster prevention AI system of this invention evaluates the level of risk in a specific area (Tokyo, Kanagawa, Chiba, and Saitama prefectures) when an earthquake occurs, and guides users to appropriate evacuation shelters and evacuation routes. The means for evaluating the level of risk is based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. For example, the greater the magnitude of the earthquake, the closer the epicenter, the shallower the earthquake, and the longer the time elapsed since the earthquake, the higher the level of risk is evaluated. The means for providing evacuation shelter information provides information such as the location, capacity, and facilities of evacuation shelters based on information provided by public databases and local governments. The means for guiding users to appropriate evacuation routes proposes the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0029] "Example of form 2"

[0030] The disaster prevention AI system of this invention evaluates the level of risk in a specific area (Tokyo, Kanagawa, Chiba, and Saitama prefectures) when an earthquake occurs, and guides users to appropriate evacuation shelters and evacuation routes. The means for evaluating the level of risk is based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. For example, the greater the magnitude of the earthquake, the closer the epicenter, the shallower the earthquake, and the longer the time elapsed since the earthquake, the higher the level of risk is evaluated. The means for providing evacuation shelter information provides information such as the location, capacity, and facilities of evacuation shelters based on information provided by public databases and local governments. The means for guiding users to appropriate evacuation routes proposes the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0031] The following describes the processing flow for each example of the form.

[0032] "Example of form 1"

[0033] Step 1: When an earthquake occurs, the disaster prevention AI system collects information such as the magnitude of the earthquake, its epicenter, its depth, the time it occurred, and the time elapsed since the earthquake. This information is obtained from public earthquake information services and sensor networks.

[0034] Step 2: Based on the collected information, assess the risk level of specific areas (Tokyo, Kanagawa, Chiba, and Saitama prefectures). The assessment is based on an algorithm that assigns a higher risk level to earthquakes of larger magnitude, closer proximity to the epicenter, shallower depth, and longer time elapsed since the earthquake.

[0035] Step 3: Provide information on evacuation shelters. Based on information from public databases and local governments, provide information such as the location, capacity, and facilities of evacuation shelters.

[0036] Step 4: Based on information such as current location, evacuation shelter information, road conditions, and traffic conditions, the system proposes the optimal evacuation route. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0037] (Example 1)

[0038] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0039] During earthquake disasters, it is crucial to quickly and accurately assess the risk level of specific areas and provide appropriate shelters and evacuation routes. However, conventional systems have the challenge of handling earthquake information collection, risk assessment, shelter information acquisition, and evacuation route proposals individually, making integrated and efficient responses difficult.

[0040] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0041] In this invention, the server includes means for collecting earthquake information, means for evaluating the degree of risk based on the earthquake information, means for acquiring evacuation shelter information based on the evaluated degree of risk, means for proposing the optimal evacuation route based on the evacuation shelter information and current location information, and means for providing the proposed evacuation route to the user. This enables rapid and accurate risk assessment and provision of evacuation information during an earthquake disaster.

[0042] "Means for collecting earthquake information" refers to devices or methods for acquiring data such as the magnitude of an earthquake, its epicenter, its depth, and the time of its occurrence.

[0043] "Means for assessing risk" refers to a device or method for calculating the risk of earthquake disasters in a specific area based on collected earthquake information.

[0044] "Means for acquiring evacuation shelter information" refers to a device or method for acquiring information such as the location, capacity, and facilities of evacuation shelters based on the assessed risk level.

[0045] "Means for proposing the optimal evacuation route" refers to a device or method for calculating the most suitable evacuation route for a user, taking into account traffic and road conditions, based on evacuation shelter information and current location information.

[0046] "Means to provide to the user" refers to a device or method for communicating the proposed evacuation route to the user visually or audibly.

[0047] A description of embodiments for carrying out this invention will be given.

[0048] The server utilizes an API from an earthquake information service to collect earthquake information. This API can obtain data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time. Based on this data, the server uses its own algorithm to assess the risk level of a specific area. This assessment is designed to indicate a higher risk level when the earthquake is large, the epicenter is close, and the depth is shallow.

[0049] The device obtains evacuation shelter information using the local government's open data API, based on a risk assessment provided by the server. This information includes the location, capacity, and facilities of the evacuation shelters. The device also obtains the user's current location information and sends it to the server.

[0050] The server proposes the optimal evacuation route based on the user's current location and evacuation shelter information. This proposal uses a map information service API and takes into account traffic conditions and road passability. The server sends the calculated evacuation route to the terminal, which then provides the information to the user visually or audibly.

[0051] As a concrete example, let's assume the user is in Shibuya Ward, Tokyo. In this case, the user enters their current location into the device, and the server evaluates the risk level of Shibuya Ward based on earthquake information. Next, the device retrieves information on evacuation shelters within Shibuya Ward, and the server calculates the optimal evacuation route. Finally, the user receives information on evacuation shelters and routes from the device and can begin evacuating quickly.

[0052] An example of a prompt message would be: "I am currently in Shibuya Ward, Tokyo. An earthquake has occurred. The epicenter is in Kanagawa Prefecture, the magnitude is 7.0, and the depth is 10km. Please tell me the best evacuation shelter and evacuation route." Based on this prompt message, the server assesses the level of danger and provides the user with the most appropriate evacuation information.

[0053] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0054] Step 1:

[0055] The server uses an API from an earthquake information service to collect data such as earthquake magnitude, epicenter, depth, and time of occurrence. The input is earthquake information obtained from the API, and the output is a dataset containing this information. This dataset is used for subsequent risk assessments. Specifically, the server periodically calls the API to obtain the latest earthquake information.

[0056] Step 2:

[0057] The server evaluates the risk level of specific regions based on the collected earthquake information. The input is a dataset of earthquake information generated in Step 1, and the output is a risk score for each region. For data processing, an algorithm is used to calculate the risk level, taking into account factors such as earthquake magnitude, distance from the epicenter, and earthquake depth. Specifically, the server calculates the risk score for each region and stores it in a database.

[0058] Step 3:

[0059] The device obtains evacuation shelter information using the local government's open data API, based on a risk assessment provided by the server. Inputs include a risk score and the user's current location, while output includes information such as the location, capacity, and facilities of the evacuation shelter. The data calculation involves determining the evacuation shelter closest to the user's current location. Specifically, the device obtains the user's current location and selects the most suitable evacuation shelter.

[0060] Step 4:

[0061] The server proposes the optimal evacuation route based on the user's current location and evacuation shelter information. Inputs include the user's current location, evacuation shelter information, road conditions, and traffic conditions, and the output generates the optimal evacuation route. Data processing utilizes a map information service API to perform route calculations that consider traffic conditions and road passability. Specifically, the server evaluates multiple routes and selects the most efficient one.

[0062] Step 5:

[0063] The user receives evacuation shelter information and evacuation routes from the server via their device. The input is evacuation route information sent from the server, and the output is the information provided to the user visually or audibly. Specifically, the device guides the user along the evacuation route through map display and voice guidance.

[0064] (Application Example 1)

[0065] Next, we will describe Application Example 1 of Form 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."

[0066] During earthquakes, there is a need to automate the selection of appropriate evacuation routes and the guidance of people to shelters in order to ensure a swift and safe evacuation. However, currently, real-time information gathering and the presentation of optimal evacuation routes during earthquakes are insufficient, and there is a particular lack of evacuation support using autonomous vehicles. This could lead to delays and confusion in evacuations.

[0067] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0068] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding appropriate evacuation routes, and means for selecting the optimal evacuation route in an autonomous vehicle during an earthquake and guiding passengers to an evacuation shelter. This enables rapid and safe evacuation during an earthquake.

[0069] "Specific areas" refers to areas that may be affected by earthquake disasters, specifically areas where earthquakes are predicted to occur.

[0070] "Means for evaluating the risk of earthquake disasters" refers to methods or devices for quantifying or ranking the risk of earthquake disasters based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[0071] "Means of providing evacuation shelter information" refers to methods and devices for collecting and providing information such as the location, capacity, and facilities of evacuation shelters to users.

[0072] "Means of guiding users to appropriate evacuation routes" refers to methods or devices that calculate and present the optimal evacuation route to users based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[0073] "Means for selecting the optimal evacuation route in an autonomous vehicle during an earthquake and guiding passengers to a shelter" refers to methods or devices that enable autonomous vehicles to collect information in real time during an earthquake, select the optimal evacuation route, and safely transport passengers to a shelter.

[0074] To implement this invention, a server collects data in real time using earthquake information APIs, map APIs, and traffic information APIs, and evaluates the risk level to earthquake disasters. Based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake, the server quantifies the risk level of a specific area.

[0075] The terminal receives evacuation shelter information from the server and presents the user with details such as the location, capacity, and facilities of the shelter. Furthermore, the terminal calculates the optimal evacuation route based on information such as the user's current location, evacuation shelter information, road conditions, and traffic conditions, and guides the user along that route.

[0076] Based on information from a server, the autonomous vehicle will select the optimal evacuation route during an earthquake and safely guide passengers to a shelter. This will enable rapid and safe evacuation during an earthquake.

[0077] As a concrete example, when an earthquake occurs, inputting the prompt message "An earthquake has occurred. Your current location is Shibuya Ward, Tokyo. Please tell me the best evacuation route." into the AI ​​model can provide the optimal evacuation route.

[0078] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0079] Step 1:

[0080] The server retrieves data such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake from the earthquake information API. Based on this data, it evaluates the risk of earthquake disaster in a specific area. The input is data from the earthquake information API, and the output is the risk evaluation result. The server analyzes this data and quantifies the risk level.

[0081] Step 2:

[0082] The server uses map APIs and traffic information APIs to collect information such as the location, capacity, facilities, road conditions, and traffic conditions of evacuation shelters. Input is data from the map APIs and traffic information APIs, and output is evacuation shelter information and traffic information. The server organizes this information to understand the details of the evacuation shelters.

[0083] Step 3:

[0084] The terminal receives evacuation shelter information and traffic information provided by the server and presents it to the user. The input is evacuation shelter information and traffic information from the server, and the output is the information presented to the user. The terminal displays this information on the screen so that the user can confirm it.

[0085] Step 4:

[0086] The terminal acquires current location information and calculates the optimal evacuation route based on information from the server. Inputs include current location information, evacuation shelter information, and traffic information, while output is the optimal evacuation route. The terminal uses this information to calculate the route and guide the user.

[0087] Step 5:

[0088] The autonomous vehicle safely guides passengers to the evacuation shelter based on the optimal evacuation route provided by the terminal. The input is the optimal evacuation route, and the output is the passengers' arrival at the evacuation shelter. The autonomous vehicle drives according to the route and transports the passengers to their destination.

[0089] (Example 2)

[0090] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0091] During earthquake disasters, it is crucial to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. However, conventional systems struggle to collect information in real time and propose optimal evacuation routes, hindering users from taking swift evacuation action.

[0092] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0093] In this invention, the server includes means for collecting earthquake information, means for evaluating the degree of risk based on the collected information, means for acquiring information such as the location, capacity, and facilities of evacuation shelters, means for proposing the optimal evacuation route based on the current location information, and means for providing information to the user. This makes it possible to quickly and accurately evaluate the degree of risk in a specific area during an earthquake disaster and to provide appropriate evacuation shelters and evacuation routes in real time.

[0094] "Means for collecting earthquake information" refers to functions for acquiring data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time.

[0095] A "means for assessing risk" refers to a function that calculates the risk of earthquake disasters in a specific area based on collected earthquake information.

[0096] "Means for obtaining information such as the location, capacity, and facilities of evacuation shelters" refers to a function for obtaining detailed information about evacuation shelters based on public databases and information from local governments.

[0097] "A means of proposing the optimal evacuation route based on current location information" refers to a function that takes the user's current location into consideration and calculates and proposes the optimal evacuation route based on traffic conditions and road passability.

[0098] "Means of providing information to users" refers to functions that visually or audibly communicate risk assessments, shelter information, and suggested evacuation routes to users.

[0099] This disaster prevention AI system is designed to assess the risk level of specific areas during earthquakes and guide people to appropriate evacuation shelters and routes. The server uses an API from an earthquake information service to collect earthquake information. Specifically, it obtains data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time.

[0100] The server uses a specific algorithm to assess the level of risk based on the collected earthquake information. This algorithm is designed so that the risk level increases with the magnitude of the earthquake, the proximity of the epicenter, and the shallow depth of the earthquake.

[0101] The terminal retrieves information such as the location, capacity, and facilities of evacuation shelters from public databases and local government disaster prevention information systems. This allows users to view detailed information about evacuation shelters.

[0102] The server uses the Google® Maps API to calculate the optimal evacuation route based on the user's current location. It considers traffic conditions and road passability to propose the safest and fastest route.

[0103] Users receive risk assessments, shelter information, and suggested evacuation routes through their devices. The devices display information using a visually easy-to-understand interface and provide voice guidance to navigate the routes.

[0104] As a concrete example, when a user enters "I am in Shibuya Ward, Tokyo," the server immediately collects earthquake information and assesses the level of risk. Next, the device retrieves information on evacuation shelters within Shibuya Ward, and the server uses the Google Maps API to calculate the optimal evacuation route. Finally, the user confirms the evacuation route on the device screen and begins evacuating according to the voice guidance.

[0105] Examples of prompts to input into a generative AI model:

[0106] "I am currently in Shibuya Ward, Tokyo. Please assess the risk level in the event of an earthquake and tell me the best evacuation shelters and routes."

[0107] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0108] Step 1:

[0109] The server calls the API of an earthquake information service to obtain data such as earthquake magnitude, epicenter, depth, and time of occurrence. The input is earthquake information from the API, and the output is a dataset containing this information. The server uses this dataset to prepare for the risk assessment in the next step.

[0110] Step 2:

[0111] The server evaluates the risk level based on the earthquake information obtained in Step 1. The input is a dataset of earthquake information, and the output is a risk score for a specific region. The server applies an algorithm that increases the risk level as the earthquake magnitude increases, the epicenter is closer, and the earthquake depth decreases.

[0112] Step 3:

[0113] The terminal accesses public databases and local government disaster prevention information systems to obtain information such as the location, capacity, and facilities of evacuation shelters. The input is the user's current location, and the output is information about evacuation shelters in that area. The terminal displays this information in a format that is easy for the user to understand.

[0114] Step 4:

[0115] The server uses the Google Maps API to calculate the optimal evacuation route based on the user's current location. The input is the user's current location and evacuation shelter information, and the output is the optimal evacuation route. The server considers traffic conditions and road passability to propose the safest and fastest route.

[0116] Step 5:

[0117] The user receives risk assessments, shelter information, and suggested evacuation routes through the terminal. Input is information from the server, and output is visual and audio guidance to the user. The terminal displays evacuation routes on a map and provides voice guidance to navigate the route.

[0118] (Application Example 2)

[0119] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0120] During earthquake disasters, there is a need to quickly and accurately assess the level of risk in specific areas and guide users to the most suitable evacuation shelters and routes in real time. However, conventional systems have shortcomings in suggesting evacuation routes that do not take into account the user's current location, and real-time risk assessment is difficult.

[0121] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0122] In this invention, the server includes means for evaluating the degree of disaster risk in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, means for acquiring the user's location information, and means for evaluating the degree of risk in real time and guiding users to the optimal evacuation shelter and evacuation route. This enables quick and accurate guidance to evacuation shelters and evacuation routes based on the user's current location.

[0123] "Specific areas" refers to the geographical area that is subject to risk assessment and evacuation guidance during a disaster.

[0124] "Means for assessing the degree of risk to disaster" refers to a device or program that has the function of calculating the degree of disaster risk in a specific area based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the passage of time.

[0125] "Means of providing evacuation facility information" refers to a device or program for providing users with information such as the location, capacity, and facilities of evacuation shelters.

[0126] "Means of guiding users to appropriate evacuation routes" refers to a device or program that has the function of suggesting the optimal evacuation route, taking into account the user's current location information, road conditions, and traffic conditions.

[0127] "Means for obtaining user location information" refers to a device or program that uses technologies such as GPS to obtain the user's current geographical location.

[0128] "A means of assessing the level of risk in real time and guiding users to the optimal shelter and evacuation route" refers to a device or program that has the function of immediately assessing the level of risk in the event of a disaster and providing users with the optimal shelter and evacuation route.

[0129] To implement this invention, a server, a user terminal, and an associated database are required. The server acquires earthquake information and assesses the degree of disaster risk in a specific area. This uses data such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed. Based on this data, the server calculates the degree of risk relevant to the user's current location in real time.

[0130] The user's device obtains its current location using GPS functionality. The device receives evacuation facility information from the server and guides the user to the most suitable evacuation shelter and evacuation route. Road conditions and traffic conditions are also taken into consideration when guiding the evacuation route.

[0131] Specifically, the server utilizes an external earthquake data API to obtain earthquake information. The user's device obtains location information using a GPS module and sends it to the server. The server combines the received location information with earthquake data to assess the level of risk and calculate the optimal evacuation shelter and route. This enables the user to take quick and accurate evacuation action.

[0132] For example, if a user is in Tokyo, the server will immediately acquire earthquake information when an earthquake occurs and guide the user to the optimal evacuation shelter and route based on their current location. It is also possible to provide guidance to the user in natural language using a generative AI model.

[0133] An example of a prompt message could be: "An earthquake has occurred in Tokyo. Your current location is 35.6895, 139.6917. Please tell me the best evacuation shelter and evacuation route." By using this prompt message, the AI ​​model can provide the user with appropriate evacuation information.

[0134] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0135] Step 1:

[0136] The server obtains the latest earthquake information from an external earthquake data API. It receives earthquake data from the API as input and extracts information such as earthquake magnitude, epicenter, depth, and time of occurrence. Based on this data, it generates basic data for assessing the risk level in specific regions.

[0137] Step 2:

[0138] The user's device obtains current location information using its GPS function. It receives location information from the GPS module as input and sends latitude and longitude data to the server. This location information serves as basic data for guiding the user to the most suitable evacuation shelter and evacuation route.

[0139] Step 3:

[0140] The server combines user location information and earthquake data to assess the risk level in a specific area in real time. It receives user location information and earthquake data as input and applies an algorithm to calculate the risk level. As output, it generates a risk level related to the user's current location.

[0141] Step 4:

[0142] The server searches the evacuation facility information database for the most suitable evacuation shelter for the user's current location. It receives the user's location and risk level as input, and selects the optimal shelter considering information such as location, capacity, and facilities. The server then generates information about the optimal shelter as output.

[0143] Step 5:

[0144] The server calculates the optimal evacuation route for the user, taking into account road and traffic conditions. It receives the user's location information, evacuation shelter information, and road condition data as input, and applies a route calculation algorithm. As output, it generates the optimal evacuation route to guide the user.

[0145] Step 6:

[0146] The user's device displays evacuation shelter information and evacuation routes received from the server. It receives evacuation shelter and route information from the server as input and displays it to the user in a visually easy-to-understand format. This enables users to take swift and safe evacuation actions.

[0147] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0148] "Example of form 1"

[0149] One embodiment of the present invention provides a disaster prevention AI system that incorporates an emotion engine. This system assesses the risk of earthquake disasters, provides appropriate shelter information, and guides users along appropriate evacuation routes. Furthermore, it recognizes the user's emotions and provides appropriate evacuation information according to those emotions. Specifically, it recognizes emotions from the user's voice, facial expressions, and actions, and provides appropriate evacuation information according to those emotions. For example, if the user is panicking, it provides evacuation information accompanied by a message to calm them down. Also, if the user is confused, it provides more specific and concise evacuation information. In addition, it adjusts the method of guiding users along evacuation routes according to the user's emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them along the safest and fastest evacuation route.

[0150] "Example of form 2"

[0151] One embodiment of the present invention provides a disaster prevention AI system that incorporates an emotion engine. This system assesses the risk of earthquake disasters, provides appropriate shelter information, and guides users along appropriate evacuation routes. Furthermore, it recognizes the user's emotions and provides appropriate evacuation information according to those emotions. Specifically, it recognizes emotions from the user's voice, facial expressions, and actions, and provides appropriate evacuation information according to those emotions. For example, if the user is panicking, it provides evacuation information accompanied by a message to calm them down. Also, if the user is confused, it provides more specific and concise evacuation information. In addition, it adjusts the method of guiding users along evacuation routes according to the user's emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them along the safest and fastest evacuation route.

[0152] The following describes the processing flow for each example of the form.

[0153] "Example of form 1"

[0154] Step 1: When an earthquake occurs, the disaster prevention AI system determines the magnitude and epicenter of the earthquake.

[0155] Step 2: The disaster prevention AI system assesses the risk level for each region in Tokyo, Kanagawa, Chiba, and Saitama prefectures based on the magnitude and epicenter of the earthquake.

[0156] Step 3: The disaster prevention AI system provides appropriate evacuation shelter information and evacuation routes for each area based on the assessed risk level.

[0157] Step 4: The emotion engine recognizes emotions from the user's voice, facial expressions, and actions.

[0158] Step 5: The emotion engine provides appropriate evacuation information based on the recognized emotion. For example, if the user is panicking, it provides evacuation information accompanied by a calming message. If the user is confused, it provides more specific and concise evacuation information.

[0159] Step 6: The emotion engine adjusts how it guides users through evacuation routes according to their emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them through the safest and quickest evacuation route.

[0160] (Example 1)

[0161] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0162] When a natural disaster occurs, it is necessary to quickly and accurately assess the level of danger and guide people to appropriate evacuation facilities and routes. However, conventional systems have the problem of not adequately providing evacuation information that takes into account the emotional state of users, and are unable to provide appropriate support to users who are in a panic state.

[0163] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0164] In this invention, the server includes means for evaluating the degree of risk to natural disasters in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, means for recognizing the emotional state of the user, and means for providing evacuation information according to the emotional state. This makes it possible to provide appropriate evacuation information according to the emotional state of the user.

[0165] "Specific areas" refers to areas that are subject to risk assessment and evacuation information provision in the event of a natural disaster.

[0166] "Natural disaster" refers to a disaster caused by natural phenomena such as earthquakes and typhoons.

[0167] "Means for assessing risk" refers to methods and devices for assessing the risk level of a specific area based on information such as the scale, location, depth, time of occurrence, and time elapsed since the occurrence of a natural disaster.

[0168] "Means of providing evacuation facility information" refers to methods and devices for collecting and providing information such as the location, capacity, and facilities of evacuation facilities to users.

[0169] "Means of guiding evacuation routes" refers to methods and devices that propose the optimal evacuation route based on information such as current location, evacuation facility information, road conditions, and traffic conditions.

[0170] "Means for recognizing the emotional state of a user" refers to methods or devices for recognizing emotions from a user's voice, facial expressions, behavior, etc.

[0171] "Means of providing evacuation information according to emotional state" refers to methods or devices for providing appropriate evacuation information according to the recognized emotional state of the user.

[0172] In one embodiment of this invention, the server provides a system that assesses the risk level of a specific area when a natural disaster occurs and guides users to appropriate evacuation facilities and routes. The server uses seismometer data and weather information APIs to collect information such as the magnitude of the natural disaster, its location, depth, time of occurrence, and the time elapsed since the disaster. This allows for real-time assessment of the risk level.

[0173] The server utilizes local government databases and open data to collect information such as the location, capacity, and facilities of evacuation centers, and provides this information to users. Furthermore, it uses the Google Maps API and traffic information API to suggest the optimal evacuation route based on current location information, evacuation center information, road conditions, and traffic conditions.

[0174] The device uses voice recognition software and facial recognition technology with a camera to recognize emotions from the user's voice, facial expressions, and actions. This makes it possible to provide appropriate evacuation information according to the user's emotional state.

[0175] As a specific example, if the user is using a smartphone, the device will automatically acquire earthquake information when an earthquake occurs and display the level of danger on the screen. Next, it will display the nearest evacuation facility and its route on a map and begin voice guidance. If the device detects that the user is in a state of panic, it will voice a message such as, "Please stay calm. The nearest evacuation facility is XX." If the device detects that the user is calm, it will present multiple evacuation routes and prompt the user to choose, "Which route would you like to take?"

[0176] An example of a prompt to input into a generating AI model would be, "Please suggest evacuation facilities and routes in the event of a magnitude 7 earthquake in Tokyo." Based on this prompt, the AI ​​model will generate appropriate evacuation information.

[0177] The flow of the specific processing in Example 1 will be explained using Figure 15.

[0178] Step 1:

[0179] The server uses seismometer data and weather information APIs to collect information such as the magnitude, location, depth, time of occurrence, and time elapsed since the occurrence of a natural disaster. It receives seismometer data and information from APIs as input, analyzes this data, and generates basic data for risk assessment. Specifically, the server periodically calls APIs to obtain the latest earthquake information.

[0180] Step 2:

[0181] The server evaluates the risk level of a specific area based on the collected earthquake information. Using the basic data generated in Step 1 as input, it applies a risk assessment algorithm to output a risk score. Specifically, the server calculates the risk score considering factors such as the magnitude of the earthquake and the distance to the epicenter.

[0182] Step 3:

[0183] The server collects information such as the location, capacity, and facilities of evacuation facilities by utilizing local government databases and open data. It receives evacuation facility data provided by local governments as input, analyzes it, and outputs evacuation facility information for users. Specifically, the server periodically updates the database to maintain the latest evacuation facility information.

[0184] Step 4:

[0185] The server proposes the optimal evacuation route based on current location information, evacuation facility information, road conditions, and traffic conditions. Using the user's current location information and the evacuation facility information obtained in step 3 as input, it calculates the optimal route using the Google Maps API and traffic information API, and provides the evacuation route as output. Specifically, the server obtains the user's current location and calculates the route to the nearest evacuation facility.

[0186] Step 5:

[0187] The device recognizes emotions from the user's voice, facial expressions, and actions. It uses audio and video data acquired from the device's microphone and camera as input, and applies an emotion recognition algorithm to output the user's emotional state. Specifically, the device analyzes the user's emotions in real time using voice recognition software and facial recognition technology.

[0188] Step 6:

[0189] The device provides appropriate evacuation information based on the recognized user's emotional state. Using the emotional state obtained in step 5 as input, it generates and outputs messages and evacuation information tailored to that emotion. Specifically, the device sends calming messages to panicked users and presents multiple options to calm users.

[0190] (Application Example 1)

[0191] Next, we will describe Application Example 1 of Form 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."

[0192] During earthquake disasters, it is essential to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. Furthermore, providing appropriate evacuation information tailored to the emotional state of users is crucial to preventing panic and confusion and supporting safe evacuation. In addition, autonomous vehicles are required to select and guide passengers along the optimal evacuation route based on their emotions.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0194] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding appropriate evacuation routes, means for recognizing the user's emotions and providing evacuation information corresponding to those emotions, and means for selecting and guiding passengers on evacuation routes based on their emotions in an autonomous vehicle. This enables rapid and appropriate evacuation support during earthquake disasters and ensures the safety of users.

[0195] "Specific areas" refers to areas that are subject to risk assessment and evacuation information provision during earthquake disasters, and specifically includes areas such as Tokyo, Kanagawa, Chiba, and Saitama prefectures.

[0196] "Means for assessing the risk of earthquake disasters" refers to methods and devices for assessing the risk level of a specific area based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[0197] "Means of providing evacuation shelter information" refers to methods or devices for providing information such as the location, capacity, and facilities of evacuation shelters in a specific area.

[0198] "Means of guiding appropriate evacuation routes" refers to methods and devices that propose and guide users to the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[0199] "Means of recognizing users' emotions and providing evacuation information in accordance with those emotions" refers to methods or devices for recognizing users' emotions from their voice, facial expressions, and actions, and providing appropriate evacuation information in accordance with those emotions.

[0200] "Means for selecting and guiding passengers on evacuation routes based on their emotions in autonomous vehicles" refers to methods or devices for recognizing passengers' emotions within an autonomous vehicle and selecting and guiding them on the optimal evacuation route based on those emotions.

[0201] The system for carrying out this invention includes a server, a terminal, and an autonomous vehicle. The server runs a program to assess the risk of earthquake disasters in a specific area, provide information on evacuation shelters, and guide appropriate evacuation routes. The server collects information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of the earthquake, and the time elapsed since the earthquake, and assesses the risk based on this data.

[0202] The device senses the user's voice, facial expressions, and actions, and executes a program to recognize emotions. Emotion recognition uses speech recognition software and image analysis software. Specifically, it uses a "Speech Recognition API" for speech recognition and an "Image Analysis API" for image analysis.

[0203] The autonomous vehicle runs a program to select and guide passengers along the optimal evacuation route based on their emotions. It uses in-vehicle cameras and microphones to analyze passengers' emotions in real time and provide evacuation information tailored to those emotions. A "map API" is used for route selection, and a "generative AI model" is used for generating emotion-based messages.

[0204] As a concrete example, during an earthquake, cameras inside the autonomous vehicle capture passengers' facial expressions and microphones collect their voices. This data is analyzed using an "image analysis API" and a "speech recognition API," and if it is determined that the passengers are in a state of panic, a "generative AI model" generates a message such as, "Please stay calm. We will guide you to the safest evacuation route."

[0205] An example of a prompt message is, "The passenger is in a panic. Generate a calming message."

[0206] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[0207] Step 1:

[0208] The server collects information such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake. This information is obtained from earthquake observation agencies and meteorological databases. The input is earthquake-related data, and the output is basic data for risk assessment. The server analyzes this data to assess the risk level of a specific area.

[0209] Step 2:

[0210] The device uses a camera and microphone to collect data in order to sense the user's voice, facial expressions, and actions. The input is the user's voice and image data, and the output is the emotion recognition result. The device uses a "Voice Recognition API" and an "Image Analysis API" to analyze this data and recognize the user's emotions.

[0211] Step 3:

[0212] The server selects appropriate evacuation shelter information and evacuation routes based on the risk assessment results and the user's sentiment recognition results. The inputs are the risk assessment results and sentiment recognition results, and the outputs are evacuation shelter information and evacuation route information. The server uses the "Map API" to calculate the optimal evacuation route.

[0213] Step 4:

[0214] The autonomous vehicle guides passengers along evacuation routes based on evacuation route information received from a server. The input is evacuation route information, and the output is guidance messages for passengers. The vehicle uses a "generative AI model" to generate messages that respond to the passengers' emotions and provides guidance via voice or display.

[0215] Step 5:

[0216] The user begins a safe evacuation based on evacuation information provided by the vehicle. The input is guidance messages from the vehicle, and the output is the user's evacuation actions. The user selects the optimal evacuation route and carries out the evacuation according to the provided information.

[0217] (Example 2)

[0218] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0219] In the event of a natural disaster, it is essential to quickly and accurately assess the level of risk in specific areas and provide appropriate evacuation facilities and routes. Furthermore, there is a lack of information provision tailored to the emotional state of users, necessitating measures to prevent panic and confusion.

[0220] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0221] In this invention, the server includes means for evaluating the risk of natural disasters in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, and means for recognizing the user's emotions and providing evacuation information that corresponds to those emotions. This enables rapid and accurate risk assessment in the event of a natural disaster and the provision of appropriate evacuation information that corresponds to the user's emotions.

[0222] "Specific areas" refers to areas that may be affected by natural disasters, and specifically includes administrative divisions and geographical boundaries.

[0223] "Natural disasters" refer to disasters caused by natural phenomena such as earthquakes, typhoons, and floods.

[0224] "Means for assessing risk" refers to methods and devices that use information about natural disasters to indicate the degree of their impact using numerical values ​​or indicators.

[0225] "Means of providing information on evacuation facilities" refers to methods and devices for providing users with information such as the location, capacity, and equipment status of evacuation shelters and facilities.

[0226] "Means of guiding evacuation routes" refers to methods and devices that present the optimal route for users to safely reach evacuation facilities.

[0227] "Means of recognizing emotions" refers to methods and devices for determining a user's emotional state from their voice, facial expressions, behavior, etc.

[0228] "Means of providing evacuation information in accordance with emotions" refers to methods and devices for providing appropriate evacuation information and messages according to the emotional state of the user.

[0229] This invention is a system aimed at rapid and accurate risk assessment during natural disasters and providing appropriate evacuation information tailored to the user's emotions. Specific embodiments of this system are described below.

[0230] The server collects information on natural disasters and assesses the risk level of specific areas. This involves using APIs from the Japan Meteorological Agency and earthquake research institutions to obtain data such as earthquake magnitude, location, depth, time of occurrence, and time elapsed since the event. Using this data, the server employs machine learning models based on historical disaster data to quantify the risk level.

[0231] The terminal receives risk information transmitted from the server and notifies the user. The terminal also uses GPS to obtain the user's current location and calculates the optimal evacuation route in real time based on evacuation facility information provided by the server. This enables the user to evacuate safely and quickly.

[0232] Furthermore, the device detects the user's voice, facial expressions, and actions through its camera and microphone, and recognizes their emotions. If it determines that the user is in a state of panic, the device displays a message such as, "Please stay calm. We will guide you to a safe evacuation route." If the user is calm, it presents multiple evacuation routes and prompts them to choose one.

[0233] As a concrete example, by inputting the prompt message "Please tell me how to assess the risk level and provide evacuation information during an earthquake" into an AI model during an earthquake, the AI ​​model will generate appropriate response methods for earthquake disasters. This prompt message allows the system to quickly present countermeasures and ensure the safety of users.

[0234] The flow of the specific processing in Example 2 will be explained using Figure 17.

[0235] Step 1:

[0236] The server collects information about natural disasters. As input, it obtains data such as earthquake magnitude, location, depth, time of occurrence, and time elapsed since the earthquake from APIs of the Japan Meteorological Agency and earthquake research institutions. Based on this data, the server processes the data to assess the level of risk and converts it into a format suitable for input into the assessment model. As output, it generates data ready for input into the assessment model.

[0237] Step 2:

[0238] The server uses the collected data and a machine learning model based on past disaster data to assess the risk level of a specific area. The data generated in Step 1 is used as input. The server performs data calculations using the machine learning model to quantify the risk level for each area. The server generates the risk assessment results for each area as output and sends them to the terminal.

[0239] Step 3:

[0240] The terminal receives the risk assessment results sent from the server and notifies the user. It receives the risk assessment results from the server as input. The terminal displays the assessment results on the screen and also provides audio notification. It notifies the user of the risk level as output.

[0241] Step 4:

[0242] The device uses GPS functionality to obtain the user's current location. The device's location services are used as input. The device obtains the current location information and sends it to the server. The current location information is provided to the server as output.

[0243] Step 5:

[0244] The server calculates the optimal evacuation route based on the user's current location and information on evacuation facilities. The inputs used are the user's current location from the terminal and pre-collected information on evacuation facilities. The server also considers road and traffic conditions, performing data calculations to determine the optimal route. The output is the generated information on the optimal evacuation route, which is then sent to the terminal.

[0245] Step 6:

[0246] The terminal receives evacuation route information transmitted from the server and guides the user. It receives evacuation route information from the server as input. The terminal presents the user with specific evacuation routes through screen displays and voice guidance. It provides evacuation route guidance to the user as output.

[0247] Step 7:

[0248] The device senses the user's voice, facial expressions, and actions through its camera and microphone, and recognizes their emotions. It uses data from its sensors as input. The device performs data processing using an emotion recognition algorithm to determine the user's emotional state. As output, it evaluates the user's emotional state and prepares to provide appropriate evacuation information.

[0249] Step 8:

[0250] The device provides appropriate evacuation information based on the user's emotional state. The emotional state assessed in step 7 is used as input. The device displays calming messages to panicked users and presents multiple evacuation routes to calm users. The output provides evacuation information tailored to the user's emotional state.

[0251] (Application Example 2)

[0252] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0253] During earthquake disasters, it is essential to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. However, conventional systems have a challenge in providing evacuation information that takes into account the emotional state of users, making it difficult to provide appropriate information to users in a panicked state.

[0254] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0255] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding users to appropriate evacuation routes, and means for recognizing the user's emotions and providing evacuation information corresponding to those emotions. This makes it possible to provide appropriate evacuation information according to the user's emotional state.

[0256] "Specific areas" refer to areas that are subject to risk assessment and evacuation information provision during earthquake disasters.

[0257] "Methods for assessing the risk of earthquake disasters" are methods for assessing the risk to a specific area based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[0258] "Means of providing information on evacuation shelters" refers to means of providing information such as the location, capacity, and facilities of evacuation shelters in a specific area.

[0259] "Means of guiding people to appropriate evacuation routes" refers to methods for suggesting the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[0260] "Means of recognizing users' emotions and providing evacuation information in accordance with those emotions" refers to means of recognizing users' emotions from their voice, facial expressions, and actions, and providing appropriate evacuation information in accordance with those emotions.

[0261] The system for carrying out this invention includes a server and a terminal. The server runs a program to assess the risk level of a specific area during an earthquake disaster and provide information on evacuation shelters and appropriate evacuation routes. Specifically, the server obtains earthquake information using the Japan Meteorological Agency API and analyzes data such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. This allows the server to assess the risk level of a specific area.

[0262] Furthermore, the server retrieves evacuation shelter information from local government databases and calculates the optimal evacuation route using the Google Maps API. In addition, the terminal acquires the user's voice and facial expressions using the smartphone's microphone and camera, and analyzes them with OpenAI's emotion recognition model. This allows the system to recognize the user's emotional state and provide evacuation information tailored to that emotion.

[0263] As a concrete example, when an earthquake occurs, the server sends information to the terminal such as, "An earthquake with a seismic intensity of 6 has occurred. The nearest evacuation center is XX Park. Please evacuate using this route." If the user is in a panic state, the terminal displays a message such as, "Please stay calm. We will guide you to a safe evacuation center."

[0264] An example of a prompt message for a generative AI model is, "Recognize the user's emotions from their voice and facial expressions, and generate appropriate evacuation information."

[0265] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[0266] Step 1:

[0267] The server retrieves earthquake information from the Japan Meteorological Agency API. It uses the timestamp of the earthquake as input. The output includes data such as earthquake magnitude, epicenter, depth, and time of occurrence. This data is then processed to assess the risk level for specific regions.

[0268] Step 2:

[0269] The server retrieves evacuation shelter information from local government databases. It uses a specific regional identifier as input. The output includes information such as the location, capacity, and facilities of the evacuation shelters. Based on this information, the server selects the appropriate evacuation shelter.

[0270] Step 3:

[0271] The server uses the Google Maps API to calculate the optimal evacuation route. It uses the user's current location and evacuation shelter information as input. The output is the optimal evacuation route. Based on this route information, the server processes the data to guide the user.

[0272] Step 4:

[0273] The device uses the smartphone's microphone and camera to capture the user's voice and facial expressions. Real-time audio and video data of the user is used as input. Audio and facial expression data are obtained as output. This data is then processed for emotion recognition.

[0274] Step 5:

[0275] The device analyzes the user's emotions using OpenAI's emotion recognition model. It uses voice and facial expression data as input. The output is the user's emotional state. Based on this emotional information, it performs data calculations to generate appropriate evacuation information.

[0276] Step 6:

[0277] The server generates evacuation information based on the user's emotional state and sends it to the terminal. It uses emotional information and evacuation route information as input. The output is evacuation information to be displayed to the user. Specifically, if the user is in a panic state, it generates a message such as, "Please stay calm. We will guide you to a safe evacuation center."

[0278] (Other examples)

[0279] Next, another embodiment will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart device 14 is referred to as a "terminal".

[0280] When an earthquake occurs, it is required to quickly and accurately assess the risk level and propose an optimal evacuation route. However, in conventional systems, collection of earthquake information and proposal of evacuation routes are often performed manually, which has problems of being time-consuming and having low accuracy. This may lead to delayed evacuation and selection of incorrect evacuation routes, which may threaten user safety.

[0281] The specifying processing performed by the specifying processing unit 290 of the data processing device 12 in another embodiment is implemented by the following means.

[0282] In the present invention, the server includes: means for using a sensor and a database for collecting earthquake information; means for assessing a risk level using a generative AI model based on the earthquake information; and means for generating a prompt for instructing acquisition of shelter information based on the assessed risk level. This makes it possible to quickly and accurately assess the risk level and provide an optimal evacuation route to the user.

[0283] "Earthquake information" is a general term for data related to earthquakes, such as earthquake magnitude, epicenter, earthquake depth, earthquake occurrence time, and elapsed time after the earthquake occurs.

[0284] A "generative AI model" is a model trained to perform a specific task using artificial intelligence technology, and performs prediction and classification based on input data.

[0285] A "prompt" is a text input for giving a specific instruction to a generative AI model, and serves as a guide for the model to generate an appropriate output.

[0286] "Risk level" is an indicator that shows the degree of impact caused by earthquakes, and is evaluated based on factors such as the magnitude of the earthquake and the distance from the epicenter.

[0287] "Evacuation shelter information" is a general term for detailed data about evacuation shelters, including their location, capacity, and facilities.

[0288] An "evacuation route" is a recommended route for users to safely move from their current location to an evacuation shelter, and is suggested taking into account road conditions and traffic conditions.

[0289] This invention is a system that quickly and accurately assesses the level of risk during an earthquake and provides the user with the optimal evacuation route. Specific embodiments of this system are described below.

[0290] The server accesses earthquake sensors and external earthquake databases (e.g., USGS and the Japan Meteorological Agency) to collect earthquake information. This allows it to obtain data such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake.

[0291] The server uses a generative AI model (e.g., a model using TENSORFLOW® or PyTorch) to assess the risk level based on the collected earthquake information. During this process, it generates prompt statements for assessing the risk level. Example prompt statement: "Assess the risk level of the area within 50km of the epicenter."

[0292] The server generates a prompt message to retrieve shelter information based on the assessed risk level. Example prompt message: "List the shelters closest to the high-risk area." Using this prompt message, a query is sent to the shelter database to retrieve the necessary shelter information.

[0293] The server generates a prompt message to suggest the optimal evacuation route based on the acquired evacuation shelter information and the user's current location. Example prompt message: "Calculate the shortest route from your current location to the safest evacuation shelter." This prompt message is input into the generating AI model to calculate the optimal evacuation route.

[0294] The server sends the optimal evacuation route, derived from the generated AI model, to the user's device. The device then displays this information using a map application (e.g., Google Maps or Apple Maps) to visually guide the user.

[0295] Users can check the evacuation route displayed on their device and begin evacuation actions as needed. By following the instructions on their device, users can safely proceed to the evacuation shelter.

[0296] In this way, the present invention enables rapid and accurate evacuation support during earthquakes.

[0297] The flow of a specific process in another embodiment will be explained using Figure 19.

[0298] Step 1:

[0299] The server acquires earthquake information from earthquake sensors and external earthquake databases (e.g., USGS and Japan Meteorological Agency). It receives data such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake as input. This data is collected and stored as basic information for the next processing step.

[0300] Step 2:

[0301] The server uses a generative AI model to assess the risk level based on the collected earthquake information. The earthquake information obtained in step 1 is used as input. The server generates a prompt statement for assessing the risk level. Example of a prompt statement: "Assess the risk level of the area within 50km of the epicenter." This prompt statement is input to the generative AI model, and the risk assessment result is obtained as output.

[0302] Step 3:

[0303] The server generates a prompt sentence for acquiring shelter information based on the risk level evaluated in Step 2. The risk level evaluation result is used as an input. An example of the prompt sentence is: "List the closest shelters for regions with high risk". Using this prompt sentence, a query is sent to the shelter database, and shelter information is obtained as an output.

[0304] Step 4:

[0305] The server generates a prompt sentence for proposing an optimal evacuation route based on the acquired shelter information and the user's current location information. The shelter information and the user's current location information are used as inputs. An example of the prompt sentence is: "Calculate the shortest route from the current location to the safest shelter". This prompt sentence is input to a generative AI model, and the optimal evacuation route is obtained as an output.

[0306] Step 5:

[0307] The server transmits the optimal evacuation route obtained in Step 4 to the user's terminal. The terminal displays the received evacuation route information on a map application (e.g., Google Maps or Apple Maps). The user checks the evacuation route displayed on the terminal and starts evacuation action as necessary. The user can safely head to the shelter by following the instructions on the terminal.

[0308] The specific processing unit 290 transmits the result of 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 voice indicating a user input with respect to the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[0309] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0310] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are examples.

[0311] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0312] [Second Embodiment]

[0313] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0314] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0315] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0316] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0317] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0318] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0319] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0320] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0321] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0322] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0323] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0324] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0325] "Example of form 1"

[0326] The disaster prevention AI system of this invention evaluates the level of risk in a specific area (Tokyo, Kanagawa, Chiba, and Saitama prefectures) when an earthquake occurs, and guides users to appropriate evacuation shelters and evacuation routes. The means for evaluating the level of risk is based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. For example, the greater the magnitude of the earthquake, the closer the epicenter, the shallower the earthquake, and the longer the time elapsed since the earthquake, the higher the level of risk is evaluated. The means for providing evacuation shelter information provides information such as the location, capacity, and facilities of evacuation shelters based on information provided by public databases and local governments. The means for guiding users to appropriate evacuation routes proposes the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0327] "Example of form 2"

[0328] The disaster prevention AI system of this invention evaluates the level of risk in a specific area (Tokyo, Kanagawa, Chiba, and Saitama prefectures) when an earthquake occurs, and guides users to appropriate evacuation shelters and evacuation routes. The means for evaluating the level of risk is based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. For example, the greater the magnitude of the earthquake, the closer the epicenter, the shallower the earthquake, and the longer the time elapsed since the earthquake, the higher the level of risk is evaluated. The means for providing evacuation shelter information provides information such as the location, capacity, and facilities of evacuation shelters based on information provided by public databases and local governments. The means for guiding users to appropriate evacuation routes proposes the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0329] The following describes the processing flow for each example of the form.

[0330] "Example of form 1"

[0331] Step 1: When an earthquake occurs, the disaster prevention AI system collects information such as the magnitude of the earthquake, its epicenter, its depth, the time it occurred, and the time elapsed since the earthquake. This information is obtained from public earthquake information services and sensor networks.

[0332] Step 2: Based on the collected information, assess the risk level of specific areas (Tokyo, Kanagawa, Chiba, and Saitama prefectures). The assessment is based on an algorithm that assigns a higher risk level to earthquakes of larger magnitude, closer proximity to the epicenter, shallower depth, and longer time elapsed since the earthquake.

[0333] Step 3: Provide information on evacuation shelters. Based on information from public databases and local governments, provide information such as the location, capacity, and facilities of evacuation shelters.

[0334] Step 4: Based on information such as current location, evacuation shelter information, road conditions, and traffic conditions, the system proposes the optimal evacuation route. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0335] (Example 1)

[0336] Next, we will describe Embodiment 1 of Embodiment Example 1. 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".

[0337] During earthquake disasters, it is crucial to quickly and accurately assess the risk level of specific areas and provide appropriate shelters and evacuation routes. However, conventional systems have the challenge of handling earthquake information collection, risk assessment, shelter information acquisition, and evacuation route proposals individually, making integrated and efficient responses difficult.

[0338] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0339] In this invention, the server includes means for collecting earthquake information, means for evaluating the degree of risk based on the earthquake information, means for acquiring evacuation shelter information based on the evaluated degree of risk, means for proposing the optimal evacuation route based on the evacuation shelter information and current location information, and means for providing the proposed evacuation route to the user. This enables rapid and accurate risk assessment and provision of evacuation information during an earthquake disaster.

[0340] "Means for collecting earthquake information" refers to devices or methods for acquiring data such as the magnitude of an earthquake, its epicenter, its depth, and the time of its occurrence.

[0341] "Means for assessing risk" refers to a device or method for calculating the risk of earthquake disasters in a specific area based on collected earthquake information.

[0342] "Means for acquiring evacuation shelter information" refers to a device or method for acquiring information such as the location, capacity, and facilities of evacuation shelters based on the assessed risk level.

[0343] "Means for proposing the optimal evacuation route" refers to a device or method for calculating the most suitable evacuation route for a user, taking into account traffic and road conditions, based on evacuation shelter information and current location information.

[0344] "Means to provide to the user" refers to a device or method for communicating the proposed evacuation route to the user visually or audibly.

[0345] A description of embodiments for carrying out this invention will be given.

[0346] The server utilizes an API from an earthquake information service to collect earthquake information. This API can obtain data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time. Based on this data, the server uses its own algorithm to assess the risk level of a specific area. This assessment is designed to indicate a higher risk level when the earthquake is large, the epicenter is close, and the depth is shallow.

[0347] The device obtains evacuation shelter information using the local government's open data API, based on a risk assessment provided by the server. This information includes the location, capacity, and facilities of the evacuation shelters. The device also obtains the user's current location information and sends it to the server.

[0348] The server proposes the optimal evacuation route based on the user's current location and evacuation shelter information. This proposal uses a map information service API and takes into account traffic conditions and road passability. The server sends the calculated evacuation route to the terminal, which then provides the information to the user visually or audibly.

[0349] As a concrete example, let's assume the user is in Shibuya Ward, Tokyo. In this case, the user enters their current location into the device, and the server evaluates the risk level of Shibuya Ward based on earthquake information. Next, the device retrieves information on evacuation shelters within Shibuya Ward, and the server calculates the optimal evacuation route. Finally, the user receives information on evacuation shelters and routes from the device and can begin evacuating quickly.

[0350] An example of a prompt message would be: "I am currently in Shibuya Ward, Tokyo. An earthquake has occurred. The epicenter is in Kanagawa Prefecture, the magnitude is 7.0, and the depth is 10km. Please tell me the best evacuation shelter and evacuation route." Based on this prompt message, the server assesses the level of danger and provides the user with the most appropriate evacuation information.

[0351] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0352] Step 1:

[0353] The server uses an API from an earthquake information service to collect data such as earthquake magnitude, epicenter, depth, and time of occurrence. The input is earthquake information obtained from the API, and the output is a dataset containing this information. This dataset is used for subsequent risk assessments. Specifically, the server periodically calls the API to obtain the latest earthquake information.

[0354] Step 2:

[0355] The server evaluates the risk level of specific regions based on the collected earthquake information. The input is a dataset of earthquake information generated in Step 1, and the output is a risk score for each region. For data processing, an algorithm is used to calculate the risk level, taking into account factors such as earthquake magnitude, distance from the epicenter, and earthquake depth. Specifically, the server calculates the risk score for each region and stores it in a database.

[0356] Step 3:

[0357] The device obtains evacuation shelter information using the local government's open data API, based on a risk assessment provided by the server. Inputs include a risk score and the user's current location, while output includes information such as the location, capacity, and facilities of the evacuation shelter. The data calculation involves determining the evacuation shelter closest to the user's current location. Specifically, the device obtains the user's current location and selects the most suitable evacuation shelter.

[0358] Step 4:

[0359] The server proposes the optimal evacuation route based on the user's current location and evacuation shelter information. Inputs include the user's current location, evacuation shelter information, road conditions, and traffic conditions, and the output generates the optimal evacuation route. Data processing utilizes a map information service API to perform route calculations that consider traffic conditions and road passability. Specifically, the server evaluates multiple routes and selects the most efficient one.

[0360] Step 5:

[0361] The user receives evacuation shelter information and evacuation routes from the server via their device. The input is evacuation route information sent from the server, and the output is the information provided to the user visually or audibly. Specifically, the device guides the user along the evacuation route through map display and voice guidance.

[0362] (Application Example 1)

[0363] Next, we will describe Application Example 1 of Form Example 1. 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."

[0364] During earthquakes, there is a need to automate the selection of appropriate evacuation routes and the guidance of people to shelters in order to ensure a swift and safe evacuation. However, currently, real-time information gathering and the presentation of optimal evacuation routes during earthquakes are insufficient, and there is a particular lack of evacuation support using autonomous vehicles. This could lead to delays and confusion in evacuations.

[0365] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0366] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding appropriate evacuation routes, and means for selecting the optimal evacuation route in an autonomous vehicle during an earthquake and guiding passengers to an evacuation shelter. This enables rapid and safe evacuation during an earthquake.

[0367] "Specific areas" refers to areas that may be affected by earthquake disasters, specifically areas where earthquakes are predicted to occur.

[0368] "Means for evaluating the risk of earthquake disasters" refers to methods or devices for quantifying or ranking the risk of earthquake disasters based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[0369] "Means of providing evacuation shelter information" refers to methods and devices for collecting and providing information such as the location, capacity, and facilities of evacuation shelters to users.

[0370] "Means of guiding users to appropriate evacuation routes" refers to methods or devices that calculate and present the optimal evacuation route to users based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[0371] "Means for selecting the optimal evacuation route in an autonomous vehicle during an earthquake and guiding passengers to a shelter" refers to methods or devices that enable autonomous vehicles to collect information in real time during an earthquake, select the optimal evacuation route, and safely transport passengers to a shelter.

[0372] To implement this invention, a server collects data in real time using earthquake information APIs, map APIs, and traffic information APIs, and evaluates the risk level to earthquake disasters. Based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake, the server quantifies the risk level of a specific area.

[0373] The terminal receives evacuation shelter information from the server and presents the user with details such as the location, capacity, and facilities of the shelter. Furthermore, the terminal calculates the optimal evacuation route based on information such as the user's current location, evacuation shelter information, road conditions, and traffic conditions, and guides the user along that route.

[0374] Based on information from a server, the autonomous vehicle will select the optimal evacuation route during an earthquake and safely guide passengers to a shelter. This will enable rapid and safe evacuation during an earthquake.

[0375] As a concrete example, when an earthquake occurs, inputting the prompt message "An earthquake has occurred. Your current location is Shibuya Ward, Tokyo. Please tell me the best evacuation route." into the AI ​​model can provide the optimal evacuation route.

[0376] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0377] Step 1:

[0378] The server retrieves data such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake from the earthquake information API. Based on this data, it evaluates the risk of earthquake disaster in a specific area. The input is data from the earthquake information API, and the output is the risk evaluation result. The server analyzes this data and quantifies the risk level.

[0379] Step 2:

[0380] The server uses map APIs and traffic information APIs to collect information such as the location, capacity, facilities, road conditions, and traffic conditions of evacuation shelters. Input is data from the map APIs and traffic information APIs, and output is evacuation shelter information and traffic information. The server organizes this information to understand the details of the evacuation shelters.

[0381] Step 3:

[0382] The terminal receives evacuation shelter information and traffic information provided by the server and presents it to the user. The input is evacuation shelter information and traffic information from the server, and the output is the information presented to the user. The terminal displays this information on the screen so that the user can confirm it.

[0383] Step 4:

[0384] The terminal acquires current location information and calculates the optimal evacuation route based on information from the server. Inputs include current location information, evacuation shelter information, and traffic information, while output is the optimal evacuation route. The terminal uses this information to calculate the route and guide the user.

[0385] Step 5:

[0386] The autonomous vehicle safely guides passengers to the evacuation shelter based on the optimal evacuation route provided by the terminal. The input is the optimal evacuation route, and the output is the passengers' arrival at the evacuation shelter. The autonomous vehicle drives according to the route and transports the passengers to their destination.

[0387] (Example 2)

[0388] Next, we will describe Example 2 of Form Example 2. 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".

[0389] During earthquake disasters, it is crucial to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. However, conventional systems struggle to collect information in real time and propose optimal evacuation routes, hindering users from taking swift evacuation action.

[0390] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0391] In this invention, the server includes means for collecting earthquake information, means for evaluating the degree of risk based on the collected information, means for acquiring information such as the location, capacity, and facilities of evacuation shelters, means for proposing the optimal evacuation route based on the current location information, and means for providing information to the user. This makes it possible to quickly and accurately evaluate the degree of risk in a specific area during an earthquake disaster and to provide appropriate evacuation shelters and evacuation routes in real time.

[0392] "Means for collecting earthquake information" refers to functions for acquiring data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time.

[0393] A "means for assessing risk" refers to a function that calculates the risk of earthquake disasters in a specific area based on collected earthquake information.

[0394] "Means for obtaining information such as the location, capacity, and facilities of evacuation shelters" refers to a function for obtaining detailed information about evacuation shelters based on public databases and information from local governments.

[0395] "A means of proposing the optimal evacuation route based on current location information" refers to a function that takes the user's current location into consideration and calculates and proposes the optimal evacuation route based on traffic conditions and road passability.

[0396] "Means of providing information to users" refers to functions that visually or audibly communicate risk assessments, shelter information, and suggested evacuation routes to users.

[0397] This disaster prevention AI system is designed to assess the risk level of specific areas during earthquakes and guide people to appropriate evacuation shelters and routes. The server uses an API from an earthquake information service to collect earthquake information. Specifically, it obtains data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time.

[0398] The server uses a specific algorithm to assess the level of risk based on the collected earthquake information. This algorithm is designed so that the risk level increases with the magnitude of the earthquake, the proximity of the epicenter, and the shallow depth of the earthquake.

[0399] The terminal retrieves information such as the location, capacity, and facilities of evacuation shelters from public databases and local government disaster prevention information systems. This allows users to view detailed information about evacuation shelters.

[0400] The server uses the Google Maps API to calculate the optimal evacuation route based on the user's current location. It considers traffic conditions and road passability to suggest the safest and fastest route.

[0401] Users receive risk assessments, shelter information, and suggested evacuation routes through their devices. The devices display information using a visually easy-to-understand interface and provide voice guidance to navigate the routes.

[0402] As a concrete example, when a user enters "I am in Shibuya Ward, Tokyo," the server immediately collects earthquake information and assesses the level of risk. Next, the device retrieves information on evacuation shelters within Shibuya Ward, and the server uses the Google Maps API to calculate the optimal evacuation route. Finally, the user confirms the evacuation route on the device screen and begins evacuating according to the voice guidance.

[0403] Examples of prompts to input into a generative AI model:

[0404] "I am currently in Shibuya Ward, Tokyo. Please assess the risk level in the event of an earthquake and tell me the best evacuation shelters and routes."

[0405] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0406] Step 1:

[0407] The server calls the API of an earthquake information service to obtain data such as earthquake magnitude, epicenter, depth, and time of occurrence. The input is earthquake information from the API, and the output is a dataset containing this information. The server uses this dataset to prepare for the risk assessment in the next step.

[0408] Step 2:

[0409] The server evaluates the risk level based on the earthquake information obtained in Step 1. The input is a dataset of earthquake information, and the output is a risk score for a specific region. The server applies an algorithm that increases the risk level as the earthquake magnitude increases, the epicenter is closer, and the earthquake depth decreases.

[0410] Step 3:

[0411] The terminal accesses public databases and local government disaster prevention information systems to obtain information such as the location, capacity, and facilities of evacuation shelters. The input is the user's current location, and the output is information about evacuation shelters in that area. The terminal displays this information in a format that is easy for the user to understand.

[0412] Step 4:

[0413] The server uses the Google Maps API to calculate the optimal evacuation route based on the user's current location. The input is the user's current location and evacuation shelter information, and the output is the optimal evacuation route. The server considers traffic conditions and road passability to propose the safest and fastest route.

[0414] Step 5:

[0415] The user receives risk assessments, shelter information, and suggested evacuation routes through the terminal. Input is information from the server, and output is visual and audio guidance to the user. The terminal displays evacuation routes on a map and provides voice guidance to navigate the route.

[0416] (Application Example 2)

[0417] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0418] During earthquake disasters, there is a need to quickly and accurately assess the level of risk in specific areas and guide users to the most suitable evacuation shelters and routes in real time. However, conventional systems have shortcomings in suggesting evacuation routes that do not take into account the user's current location, and real-time risk assessment is difficult.

[0419] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0420] In this invention, the server includes means for evaluating the degree of disaster risk in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, means for acquiring the user's location information, and means for evaluating the degree of risk in real time and guiding users to the optimal evacuation shelter and evacuation route. This enables quick and accurate guidance to evacuation shelters and evacuation routes based on the user's current location.

[0421] "Specific areas" refers to the geographical area that is subject to risk assessment and evacuation guidance during a disaster.

[0422] "Means for assessing the degree of risk to disaster" refers to a device or program that has the function of calculating the degree of disaster risk in a specific area based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the passage of time.

[0423] "Means of providing evacuation facility information" refers to a device or program for providing users with information such as the location, capacity, and facilities of evacuation shelters.

[0424] "Means of guiding users to appropriate evacuation routes" refers to a device or program that has the function of suggesting the optimal evacuation route, taking into account the user's current location information, road conditions, and traffic conditions.

[0425] "Means for obtaining user location information" refers to a device or program that uses technologies such as GPS to obtain the user's current geographical location.

[0426] "A means of assessing the level of risk in real time and guiding users to the optimal shelter and evacuation route" refers to a device or program that has the function of immediately assessing the level of risk in the event of a disaster and providing users with the optimal shelter and evacuation route.

[0427] To implement this invention, a server, a user terminal, and an associated database are required. The server acquires earthquake information and assesses the degree of disaster risk in a specific area. This uses data such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed. Based on this data, the server calculates the degree of risk relevant to the user's current location in real time.

[0428] The user's device obtains its current location using GPS functionality. The device receives evacuation facility information from the server and guides the user to the most suitable evacuation shelter and evacuation route. Road conditions and traffic conditions are also taken into consideration when guiding the evacuation route.

[0429] Specifically, the server utilizes an external earthquake data API to obtain earthquake information. The user's device obtains location information using a GPS module and sends it to the server. The server combines the received location information with earthquake data to assess the level of risk and calculate the optimal evacuation shelter and route. This enables the user to take quick and accurate evacuation action.

[0430] For example, if a user is in Tokyo, the server will immediately acquire earthquake information when an earthquake occurs and guide the user to the optimal evacuation shelter and route based on their current location. It is also possible to provide guidance to the user in natural language using a generative AI model.

[0431] An example of a prompt message could be: "An earthquake has occurred in Tokyo. Your current location is 35.6895, 139.6917. Please tell me the best evacuation shelter and evacuation route." By using this prompt message, the AI ​​model can provide the user with appropriate evacuation information.

[0432] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0433] Step 1:

[0434] The server obtains the latest earthquake information from an external earthquake data API. It receives earthquake data from the API as input and extracts information such as earthquake magnitude, epicenter, depth, and time of occurrence. Based on this data, it generates basic data for assessing the risk level in specific regions.

[0435] Step 2:

[0436] The user's device obtains current location information using its GPS function. It receives location information from the GPS module as input and sends latitude and longitude data to the server. This location information serves as basic data for guiding the user to the most suitable evacuation shelter and evacuation route.

[0437] Step 3:

[0438] The server combines user location information and earthquake data to assess the risk level in a specific area in real time. It receives user location information and earthquake data as input and applies an algorithm to calculate the risk level. As output, it generates a risk level related to the user's current location.

[0439] Step 4:

[0440] The server searches the evacuation facility information database for the most suitable evacuation shelter for the user's current location. It receives the user's location and risk level as input, and selects the optimal shelter considering information such as location, capacity, and facilities. The server then generates information about the optimal shelter as output.

[0441] Step 5:

[0442] The server calculates the optimal evacuation route for the user, taking into account road and traffic conditions. It receives the user's location information, evacuation shelter information, and road condition data as input, and applies a route calculation algorithm. As output, it generates the optimal evacuation route to guide the user.

[0443] Step 6:

[0444] The user's device displays evacuation shelter information and evacuation routes received from the server. It receives evacuation shelter and route information from the server as input and displays it to the user in a visually easy-to-understand format. This enables users to take swift and safe evacuation actions.

[0445] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0446] "Example of form 1"

[0447] One embodiment of the present invention provides a disaster prevention AI system that incorporates an emotion engine. This system assesses the risk of earthquake disasters, provides appropriate shelter information, and guides users along appropriate evacuation routes. Furthermore, it recognizes the user's emotions and provides appropriate evacuation information according to those emotions. Specifically, it recognizes emotions from the user's voice, facial expressions, and actions, and provides appropriate evacuation information according to those emotions. For example, if the user is panicking, it provides evacuation information accompanied by a message to calm them down. Also, if the user is confused, it provides more specific and concise evacuation information. In addition, it adjusts the method of guiding users along evacuation routes according to the user's emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them along the safest and fastest evacuation route.

[0448] "Example of form 2"

[0449] One embodiment of the present invention provides a disaster prevention AI system that incorporates an emotion engine. This system assesses the risk of earthquake disasters, provides appropriate shelter information, and guides users along appropriate evacuation routes. Furthermore, it recognizes the user's emotions and provides appropriate evacuation information according to those emotions. Specifically, it recognizes emotions from the user's voice, facial expressions, and actions, and provides appropriate evacuation information according to those emotions. For example, if the user is panicking, it provides evacuation information accompanied by a message to calm them down. Also, if the user is confused, it provides more specific and concise evacuation information. In addition, it adjusts the method of guiding users along evacuation routes according to the user's emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them along the safest and fastest evacuation route.

[0450] The following describes the processing flow for each example of the form.

[0451] "Example of form 1"

[0452] Step 1: When an earthquake occurs, the disaster prevention AI system determines the magnitude and epicenter of the earthquake.

[0453] Step 2: The disaster prevention AI system assesses the risk level for each region in Tokyo, Kanagawa, Chiba, and Saitama prefectures based on the magnitude and epicenter of the earthquake.

[0454] Step 3: The disaster prevention AI system provides appropriate evacuation shelter information and evacuation routes for each area based on the assessed risk level.

[0455] Step 4: The emotion engine recognizes emotions from the user's voice, facial expressions, and actions.

[0456] Step 5: The emotion engine provides appropriate evacuation information based on the recognized emotion. For example, if the user is panicking, it provides evacuation information accompanied by a calming message. If the user is confused, it provides more specific and concise evacuation information.

[0457] Step 6: The emotion engine adjusts how it guides users through evacuation routes according to their emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them through the safest and quickest evacuation route.

[0458] (Example 1)

[0459] Next, we will describe Embodiment 1 of Embodiment Example 1. 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".

[0460] When a natural disaster occurs, it is necessary to quickly and accurately assess the level of danger and guide people to appropriate evacuation facilities and routes. However, conventional systems have the problem of not adequately providing evacuation information that takes into account the emotional state of users, and are unable to provide appropriate support to users who are in a panic state.

[0461] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0462] In this invention, the server includes means for evaluating the degree of risk to natural disasters in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, means for recognizing the emotional state of the user, and means for providing evacuation information according to the emotional state. This makes it possible to provide appropriate evacuation information according to the emotional state of the user.

[0463] "Specific areas" refers to areas that are subject to risk assessment and evacuation information provision in the event of a natural disaster.

[0464] "Natural disaster" refers to a disaster caused by natural phenomena such as earthquakes and typhoons.

[0465] "Means for assessing risk" refers to methods and devices for assessing the risk level of a specific area based on information such as the scale, location, depth, time of occurrence, and time elapsed since the occurrence of a natural disaster.

[0466] "Means of providing evacuation facility information" refers to methods and devices for collecting and providing information such as the location, capacity, and facilities of evacuation facilities to users.

[0467] "Means of guiding evacuation routes" refers to methods and devices that propose the optimal evacuation route based on information such as current location, evacuation facility information, road conditions, and traffic conditions.

[0468] "Means for recognizing the emotional state of a user" refers to methods or devices for recognizing emotions from a user's voice, facial expressions, behavior, etc.

[0469] "Means of providing evacuation information according to emotional state" refers to methods or devices for providing appropriate evacuation information according to the recognized emotional state of the user.

[0470] In one embodiment of this invention, the server provides a system that assesses the risk level of a specific area when a natural disaster occurs and guides users to appropriate evacuation facilities and routes. The server uses seismometer data and weather information APIs to collect information such as the magnitude of the natural disaster, its location, depth, time of occurrence, and the time elapsed since the disaster. This allows for real-time assessment of the risk level.

[0471] The server utilizes local government databases and open data to collect information such as the location, capacity, and facilities of evacuation centers, and provides this information to users. Furthermore, it uses the Google Maps API and traffic information API to suggest the optimal evacuation route based on current location information, evacuation center information, road conditions, and traffic conditions.

[0472] The device uses voice recognition software and facial recognition technology with a camera to recognize emotions from the user's voice, facial expressions, and actions. This makes it possible to provide appropriate evacuation information according to the user's emotional state.

[0473] As a specific example, if the user is using a smartphone, the device will automatically acquire earthquake information when an earthquake occurs and display the level of danger on the screen. Next, it will display the nearest evacuation facility and its route on a map and begin voice guidance. If the device detects that the user is in a state of panic, it will voice a message such as, "Please stay calm. The nearest evacuation facility is XX." If the device detects that the user is calm, it will present multiple evacuation routes and prompt the user to choose, "Which route would you like to take?"

[0474] An example of a prompt to input into a generating AI model would be, "Please suggest evacuation facilities and routes in the event of a magnitude 7 earthquake in Tokyo." Based on this prompt, the AI ​​model will generate appropriate evacuation information.

[0475] The flow of the specific processing in Example 1 will be explained using Figure 15.

[0476] Step 1:

[0477] The server uses seismometer data and weather information APIs to collect information such as the magnitude, location, depth, time of occurrence, and time elapsed since the occurrence of a natural disaster. It receives seismometer data and information from APIs as input, analyzes this data, and generates basic data for risk assessment. Specifically, the server periodically calls APIs to obtain the latest earthquake information.

[0478] Step 2:

[0479] The server evaluates the risk level of a specific area based on the collected earthquake information. Using the basic data generated in Step 1 as input, it applies a risk assessment algorithm to output a risk score. Specifically, the server calculates the risk score considering factors such as the magnitude of the earthquake and the distance to the epicenter.

[0480] Step 3:

[0481] The server collects information such as the location, capacity, and facilities of evacuation facilities by utilizing local government databases and open data. It receives evacuation facility data provided by local governments as input, analyzes it, and outputs evacuation facility information for users. Specifically, the server periodically updates the database to maintain the latest evacuation facility information.

[0482] Step 4:

[0483] The server proposes the optimal evacuation route based on current location information, evacuation facility information, road conditions, and traffic conditions. Using the user's current location information and the evacuation facility information obtained in step 3 as input, it calculates the optimal route using the Google Maps API and traffic information API, and provides the evacuation route as output. Specifically, the server obtains the user's current location and calculates the route to the nearest evacuation facility.

[0484] Step 5:

[0485] The device recognizes emotions from the user's voice, facial expressions, and actions. It uses audio and video data acquired from the device's microphone and camera as input, and applies an emotion recognition algorithm to output the user's emotional state. Specifically, the device analyzes the user's emotions in real time using voice recognition software and facial recognition technology.

[0486] Step 6:

[0487] The device provides appropriate evacuation information based on the recognized user's emotional state. Using the emotional state obtained in step 5 as input, it generates and outputs messages and evacuation information tailored to that emotion. Specifically, the device sends calming messages to panicked users and presents multiple options to calm users.

[0488] (Application Example 1)

[0489] Next, we will describe Application Example 1 of Form Example 1. 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."

[0490] During earthquake disasters, it is essential to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. Furthermore, providing appropriate evacuation information tailored to the emotional state of users is crucial to preventing panic and confusion and supporting safe evacuation. In addition, autonomous vehicles are required to select and guide passengers along the optimal evacuation route based on their emotions.

[0491] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0492] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding appropriate evacuation routes, means for recognizing the user's emotions and providing evacuation information corresponding to those emotions, and means for selecting and guiding passengers on evacuation routes based on their emotions in an autonomous vehicle. This enables rapid and appropriate evacuation support during earthquake disasters and ensures the safety of users.

[0493] "Specific areas" refers to areas that are subject to risk assessment and evacuation information provision during earthquake disasters, and specifically includes areas such as Tokyo, Kanagawa, Chiba, and Saitama prefectures.

[0494] "Means for assessing the risk of earthquake disasters" refers to methods and devices for assessing the risk level of a specific area based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[0495] "Means of providing evacuation shelter information" refers to methods or devices for providing information such as the location, capacity, and facilities of evacuation shelters in a specific area.

[0496] "Means of guiding appropriate evacuation routes" refers to methods and devices that propose and guide users to the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[0497] "Means of recognizing users' emotions and providing evacuation information in accordance with those emotions" refers to methods or devices for recognizing users' emotions from their voice, facial expressions, and actions, and providing appropriate evacuation information in accordance with those emotions.

[0498] "Means for selecting and guiding passengers on evacuation routes based on their emotions in autonomous vehicles" refers to methods or devices for recognizing passengers' emotions within an autonomous vehicle and selecting and guiding them on the optimal evacuation route based on those emotions.

[0499] The system for carrying out this invention includes a server, a terminal, and an autonomous vehicle. The server runs a program to assess the risk of earthquake disasters in a specific area, provide information on evacuation shelters, and guide appropriate evacuation routes. The server collects information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of the earthquake, and the time elapsed since the earthquake, and assesses the risk based on this data.

[0500] The device senses the user's voice, facial expressions, and actions, and executes a program to recognize emotions. Emotion recognition uses speech recognition software and image analysis software. Specifically, it uses a "Speech Recognition API" for speech recognition and an "Image Analysis API" for image analysis.

[0501] The autonomous vehicle runs a program to select and guide passengers along the optimal evacuation route based on their emotions. It uses in-vehicle cameras and microphones to analyze passengers' emotions in real time and provide evacuation information tailored to those emotions. A "map API" is used for route selection, and a "generative AI model" is used for generating emotion-based messages.

[0502] As a concrete example, during an earthquake, cameras inside the autonomous vehicle capture passengers' facial expressions and microphones collect their voices. This data is analyzed using an "image analysis API" and a "speech recognition API," and if it is determined that the passengers are in a state of panic, a "generative AI model" generates a message such as, "Please stay calm. We will guide you to the safest evacuation route."

[0503] An example of a prompt message is, "The passenger is in a panic. Generate a calming message."

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[0505] Step 1:

[0506] The server collects information such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake. This information is obtained from earthquake observation agencies and meteorological databases. The input is earthquake-related data, and the output is basic data for risk assessment. The server analyzes this data to assess the risk level of a specific area.

[0507] Step 2:

[0508] The device uses a camera and microphone to collect data in order to sense the user's voice, facial expressions, and actions. The input is the user's voice and image data, and the output is the emotion recognition result. The device uses a "Voice Recognition API" and an "Image Analysis API" to analyze this data and recognize the user's emotions.

[0509] Step 3:

[0510] The server selects appropriate evacuation shelter information and evacuation routes based on the risk assessment results and the user's sentiment recognition results. The inputs are the risk assessment results and sentiment recognition results, and the outputs are evacuation shelter information and evacuation route information. The server uses the "Map API" to calculate the optimal evacuation route.

[0511] Step 4:

[0512] The autonomous vehicle guides passengers along evacuation routes based on evacuation route information received from a server. The input is evacuation route information, and the output is guidance messages for passengers. The vehicle uses a "generative AI model" to generate messages that respond to the passengers' emotions and provides guidance via voice or display.

[0513] Step 5:

[0514] The user begins a safe evacuation based on evacuation information provided by the vehicle. The input is guidance messages from the vehicle, and the output is the user's evacuation actions. The user selects the optimal evacuation route and carries out the evacuation according to the provided information.

[0515] (Example 2)

[0516] Next, we will describe Example 2 of Form Example 2. 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] In the event of a natural disaster, it is essential to quickly and accurately assess the level of risk in specific areas and provide appropriate evacuation facilities and routes. Furthermore, there is a lack of information provision tailored to the emotional state of users, necessitating measures to prevent panic and confusion.

[0518] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0519] In this invention, the server includes means for evaluating the risk of natural disasters in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, and means for recognizing the user's emotions and providing evacuation information that corresponds to those emotions. This enables rapid and accurate risk assessment in the event of a natural disaster and the provision of appropriate evacuation information that corresponds to the user's emotions.

[0520] "Specific areas" refers to areas that may be affected by natural disasters, and specifically includes administrative divisions and geographical boundaries.

[0521] "Natural disasters" refer to disasters caused by natural phenomena such as earthquakes, typhoons, and floods.

[0522] "Means for assessing risk" refers to methods and devices that use information about natural disasters to indicate the degree of their impact using numerical values ​​or indicators.

[0523] "Means of providing information on evacuation facilities" refers to methods and devices for providing users with information such as the location, capacity, and equipment status of evacuation shelters and facilities.

[0524] "Means of guiding evacuation routes" refers to methods and devices that present the optimal route for users to safely reach evacuation facilities.

[0525] "Means of recognizing emotions" refers to methods and devices for determining a user's emotional state from their voice, facial expressions, behavior, etc.

[0526] "Means of providing evacuation information in accordance with emotions" refers to methods and devices for providing appropriate evacuation information and messages according to the emotional state of the user.

[0527] This invention is a system aimed at rapid and accurate risk assessment during natural disasters and providing appropriate evacuation information tailored to the user's emotions. Specific embodiments of this system are described below.

[0528] The server collects information on natural disasters and assesses the risk level of specific areas. This involves using APIs from the Japan Meteorological Agency and earthquake research institutions to obtain data such as earthquake magnitude, location, depth, time of occurrence, and time elapsed since the event. Using this data, the server employs machine learning models based on historical disaster data to quantify the risk level.

[0529] The terminal receives risk information transmitted from the server and notifies the user. The terminal also uses GPS to obtain the user's current location and calculates the optimal evacuation route in real time based on evacuation facility information provided by the server. This enables the user to evacuate safely and quickly.

[0530] Furthermore, the device detects the user's voice, facial expressions, and actions through its camera and microphone, and recognizes their emotions. If it determines that the user is in a state of panic, the device displays a message such as, "Please stay calm. We will guide you to a safe evacuation route." If the user is calm, it presents multiple evacuation routes and prompts them to choose one.

[0531] As a concrete example, by inputting the prompt message "Please tell me how to assess the risk level and provide evacuation information during an earthquake" into an AI model during an earthquake, the AI ​​model will generate appropriate response methods for earthquake disasters. This prompt message allows the system to quickly present countermeasures and ensure the safety of users.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 17.

[0533] Step 1:

[0534] The server collects information about natural disasters. As input, it obtains data such as earthquake magnitude, location, depth, time of occurrence, and time elapsed since the earthquake from APIs of the Japan Meteorological Agency and earthquake research institutions. Based on this data, the server processes the data to assess the level of risk and converts it into a format suitable for input into the assessment model. As output, it generates data ready for input into the assessment model.

[0535] Step 2:

[0536] The server uses the collected data and a machine learning model based on past disaster data to assess the risk level of a specific area. The data generated in Step 1 is used as input. The server performs data calculations using the machine learning model to quantify the risk level for each area. The server generates the risk assessment results for each area as output and sends them to the terminal.

[0537] Step 3:

[0538] The terminal receives the risk assessment results sent from the server and notifies the user. It receives the risk assessment results from the server as input. The terminal displays the assessment results on the screen and also provides audio notification. It notifies the user of the risk level as output.

[0539] Step 4:

[0540] The device uses GPS functionality to obtain the user's current location. The device's location services are used as input. The device obtains the current location information and sends it to the server. The current location information is provided to the server as output.

[0541] Step 5:

[0542] The server calculates the optimal evacuation route based on the user's current location and information on evacuation facilities. The inputs used are the user's current location from the terminal and pre-collected information on evacuation facilities. The server also considers road and traffic conditions, performing data calculations to determine the optimal route. The output is the generated information on the optimal evacuation route, which is then sent to the terminal.

[0543] Step 6:

[0544] The terminal receives evacuation route information transmitted from the server and guides the user. It receives evacuation route information from the server as input. The terminal presents the user with specific evacuation routes through screen displays and voice guidance. It provides evacuation route guidance to the user as output.

[0545] Step 7:

[0546] The device senses the user's voice, facial expressions, and actions through its camera and microphone, and recognizes their emotions. It uses data from its sensors as input. The device performs data processing using an emotion recognition algorithm to determine the user's emotional state. As output, it evaluates the user's emotional state and prepares to provide appropriate evacuation information.

[0547] Step 8:

[0548] The device provides appropriate evacuation information based on the user's emotional state. The emotional state assessed in step 7 is used as input. The device displays calming messages to panicked users and presents multiple evacuation routes to calm users. The output provides evacuation information tailored to the user's emotional state.

[0549] (Application Example 2)

[0550] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0551] During earthquake disasters, it is essential to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. However, conventional systems have a challenge in providing evacuation information that takes into account the emotional state of users, making it difficult to provide appropriate information to users in a panicked state.

[0552] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0553] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding users to appropriate evacuation routes, and means for recognizing the user's emotions and providing evacuation information corresponding to those emotions. This makes it possible to provide appropriate evacuation information according to the user's emotional state.

[0554] "Specific areas" refer to areas that are subject to risk assessment and evacuation information provision during earthquake disasters.

[0555] "Methods for assessing the risk of earthquake disasters" are methods for assessing the risk to a specific area based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[0556] "Means of providing information on evacuation shelters" refers to means of providing information such as the location, capacity, and facilities of evacuation shelters in a specific area.

[0557] "Means of guiding people to appropriate evacuation routes" refers to methods for suggesting the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[0558] "Means of recognizing users' emotions and providing evacuation information in accordance with those emotions" refers to means of recognizing users' emotions from their voice, facial expressions, and actions, and providing appropriate evacuation information in accordance with those emotions.

[0559] The system for carrying out this invention includes a server and a terminal. The server runs a program to assess the risk level of a specific area during an earthquake disaster and provide information on evacuation shelters and appropriate evacuation routes. Specifically, the server obtains earthquake information using the Japan Meteorological Agency API and analyzes data such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. This allows the server to assess the risk level of a specific area.

[0560] The server also retrieves evacuation shelter information from local government databases and calculates the optimal evacuation route using the Google Maps API. Furthermore, the terminal acquires the user's voice and facial expressions using the smartphone's microphone and camera, and analyzes them with OpenAI's emotion recognition model. This allows the system to recognize the user's emotional state and provide evacuation information tailored to that emotion.

[0561] As a concrete example, when an earthquake occurs, the server sends information to the terminal such as, "An earthquake with a seismic intensity of 6 has occurred. The nearest evacuation center is XX Park. Please evacuate using this route." If the user is in a panic state, the terminal displays a message such as, "Please stay calm. We will guide you to a safe evacuation center."

[0562] An example of a prompt message for a generative AI model is, "Recognize the user's emotions from their voice and facial expressions, and generate appropriate evacuation information."

[0563] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[0564] Step 1:

[0565] The server retrieves earthquake information from the Japan Meteorological Agency API. It uses the timestamp of the earthquake as input. The output includes data such as earthquake magnitude, epicenter, depth, and time of occurrence. This data is then processed to assess the risk level for specific regions.

[0566] Step 2:

[0567] The server retrieves evacuation shelter information from local government databases. It uses a specific regional identifier as input. The output includes information such as the location, capacity, and facilities of the evacuation shelters. Based on this information, the server selects the appropriate evacuation shelter.

[0568] Step 3:

[0569] The server uses the Google Maps API to calculate the optimal evacuation route. It uses the user's current location and evacuation shelter information as input. The output is the optimal evacuation route. Based on this route information, the server processes the data to guide the user.

[0570] Step 4:

[0571] The device uses the smartphone's microphone and camera to capture the user's voice and facial expressions. Real-time audio and video data of the user is used as input. Audio and facial expression data are obtained as output. This data is then processed for emotion recognition.

[0572] Step 5:

[0573] The device analyzes the user's emotions using OpenAI's emotion recognition model. It uses voice and facial expression data as input. The output is the user's emotional state. Based on this emotional information, it performs data calculations to generate appropriate evacuation information.

[0574] Step 6:

[0575] The server generates evacuation information based on the user's emotional state and sends it to the terminal. It uses emotional information and evacuation route information as input. The output is evacuation information to be displayed to the user. Specifically, if the user is in a panic state, it generates a message such as, "Please stay calm. We will guide you to a safe evacuation center."

[0576] (Other examples)

[0577] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0578] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0579] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0580] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are examples.

[0581] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0582] [Third Embodiment]

[0583] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0584] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0585] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0586] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0587] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0588] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0589] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0590] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0591] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0592] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0593] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0594] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0595] "Example of form 1"

[0596] The disaster prevention AI system of this invention evaluates the level of risk in a specific area (Tokyo, Kanagawa, Chiba, and Saitama prefectures) when an earthquake occurs, and guides users to appropriate evacuation shelters and evacuation routes. The means for evaluating the level of risk is based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. For example, the greater the magnitude of the earthquake, the closer the epicenter, the shallower the earthquake, and the longer the time elapsed since the earthquake, the higher the level of risk is evaluated. The means for providing evacuation shelter information provides information such as the location, capacity, and facilities of evacuation shelters based on information provided by public databases and local governments. The means for guiding users to appropriate evacuation routes proposes the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0597] "Example of form 2"

[0598] The disaster prevention AI system of this invention evaluates the level of risk in a specific area (Tokyo, Kanagawa, Chiba, and Saitama prefectures) when an earthquake occurs, and guides users to appropriate evacuation shelters and evacuation routes. The means for evaluating the level of risk is based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. For example, the greater the magnitude of the earthquake, the closer the epicenter, the shallower the earthquake, and the longer the time elapsed since the earthquake, the higher the level of risk is evaluated. The means for providing evacuation shelter information provides information such as the location, capacity, and facilities of evacuation shelters based on information provided by public databases and local governments. The means for guiding users to appropriate evacuation routes proposes the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0599] The following describes the processing flow for each example of the form.

[0600] "Example of form 1"

[0601] Step 1: When an earthquake occurs, the disaster prevention AI system collects information such as the magnitude of the earthquake, its epicenter, its depth, the time it occurred, and the time elapsed since the earthquake. This information is obtained from public earthquake information services and sensor networks.

[0602] Step 2: Based on the collected information, assess the risk level of specific areas (Tokyo, Kanagawa, Chiba, and Saitama prefectures). The assessment is based on an algorithm that assigns a higher risk level to earthquakes of larger magnitude, closer proximity to the epicenter, shallower depth, and longer time elapsed since the earthquake.

[0603] Step 3: Provide information on evacuation shelters. Based on information from public databases and local governments, provide information such as the location, capacity, and facilities of evacuation shelters.

[0604] Step 4: Based on information such as current location, evacuation shelter information, road conditions, and traffic conditions, the system proposes the optimal evacuation route. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0605] (Example 1)

[0606] Next, we will describe Embodiment 1 of Example 1. 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."

[0607] During earthquake disasters, it is crucial to quickly and accurately assess the risk level of specific areas and provide appropriate shelters and evacuation routes. However, conventional systems have the challenge of handling earthquake information collection, risk assessment, shelter information acquisition, and evacuation route proposals individually, making integrated and efficient responses difficult.

[0608] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0609] In this invention, the server includes means for collecting earthquake information, means for evaluating the degree of risk based on the earthquake information, means for acquiring evacuation shelter information based on the evaluated degree of risk, means for proposing the optimal evacuation route based on the evacuation shelter information and current location information, and means for providing the proposed evacuation route to the user. This enables rapid and accurate risk assessment and provision of evacuation information during an earthquake disaster.

[0610] "Means for collecting earthquake information" refers to devices or methods for acquiring data such as the magnitude of an earthquake, its epicenter, its depth, and the time of its occurrence.

[0611] "Means for assessing risk" refers to a device or method for calculating the risk of earthquake disasters in a specific area based on collected earthquake information.

[0612] "Means for acquiring evacuation shelter information" refers to a device or method for acquiring information such as the location, capacity, and facilities of evacuation shelters based on the assessed risk level.

[0613] "Means for proposing the optimal evacuation route" refers to a device or method for calculating the most suitable evacuation route for a user, taking into account traffic and road conditions, based on evacuation shelter information and current location information.

[0614] "Means to provide to the user" refers to a device or method for communicating the proposed evacuation route to the user visually or audibly.

[0615] A description of embodiments for carrying out this invention will be given.

[0616] The server utilizes an API from an earthquake information service to collect earthquake information. This API can obtain data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time. Based on this data, the server uses its own algorithm to assess the risk level of a specific area. This assessment is designed to indicate a higher risk level when the earthquake is large, the epicenter is close, and the depth is shallow.

[0617] The device obtains evacuation shelter information using the local government's open data API, based on a risk assessment provided by the server. This information includes the location, capacity, and facilities of the evacuation shelters. The device also obtains the user's current location information and sends it to the server.

[0618] The server proposes the optimal evacuation route based on the user's current location and evacuation shelter information. This proposal uses a map information service API and takes into account traffic conditions and road passability. The server sends the calculated evacuation route to the terminal, which then provides the information to the user visually or audibly.

[0619] As a concrete example, let's assume the user is in Shibuya Ward, Tokyo. In this case, the user enters their current location into the device, and the server evaluates the risk level of Shibuya Ward based on earthquake information. Next, the device retrieves information on evacuation shelters within Shibuya Ward, and the server calculates the optimal evacuation route. Finally, the user receives information on evacuation shelters and routes from the device and can begin evacuating quickly.

[0620] An example of a prompt message would be: "I am currently in Shibuya Ward, Tokyo. An earthquake has occurred. The epicenter is in Kanagawa Prefecture, the magnitude is 7.0, and the depth is 10km. Please tell me the best evacuation shelter and evacuation route." Based on this prompt message, the server assesses the level of danger and provides the user with the most appropriate evacuation information.

[0621] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0622] Step 1:

[0623] The server uses an API from an earthquake information service to collect data such as earthquake magnitude, epicenter, depth, and time of occurrence. The input is earthquake information obtained from the API, and the output is a dataset containing this information. This dataset is used for subsequent risk assessments. Specifically, the server periodically calls the API to obtain the latest earthquake information.

[0624] Step 2:

[0625] The server evaluates the risk level of specific regions based on the collected earthquake information. The input is a dataset of earthquake information generated in Step 1, and the output is a risk score for each region. For data processing, an algorithm is used to calculate the risk level, taking into account factors such as earthquake magnitude, distance from the epicenter, and earthquake depth. Specifically, the server calculates the risk score for each region and stores it in a database.

[0626] Step 3:

[0627] The device obtains evacuation shelter information using the local government's open data API, based on a risk assessment provided by the server. Inputs include a risk score and the user's current location, while output includes information such as the location, capacity, and facilities of the evacuation shelter. The data calculation involves determining the evacuation shelter closest to the user's current location. Specifically, the device obtains the user's current location and selects the most suitable evacuation shelter.

[0628] Step 4:

[0629] The server proposes the optimal evacuation route based on the user's current location and evacuation shelter information. Inputs include the user's current location, evacuation shelter information, road conditions, and traffic conditions, and the output generates the optimal evacuation route. Data processing utilizes a map information service API to perform route calculations that consider traffic conditions and road passability. Specifically, the server evaluates multiple routes and selects the most efficient one.

[0630] Step 5:

[0631] The user receives evacuation shelter information and evacuation routes from the server via their device. The input is evacuation route information sent from the server, and the output is the information provided to the user visually or audibly. Specifically, the device guides the user along the evacuation route through map display and voice guidance.

[0632] (Application Example 1)

[0633] Next, we will describe Application Example 1 of Form Example 1. 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."

[0634] During earthquakes, there is a need to automate the selection of appropriate evacuation routes and the guidance of people to shelters in order to ensure a swift and safe evacuation. However, currently, real-time information gathering and the presentation of optimal evacuation routes during earthquakes are insufficient, and there is a particular lack of evacuation support using autonomous vehicles. This could lead to delays and confusion in evacuations.

[0635] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0636] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding appropriate evacuation routes, and means for selecting the optimal evacuation route in an autonomous vehicle during an earthquake and guiding passengers to an evacuation shelter. This enables rapid and safe evacuation during an earthquake.

[0637] "Specific areas" refers to areas that may be affected by earthquake disasters, specifically areas where earthquakes are predicted to occur.

[0638] "Means for evaluating the risk of earthquake disasters" refers to methods or devices for quantifying or ranking the risk of earthquake disasters based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[0639] "Means of providing evacuation shelter information" refers to methods and devices for collecting and providing information such as the location, capacity, and facilities of evacuation shelters to users.

[0640] "Means of guiding users to appropriate evacuation routes" refers to methods or devices that calculate and present the optimal evacuation route to users based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[0641] "Means for selecting the optimal evacuation route in an autonomous vehicle during an earthquake and guiding passengers to a shelter" refers to methods or devices that enable autonomous vehicles to collect information in real time during an earthquake, select the optimal evacuation route, and safely transport passengers to a shelter.

[0642] To implement this invention, a server collects data in real time using earthquake information APIs, map APIs, and traffic information APIs, and evaluates the risk level to earthquake disasters. Based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake, the server quantifies the risk level of a specific area.

[0643] The terminal receives evacuation shelter information from the server and presents the user with details such as the location, capacity, and facilities of the shelter. Furthermore, the terminal calculates the optimal evacuation route based on information such as the user's current location, evacuation shelter information, road conditions, and traffic conditions, and guides the user along that route.

[0644] Based on information from a server, the autonomous vehicle will select the optimal evacuation route during an earthquake and safely guide passengers to a shelter. This will enable rapid and safe evacuation during an earthquake.

[0645] As a concrete example, when an earthquake occurs, inputting the prompt message "An earthquake has occurred. Your current location is Shibuya Ward, Tokyo. Please tell me the best evacuation route." into the AI ​​model can provide the optimal evacuation route.

[0646] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0647] Step 1:

[0648] The server retrieves data such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake from the earthquake information API. Based on this data, it evaluates the risk of earthquake disaster in a specific area. The input is data from the earthquake information API, and the output is the risk evaluation result. The server analyzes this data and quantifies the risk level.

[0649] Step 2:

[0650] The server uses map APIs and traffic information APIs to collect information such as the location, capacity, facilities, road conditions, and traffic conditions of evacuation shelters. Input is data from the map APIs and traffic information APIs, and output is evacuation shelter information and traffic information. The server organizes this information to understand the details of the evacuation shelters.

[0651] Step 3:

[0652] The terminal receives evacuation shelter information and traffic information provided by the server and presents it to the user. The input is evacuation shelter information and traffic information from the server, and the output is the information presented to the user. The terminal displays this information on the screen so that the user can confirm it.

[0653] Step 4:

[0654] The terminal acquires current location information and calculates the optimal evacuation route based on information from the server. Inputs include current location information, evacuation shelter information, and traffic information, while output is the optimal evacuation route. The terminal uses this information to calculate the route and guide the user.

[0655] Step 5:

[0656] The autonomous vehicle safely guides passengers to the evacuation shelter based on the optimal evacuation route provided by the terminal. The input is the optimal evacuation route, and the output is the passengers' arrival at the evacuation shelter. The autonomous vehicle drives according to the route and transports the passengers to their destination.

[0657] (Example 2)

[0658] Next, we will describe Example 2 of the morphological example. 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."

[0659] During earthquake disasters, it is crucial to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. However, conventional systems struggle to collect information in real time and propose optimal evacuation routes, hindering users from taking swift evacuation action.

[0660] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0661] In this invention, the server includes means for collecting earthquake information, means for evaluating the degree of risk based on the collected information, means for acquiring information such as the location, capacity, and facilities of evacuation shelters, means for proposing the optimal evacuation route based on the current location information, and means for providing information to the user. This makes it possible to quickly and accurately evaluate the degree of risk in a specific area during an earthquake disaster and to provide appropriate evacuation shelters and evacuation routes in real time.

[0662] "Means for collecting earthquake information" refers to functions for acquiring data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time.

[0663] A "means for assessing risk" refers to a function that calculates the risk of earthquake disasters in a specific area based on collected earthquake information.

[0664] "Means for obtaining information such as the location, capacity, and facilities of evacuation shelters" refers to a function for obtaining detailed information about evacuation shelters based on public databases and information from local governments.

[0665] "A means of proposing the optimal evacuation route based on current location information" refers to a function that takes the user's current location into consideration and calculates and proposes the optimal evacuation route based on traffic conditions and road passability.

[0666] "Means of providing information to users" refers to functions that visually or audibly communicate risk assessments, shelter information, and suggested evacuation routes to users.

[0667] This disaster prevention AI system is designed to assess the risk level of specific areas during earthquakes and guide people to appropriate evacuation shelters and routes. The server uses an API from an earthquake information service to collect earthquake information. Specifically, it obtains data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time.

[0668] The server uses a specific algorithm to assess the level of risk based on the collected earthquake information. This algorithm is designed so that the risk level increases with the magnitude of the earthquake, the proximity of the epicenter, and the shallow depth of the earthquake.

[0669] The terminal retrieves information such as the location, capacity, and facilities of evacuation shelters from public databases and local government disaster prevention information systems. This allows users to view detailed information about evacuation shelters.

[0670] The server uses the Google Maps API to calculate the optimal evacuation route based on the user's current location. It considers traffic conditions and road passability to suggest the safest and fastest route.

[0671] Users receive risk assessments, shelter information, and suggested evacuation routes through their devices. The devices display information using a visually easy-to-understand interface and provide voice guidance to navigate the routes.

[0672] As a concrete example, when a user enters "I am in Shibuya Ward, Tokyo," the server immediately collects earthquake information and assesses the level of risk. Next, the device retrieves information on evacuation shelters within Shibuya Ward, and the server uses the Google Maps API to calculate the optimal evacuation route. Finally, the user confirms the evacuation route on the device screen and begins evacuating according to the voice guidance.

[0673] Examples of prompts to input into a generative AI model:

[0674] "I am currently in Shibuya Ward, Tokyo. Please assess the risk level in the event of an earthquake and tell me the best evacuation shelters and routes."

[0675] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0676] Step 1:

[0677] The server calls the API of an earthquake information service to obtain data such as earthquake magnitude, epicenter, depth, and time of occurrence. The input is earthquake information from the API, and the output is a dataset containing this information. The server uses this dataset to prepare for the risk assessment in the next step.

[0678] Step 2:

[0679] The server evaluates the risk level based on the earthquake information obtained in Step 1. The input is a dataset of earthquake information, and the output is a risk score for a specific region. The server applies an algorithm that increases the risk level as the earthquake magnitude increases, the epicenter is closer, and the earthquake depth decreases.

[0680] Step 3:

[0681] The terminal accesses public databases and local government disaster prevention information systems to obtain information such as the location, capacity, and facilities of evacuation shelters. The input is the user's current location, and the output is information about evacuation shelters in that area. The terminal displays this information in a format that is easy for the user to understand.

[0682] Step 4:

[0683] The server uses the Google Maps API to calculate the optimal evacuation route based on the user's current location. The input is the user's current location and evacuation shelter information, and the output is the optimal evacuation route. The server considers traffic conditions and road passability to propose the safest and fastest route.

[0684] Step 5:

[0685] The user receives risk assessments, shelter information, and suggested evacuation routes through the terminal. Input is information from the server, and output is visual and audio guidance to the user. The terminal displays evacuation routes on a map and provides voice guidance to navigate the route.

[0686] (Application Example 2)

[0687] Next, we will describe application example 2 of form example 2. 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."

[0688] During earthquake disasters, there is a need to quickly and accurately assess the level of risk in specific areas and guide users to the most suitable evacuation shelters and routes in real time. However, conventional systems have shortcomings in suggesting evacuation routes that do not take into account the user's current location, and real-time risk assessment is difficult.

[0689] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0690] In this invention, the server includes means for evaluating the degree of disaster risk in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, means for acquiring the user's location information, and means for evaluating the degree of risk in real time and guiding users to the optimal evacuation shelter and evacuation route. This enables quick and accurate guidance to evacuation shelters and evacuation routes based on the user's current location.

[0691] "Specific areas" refers to the geographical area that is subject to risk assessment and evacuation guidance during a disaster.

[0692] "Means for assessing the degree of risk to disaster" refers to a device or program that has the function of calculating the degree of disaster risk in a specific area based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the passage of time.

[0693] "Means of providing evacuation facility information" refers to a device or program for providing users with information such as the location, capacity, and facilities of evacuation shelters.

[0694] "Means of guiding users to appropriate evacuation routes" refers to a device or program that has the function of suggesting the optimal evacuation route, taking into account the user's current location information, road conditions, and traffic conditions.

[0695] "Means for obtaining user location information" refers to a device or program that uses technologies such as GPS to obtain the user's current geographical location.

[0696] "A means of assessing the level of risk in real time and guiding users to the optimal shelter and evacuation route" refers to a device or program that has the function of immediately assessing the level of risk in the event of a disaster and providing users with the optimal shelter and evacuation route.

[0697] To implement this invention, a server, a user terminal, and an associated database are required. The server acquires earthquake information and assesses the degree of disaster risk in a specific area. This uses data such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed. Based on this data, the server calculates the degree of risk relevant to the user's current location in real time.

[0698] The user's device obtains its current location using GPS functionality. The device receives evacuation facility information from the server and guides the user to the most suitable evacuation shelter and evacuation route. Road conditions and traffic conditions are also taken into consideration when guiding the evacuation route.

[0699] Specifically, the server utilizes an external earthquake data API to obtain earthquake information. The user's device obtains location information using a GPS module and sends it to the server. The server combines the received location information with earthquake data to assess the level of risk and calculate the optimal evacuation shelter and route. This enables the user to take quick and accurate evacuation action.

[0700] For example, if a user is in Tokyo, the server will immediately acquire earthquake information when an earthquake occurs and guide the user to the optimal evacuation shelter and route based on their current location. It is also possible to provide guidance to the user in natural language using a generative AI model.

[0701] An example of a prompt message could be: "An earthquake has occurred in Tokyo. Your current location is 35.6895, 139.6917. Please tell me the best evacuation shelter and evacuation route." By using this prompt message, the AI ​​model can provide the user with appropriate evacuation information.

[0702] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0703] Step 1:

[0704] The server obtains the latest earthquake information from an external earthquake data API. It receives earthquake data from the API as input and extracts information such as earthquake magnitude, epicenter, depth, and time of occurrence. Based on this data, it generates basic data for assessing the risk level in specific regions.

[0705] Step 2:

[0706] The user's device obtains current location information using its GPS function. It receives location information from the GPS module as input and sends latitude and longitude data to the server. This location information serves as basic data for guiding the user to the most suitable evacuation shelter and evacuation route.

[0707] Step 3:

[0708] The server combines user location information and earthquake data to assess the risk level in a specific area in real time. It receives user location information and earthquake data as input and applies an algorithm to calculate the risk level. As output, it generates a risk level related to the user's current location.

[0709] Step 4:

[0710] The server searches the evacuation facility information database for the most suitable evacuation shelter for the user's current location. It receives the user's location and risk level as input, and selects the optimal shelter considering information such as location, capacity, and facilities. The server then generates information about the optimal shelter as output.

[0711] Step 5:

[0712] The server calculates the optimal evacuation route for the user, taking into account road and traffic conditions. It receives the user's location information, evacuation shelter information, and road condition data as input, and applies a route calculation algorithm. As output, it generates the optimal evacuation route to guide the user.

[0713] Step 6:

[0714] The user's device displays evacuation shelter information and evacuation routes received from the server. It receives evacuation shelter and route information from the server as input and displays it to the user in a visually easy-to-understand format. This enables users to take swift and safe evacuation actions.

[0715] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0716] "Example of form 1"

[0717] One embodiment of the present invention provides a disaster prevention AI system that incorporates an emotion engine. This system assesses the risk of earthquake disasters, provides appropriate shelter information, and guides users along appropriate evacuation routes. Furthermore, it recognizes the user's emotions and provides appropriate evacuation information according to those emotions. Specifically, it recognizes emotions from the user's voice, facial expressions, and actions, and provides appropriate evacuation information according to those emotions. For example, if the user is panicking, it provides evacuation information accompanied by a message to calm them down. Also, if the user is confused, it provides more specific and concise evacuation information. In addition, it adjusts the method of guiding users along evacuation routes according to the user's emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them along the safest and fastest evacuation route.

[0718] "Example of form 2"

[0719] One embodiment of the present invention provides a disaster prevention AI system that incorporates an emotion engine. This system assesses the risk of earthquake disasters, provides appropriate shelter information, and guides users along appropriate evacuation routes. Furthermore, it recognizes the user's emotions and provides appropriate evacuation information according to those emotions. Specifically, it recognizes emotions from the user's voice, facial expressions, and actions, and provides appropriate evacuation information according to those emotions. For example, if the user is panicking, it provides evacuation information accompanied by a message to calm them down. Also, if the user is confused, it provides more specific and concise evacuation information. In addition, it adjusts the method of guiding users along evacuation routes according to the user's emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them along the safest and fastest evacuation route.

[0720] The following describes the processing flow for each example of the form.

[0721] "Example of form 1"

[0722] Step 1: When an earthquake occurs, the disaster prevention AI system determines the magnitude and epicenter of the earthquake.

[0723] Step 2: The disaster prevention AI system assesses the risk level for each region in Tokyo, Kanagawa, Chiba, and Saitama prefectures based on the magnitude and epicenter of the earthquake.

[0724] Step 3: The disaster prevention AI system provides appropriate evacuation shelter information and evacuation routes for each area based on the assessed risk level.

[0725] Step 4: The emotion engine recognizes emotions from the user's voice, facial expressions, and actions.

[0726] Step 5: The emotion engine provides appropriate evacuation information based on the recognized emotion. For example, if the user is panicking, it provides evacuation information accompanied by a calming message. If the user is confused, it provides more specific and concise evacuation information.

[0727] Step 6: The emotion engine adjusts how it guides users through evacuation routes according to their emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them through the safest and quickest evacuation route.

[0728] (Example 1)

[0729] Next, we will describe Embodiment 1 of Example 1. 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."

[0730] When a natural disaster occurs, it is necessary to quickly and accurately assess the level of danger and guide people to appropriate evacuation facilities and routes. However, conventional systems have the problem of not adequately providing evacuation information that takes into account the emotional state of users, and are unable to provide appropriate support to users who are in a panic state.

[0731] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0732] In this invention, the server includes means for evaluating the degree of risk to natural disasters in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, means for recognizing the emotional state of the user, and means for providing evacuation information according to the emotional state. This makes it possible to provide appropriate evacuation information according to the emotional state of the user.

[0733] "Specific areas" refers to areas that are subject to risk assessment and evacuation information provision in the event of a natural disaster.

[0734] "Natural disaster" refers to a disaster caused by natural phenomena such as earthquakes and typhoons.

[0735] "Means for assessing risk" refers to methods and devices for assessing the risk level of a specific area based on information such as the scale, location, depth, time of occurrence, and time elapsed since the occurrence of a natural disaster.

[0736] "Means of providing evacuation facility information" refers to methods and devices for collecting and providing information such as the location, capacity, and facilities of evacuation facilities to users.

[0737] "Means of guiding evacuation routes" refers to methods and devices that propose the optimal evacuation route based on information such as current location, evacuation facility information, road conditions, and traffic conditions.

[0738] "Means for recognizing the emotional state of a user" refers to methods or devices for recognizing emotions from a user's voice, facial expressions, behavior, etc.

[0739] "Means of providing evacuation information according to emotional state" refers to methods or devices for providing appropriate evacuation information according to the recognized emotional state of the user.

[0740] In one embodiment of this invention, the server provides a system that assesses the risk level of a specific area when a natural disaster occurs and guides users to appropriate evacuation facilities and routes. The server uses seismometer data and weather information APIs to collect information such as the magnitude of the natural disaster, its location, depth, time of occurrence, and the time elapsed since the disaster. This allows for real-time assessment of the risk level.

[0741] The server utilizes local government databases and open data to collect information such as the location, capacity, and facilities of evacuation centers, and provides this information to users. Furthermore, it uses the Google Maps API and traffic information API to suggest the optimal evacuation route based on current location information, evacuation center information, road conditions, and traffic conditions.

[0742] The device uses voice recognition software and facial recognition technology with a camera to recognize emotions from the user's voice, facial expressions, and actions. This makes it possible to provide appropriate evacuation information according to the user's emotional state.

[0743] As a specific example, if the user is using a smartphone, the device will automatically acquire earthquake information when an earthquake occurs and display the level of danger on the screen. Next, it will display the nearest evacuation facility and its route on a map and begin voice guidance. If the device detects that the user is in a state of panic, it will voice a message such as, "Please stay calm. The nearest evacuation facility is XX." If the device detects that the user is calm, it will present multiple evacuation routes and prompt the user to choose, "Which route would you like to take?"

[0744] An example of a prompt to input into a generating AI model would be, "Please suggest evacuation facilities and routes in the event of a magnitude 7 earthquake in Tokyo." Based on this prompt, the AI ​​model will generate appropriate evacuation information.

[0745] The flow of the specific processing in Example 1 will be explained using Figure 15.

[0746] Step 1:

[0747] The server uses seismometer data and weather information APIs to collect information such as the magnitude, location, depth, time of occurrence, and time elapsed since the occurrence of a natural disaster. It receives seismometer data and information from APIs as input, analyzes this data, and generates basic data for risk assessment. Specifically, the server periodically calls APIs to obtain the latest earthquake information.

[0748] Step 2:

[0749] The server evaluates the risk level of a specific area based on the collected earthquake information. Using the basic data generated in Step 1 as input, it applies a risk assessment algorithm to output a risk score. Specifically, the server calculates the risk score considering factors such as the magnitude of the earthquake and the distance to the epicenter.

[0750] Step 3:

[0751] The server collects information such as the location, capacity, and facilities of evacuation facilities by utilizing local government databases and open data. It receives evacuation facility data provided by local governments as input, analyzes it, and outputs evacuation facility information for users. Specifically, the server periodically updates the database to maintain the latest evacuation facility information.

[0752] Step 4:

[0753] The server proposes the optimal evacuation route based on current location information, evacuation facility information, road conditions, and traffic conditions. Using the user's current location information and the evacuation facility information obtained in step 3 as input, it calculates the optimal route using the Google Maps API and traffic information API, and provides the evacuation route as output. Specifically, the server obtains the user's current location and calculates the route to the nearest evacuation facility.

[0754] Step 5:

[0755] The device recognizes emotions from the user's voice, facial expressions, and actions. It uses audio and video data acquired from the device's microphone and camera as input, and applies an emotion recognition algorithm to output the user's emotional state. Specifically, the device analyzes the user's emotions in real time using voice recognition software and facial recognition technology.

[0756] Step 6:

[0757] The device provides appropriate evacuation information based on the recognized user's emotional state. Using the emotional state obtained in step 5 as input, it generates and outputs messages and evacuation information tailored to that emotion. Specifically, the device sends calming messages to panicked users and presents multiple options to calm users.

[0758] (Application Example 1)

[0759] Next, we will describe Application Example 1 of Form Example 1. 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."

[0760] During earthquake disasters, it is essential to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. Furthermore, providing appropriate evacuation information tailored to the emotional state of users is crucial to preventing panic and confusion and supporting safe evacuation. In addition, autonomous vehicles are required to select and guide passengers along the optimal evacuation route based on their emotions.

[0761] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0762] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding appropriate evacuation routes, means for recognizing the user's emotions and providing evacuation information corresponding to those emotions, and means for selecting and guiding passengers on evacuation routes based on their emotions in an autonomous vehicle. This enables rapid and appropriate evacuation support during earthquake disasters and ensures the safety of users.

[0763] "Specific areas" refers to areas that are subject to risk assessment and evacuation information provision during earthquake disasters, and specifically includes areas such as Tokyo, Kanagawa, Chiba, and Saitama prefectures.

[0764] "Means for assessing the risk of earthquake disasters" refers to methods and devices for assessing the risk level of a specific area based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[0765] "Means of providing evacuation shelter information" refers to methods or devices for providing information such as the location, capacity, and facilities of evacuation shelters in a specific area.

[0766] "Means of guiding appropriate evacuation routes" refers to methods and devices that propose and guide users to the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[0767] "Means of recognizing users' emotions and providing evacuation information in accordance with those emotions" refers to methods or devices for recognizing users' emotions from their voice, facial expressions, and actions, and providing appropriate evacuation information in accordance with those emotions.

[0768] "Means for selecting and guiding passengers on evacuation routes based on their emotions in autonomous vehicles" refers to methods or devices for recognizing passengers' emotions within an autonomous vehicle and selecting and guiding them on the optimal evacuation route based on those emotions.

[0769] The system for carrying out this invention includes a server, a terminal, and an autonomous vehicle. The server runs a program to assess the risk of earthquake disasters in a specific area, provide information on evacuation shelters, and guide appropriate evacuation routes. The server collects information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of the earthquake, and the time elapsed since the earthquake, and assesses the risk based on this data.

[0770] The device senses the user's voice, facial expressions, and actions, and executes a program to recognize emotions. Emotion recognition uses speech recognition software and image analysis software. Specifically, it uses a "Speech Recognition API" for speech recognition and an "Image Analysis API" for image analysis.

[0771] The autonomous vehicle runs a program to select and guide passengers along the optimal evacuation route based on their emotions. It uses in-vehicle cameras and microphones to analyze passengers' emotions in real time and provide evacuation information tailored to those emotions. A "map API" is used for route selection, and a "generative AI model" is used for generating emotion-based messages.

[0772] As a concrete example, during an earthquake, cameras inside the autonomous vehicle capture passengers' facial expressions and microphones collect their voices. This data is analyzed using an "image analysis API" and a "speech recognition API," and if it is determined that the passengers are in a state of panic, a "generative AI model" generates a message such as, "Please stay calm. We will guide you to the safest evacuation route."

[0773] An example of a prompt message is, "The passenger is in a panic. Generate a calming message."

[0774] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[0775] Step 1:

[0776] The server collects information such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake. This information is obtained from earthquake observation agencies and meteorological databases. The input is earthquake-related data, and the output is basic data for risk assessment. The server analyzes this data to assess the risk level of a specific area.

[0777] Step 2:

[0778] The device uses a camera and microphone to collect data in order to sense the user's voice, facial expressions, and actions. The input is the user's voice and image data, and the output is the emotion recognition result. The device uses a "Voice Recognition API" and an "Image Analysis API" to analyze this data and recognize the user's emotions.

[0779] Step 3:

[0780] The server selects appropriate evacuation shelter information and evacuation routes based on the risk assessment results and the user's sentiment recognition results. The inputs are the risk assessment results and sentiment recognition results, and the outputs are evacuation shelter information and evacuation route information. The server uses the "Map API" to calculate the optimal evacuation route.

[0781] Step 4:

[0782] The autonomous vehicle guides passengers along evacuation routes based on evacuation route information received from a server. The input is evacuation route information, and the output is guidance messages for passengers. The vehicle uses a "generative AI model" to generate messages that respond to the passengers' emotions and provides guidance via voice or display.

[0783] Step 5:

[0784] The user begins a safe evacuation based on evacuation information provided by the vehicle. The input is guidance messages from the vehicle, and the output is the user's evacuation actions. The user selects the optimal evacuation route and carries out the evacuation according to the provided information.

[0785] (Example 2)

[0786] Next, we will describe Example 2 of the morphological example. 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."

[0787] In the event of a natural disaster, it is essential to quickly and accurately assess the level of risk in specific areas and provide appropriate evacuation facilities and routes. Furthermore, there is a lack of information provision tailored to the emotional state of users, necessitating measures to prevent panic and confusion.

[0788] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0789] In this invention, the server includes means for evaluating the risk of natural disasters in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, and means for recognizing the user's emotions and providing evacuation information that corresponds to those emotions. This enables rapid and accurate risk assessment in the event of a natural disaster and the provision of appropriate evacuation information that corresponds to the user's emotions.

[0790] "Specific areas" refers to areas that may be affected by natural disasters, and specifically includes administrative divisions and geographical boundaries.

[0791] "Natural disasters" refer to disasters caused by natural phenomena such as earthquakes, typhoons, and floods.

[0792] "Means for assessing risk" refers to methods and devices that use information about natural disasters to indicate the degree of their impact using numerical values ​​or indicators.

[0793] "Means of providing information on evacuation facilities" refers to methods and devices for providing users with information such as the location, capacity, and equipment status of evacuation shelters and facilities.

[0794] "Means of guiding evacuation routes" refers to methods and devices that present the optimal route for users to safely reach evacuation facilities.

[0795] "Means of recognizing emotions" refers to methods and devices for determining a user's emotional state from their voice, facial expressions, behavior, etc.

[0796] "Means of providing evacuation information in accordance with emotions" refers to methods and devices for providing appropriate evacuation information and messages according to the emotional state of the user.

[0797] This invention is a system aimed at rapid and accurate risk assessment during natural disasters and providing appropriate evacuation information tailored to the user's emotions. Specific embodiments of this system are described below.

[0798] The server collects information on natural disasters and assesses the risk level of specific areas. This involves using APIs from the Japan Meteorological Agency and earthquake research institutions to obtain data such as earthquake magnitude, location, depth, time of occurrence, and time elapsed since the event. Using this data, the server employs machine learning models based on historical disaster data to quantify the risk level.

[0799] The terminal receives risk information transmitted from the server and notifies the user. The terminal also uses GPS to obtain the user's current location and calculates the optimal evacuation route in real time based on evacuation facility information provided by the server. This enables the user to evacuate safely and quickly.

[0800] Furthermore, the device detects the user's voice, facial expressions, and actions through its camera and microphone, and recognizes their emotions. If it determines that the user is in a state of panic, the device displays a message such as, "Please stay calm. We will guide you to a safe evacuation route." If the user is calm, it presents multiple evacuation routes and prompts them to choose one.

[0801] As a concrete example, by inputting the prompt message "Please tell me how to assess the risk level and provide evacuation information during an earthquake" into an AI model during an earthquake, the AI ​​model will generate appropriate response methods for earthquake disasters. This prompt message allows the system to quickly present countermeasures and ensure the safety of users.

[0802] The flow of the specific processing in Example 2 will be explained using Figure 17.

[0803] Step 1:

[0804] The server collects information about natural disasters. As input, it obtains data such as earthquake magnitude, location, depth, time of occurrence, and time elapsed since the earthquake from APIs of the Japan Meteorological Agency and earthquake research institutions. Based on this data, the server processes the data to assess the level of risk and converts it into a format suitable for input into the assessment model. As output, it generates data ready for input into the assessment model.

[0805] Step 2:

[0806] The server uses the collected data and a machine learning model based on past disaster data to assess the risk level of a specific area. The data generated in Step 1 is used as input. The server performs data calculations using the machine learning model to quantify the risk level for each area. The server generates the risk assessment results for each area as output and sends them to the terminal.

[0807] Step 3:

[0808] The terminal receives the risk assessment results sent from the server and notifies the user. It receives the risk assessment results from the server as input. The terminal displays the assessment results on the screen and also provides audio notification. It notifies the user of the risk level as output.

[0809] Step 4:

[0810] The device uses GPS functionality to obtain the user's current location. The device's location services are used as input. The device obtains the current location information and sends it to the server. The current location information is provided to the server as output.

[0811] Step 5:

[0812] The server calculates the optimal evacuation route based on the user's current location and information on evacuation facilities. The inputs used are the user's current location from the terminal and pre-collected information on evacuation facilities. The server also considers road and traffic conditions, performing data calculations to determine the optimal route. The output is the generated information on the optimal evacuation route, which is then sent to the terminal.

[0813] Step 6:

[0814] The terminal receives evacuation route information transmitted from the server and guides the user. It receives evacuation route information from the server as input. The terminal presents the user with specific evacuation routes through screen displays and voice guidance. It provides evacuation route guidance to the user as output.

[0815] Step 7:

[0816] The device senses the user's voice, facial expressions, and actions through its camera and microphone, and recognizes their emotions. It uses data from its sensors as input. The device performs data processing using an emotion recognition algorithm to determine the user's emotional state. As output, it evaluates the user's emotional state and prepares to provide appropriate evacuation information.

[0817] Step 8:

[0818] The device provides appropriate evacuation information based on the user's emotional state. The emotional state assessed in step 7 is used as input. The device displays calming messages to panicked users and presents multiple evacuation routes to calm users. The output provides evacuation information tailored to the user's emotional state.

[0819] (Application Example 2)

[0820] Next, we will describe application example 2 of form example 2. 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."

[0821] During earthquake disasters, it is essential to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. However, conventional systems have a challenge in providing evacuation information that takes into account the emotional state of users, making it difficult to provide appropriate information to users in a panicked state.

[0822] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0823] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding users to appropriate evacuation routes, and means for recognizing the user's emotions and providing evacuation information corresponding to those emotions. This makes it possible to provide appropriate evacuation information according to the user's emotional state.

[0824] "Specific areas" refer to areas that are subject to risk assessment and evacuation information provision during earthquake disasters.

[0825] "Methods for assessing the risk of earthquake disasters" are methods for assessing the risk to a specific area based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[0826] "Means of providing information on evacuation shelters" refers to means of providing information such as the location, capacity, and facilities of evacuation shelters in a specific area.

[0827] "Means of guiding people to appropriate evacuation routes" refers to methods for suggesting the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[0828] "Means of recognizing users' emotions and providing evacuation information in accordance with those emotions" refers to means of recognizing users' emotions from their voice, facial expressions, and actions, and providing appropriate evacuation information in accordance with those emotions.

[0829] The system for carrying out this invention includes a server and a terminal. The server runs a program to assess the risk level of a specific area during an earthquake disaster and provide information on evacuation shelters and appropriate evacuation routes. Specifically, the server obtains earthquake information using the Japan Meteorological Agency API and analyzes data such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. This allows the server to assess the risk level of a specific area.

[0830] The server also retrieves evacuation shelter information from local government databases and calculates the optimal evacuation route using the Google Maps API. Furthermore, the terminal acquires the user's voice and facial expressions using the smartphone's microphone and camera, and analyzes them with OpenAI's emotion recognition model. This allows the system to recognize the user's emotional state and provide evacuation information tailored to that emotion.

[0831] As a concrete example, when an earthquake occurs, the server sends information to the terminal such as, "An earthquake with a seismic intensity of 6 has occurred. The nearest evacuation center is XX Park. Please evacuate using this route." If the user is in a panic state, the terminal displays a message such as, "Please stay calm. We will guide you to a safe evacuation center."

[0832] An example of a prompt message for a generative AI model is, "Recognize the user's emotions from their voice and facial expressions, and generate appropriate evacuation information."

[0833] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[0834] Step 1:

[0835] The server retrieves earthquake information from the Japan Meteorological Agency API. It uses the timestamp of the earthquake as input. The output includes data such as earthquake magnitude, epicenter, depth, and time of occurrence. This data is then processed to assess the risk level for specific regions.

[0836] Step 2:

[0837] The server retrieves evacuation shelter information from local government databases. It uses a specific regional identifier as input. The output includes information such as the location, capacity, and facilities of the evacuation shelters. Based on this information, the server selects the appropriate evacuation shelter.

[0838] Step 3:

[0839] The server uses the Google Maps API to calculate the optimal evacuation route. It uses the user's current location and evacuation shelter information as input. The output is the optimal evacuation route. Based on this route information, the server processes the data to guide the user.

[0840] Step 4:

[0841] The device uses the smartphone's microphone and camera to capture the user's voice and facial expressions. Real-time audio and video data of the user is used as input. Audio and facial expression data are obtained as output. This data is then processed for emotion recognition.

[0842] Step 5:

[0843] The device analyzes the user's emotions using OpenAI's emotion recognition model. It uses voice and facial expression data as input. The output is the user's emotional state. Based on this emotional information, it performs data calculations to generate appropriate evacuation information.

[0844] Step 6:

[0845] The server generates evacuation information based on the user's emotional state and sends it to the terminal. It uses emotional information and evacuation route information as input. The output is evacuation information to be displayed to the user. Specifically, if the user is in a panic state, it generates a message such as, "Please stay calm. We will guide you to a safe evacuation center."

[0846] (Other examples)

[0847] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0848] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0849] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0850] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are examples.

[0851] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0852] [Fourth Embodiment]

[0853] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0854] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0855] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0856] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0857] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0858] Camera 42 includes an optical system such as a lens, aperture, and shutter, and a CMOS (Complementary This is a small digital camera equipped with an image sensor such as a metal-oxide-semiconductor (METRO) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0859] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0860] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0861] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0862] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0863] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0864] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0865] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0866] "Example of form 1"

[0867] The disaster prevention AI system of this invention evaluates the level of risk in a specific area (Tokyo, Kanagawa, Chiba, and Saitama prefectures) when an earthquake occurs, and guides users to appropriate evacuation shelters and evacuation routes. The means for evaluating the level of risk is based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. For example, the greater the magnitude of the earthquake, the closer the epicenter, the shallower the earthquake, and the longer the time elapsed since the earthquake, the higher the level of risk is evaluated. The means for providing evacuation shelter information provides information such as the location, capacity, and facilities of evacuation shelters based on information provided by public databases and local governments. The means for guiding users to appropriate evacuation routes proposes the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0868] "Example of form 2"

[0869] The disaster prevention AI system of this invention evaluates the level of risk in a specific area (Tokyo, Kanagawa, Chiba, and Saitama prefectures) when an earthquake occurs, and guides users to appropriate evacuation shelters and evacuation routes. The means for evaluating the level of risk is based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. For example, the greater the magnitude of the earthquake, the closer the epicenter, the shallower the earthquake, and the longer the time elapsed since the earthquake, the higher the level of risk is evaluated. The means for providing evacuation shelter information provides information such as the location, capacity, and facilities of evacuation shelters based on information provided by public databases and local governments. The means for guiding users to appropriate evacuation routes proposes the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0870] The following describes the processing flow for each example of the form.

[0871] "Example of form 1"

[0872] Step 1: When an earthquake occurs, the disaster prevention AI system collects information such as the magnitude of the earthquake, its epicenter, its depth, the time it occurred, and the time elapsed since the earthquake. This information is obtained from public earthquake information services and sensor networks.

[0873] Step 2: Based on the collected information, assess the risk level of specific areas (Tokyo, Kanagawa, Chiba, and Saitama prefectures). The assessment is based on an algorithm that assigns a higher risk level to earthquakes of larger magnitude, closer proximity to the epicenter, shallower depth, and longer time elapsed since the earthquake.

[0874] Step 3: Provide information on evacuation shelters. Based on information from public databases and local governments, provide information such as the location, capacity, and facilities of evacuation shelters.

[0875] Step 4: Based on information such as current location, evacuation shelter information, road conditions, and traffic conditions, the system proposes the optimal evacuation route. For example, it proposes routes to the nearest evacuation shelter from the current location, routes that take traffic conditions into consideration, and routes that take into account the passability of roads.

[0876] (Example 1)

[0877] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0878] During earthquake disasters, it is crucial to quickly and accurately assess the risk level of specific areas and provide appropriate shelters and evacuation routes. However, conventional systems have the challenge of handling earthquake information collection, risk assessment, shelter information acquisition, and evacuation route proposals individually, making integrated and efficient responses difficult.

[0879] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0880] In this invention, the server includes means for collecting earthquake information, means for evaluating the degree of risk based on the earthquake information, means for acquiring evacuation shelter information based on the evaluated degree of risk, means for proposing the optimal evacuation route based on the evacuation shelter information and current location information, and means for providing the proposed evacuation route to the user. This enables rapid and accurate risk assessment and provision of evacuation information during an earthquake disaster.

[0881] "Means for collecting earthquake information" refers to devices or methods for acquiring data such as the magnitude of an earthquake, its epicenter, its depth, and the time of its occurrence.

[0882] "Means for assessing risk" refers to a device or method for calculating the risk of earthquake disasters in a specific area based on collected earthquake information.

[0883] "Means for acquiring evacuation shelter information" refers to a device or method for acquiring information such as the location, capacity, and facilities of evacuation shelters based on the assessed risk level.

[0884] "Means for proposing the optimal evacuation route" refers to a device or method for calculating the most suitable evacuation route for a user, taking into account traffic and road conditions, based on evacuation shelter information and current location information.

[0885] "Means to provide to the user" refers to a device or method for communicating the proposed evacuation route to the user visually or audibly.

[0886] A description of embodiments for carrying out this invention will be given.

[0887] The server utilizes an API from an earthquake information service to collect earthquake information. This API can obtain data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time. Based on this data, the server uses its own algorithm to assess the risk level of a specific area. This assessment is designed to indicate a higher risk level when the earthquake is large, the epicenter is close, and the depth is shallow.

[0888] The device obtains evacuation shelter information using the local government's open data API, based on a risk assessment provided by the server. This information includes the location, capacity, and facilities of the evacuation shelters. The device also obtains the user's current location information and sends it to the server.

[0889] The server proposes the optimal evacuation route based on the user's current location and evacuation shelter information. This proposal uses a map information service API and takes into account traffic conditions and road passability. The server sends the calculated evacuation route to the terminal, which then provides the information to the user visually or audibly.

[0890] As a concrete example, let's assume the user is in Shibuya Ward, Tokyo. In this case, the user enters their current location into the device, and the server evaluates the risk level of Shibuya Ward based on earthquake information. Next, the device retrieves information on evacuation shelters within Shibuya Ward, and the server calculates the optimal evacuation route. Finally, the user receives information on evacuation shelters and routes from the device and can begin evacuating quickly.

[0891] An example of a prompt message would be: "I am currently in Shibuya Ward, Tokyo. An earthquake has occurred. The epicenter is in Kanagawa Prefecture, the magnitude is 7.0, and the depth is 10km. Please tell me the best evacuation shelter and evacuation route." Based on this prompt message, the server assesses the level of danger and provides the user with the most appropriate evacuation information.

[0892] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0893] Step 1:

[0894] The server uses an API from an earthquake information service to collect data such as earthquake magnitude, epicenter, depth, and time of occurrence. The input is earthquake information obtained from the API, and the output is a dataset containing this information. This dataset is used for subsequent risk assessments. Specifically, the server periodically calls the API to obtain the latest earthquake information.

[0895] Step 2:

[0896] The server evaluates the risk level of specific regions based on the collected earthquake information. The input is a dataset of earthquake information generated in Step 1, and the output is a risk score for each region. For data processing, an algorithm is used to calculate the risk level, taking into account factors such as earthquake magnitude, distance from the epicenter, and earthquake depth. Specifically, the server calculates the risk score for each region and stores it in a database.

[0897] Step 3:

[0898] The device obtains evacuation shelter information using the local government's open data API, based on a risk assessment provided by the server. Inputs include a risk score and the user's current location, while output includes information such as the location, capacity, and facilities of the evacuation shelter. The data calculation involves determining the evacuation shelter closest to the user's current location. Specifically, the device obtains the user's current location and selects the most suitable evacuation shelter.

[0899] Step 4:

[0900] The server proposes the optimal evacuation route based on the user's current location and evacuation shelter information. Inputs include the user's current location, evacuation shelter information, road conditions, and traffic conditions, and the output generates the optimal evacuation route. Data processing utilizes a map information service API to perform route calculations that consider traffic conditions and road passability. Specifically, the server evaluates multiple routes and selects the most efficient one.

[0901] Step 5:

[0902] The user receives evacuation shelter information and evacuation routes from the server via their device. The input is evacuation route information sent from the server, and the output is the information provided to the user visually or audibly. Specifically, the device guides the user along the evacuation route through map display and voice guidance.

[0903] (Application Example 1)

[0904] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0905] During earthquakes, there is a need to automate the selection of appropriate evacuation routes and the guidance of people to shelters in order to ensure a swift and safe evacuation. However, currently, real-time information gathering and the presentation of optimal evacuation routes during earthquakes are insufficient, and there is a particular lack of evacuation support using autonomous vehicles. This could lead to delays and confusion in evacuations.

[0906] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0907] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding appropriate evacuation routes, and means for selecting the optimal evacuation route in an autonomous vehicle during an earthquake and guiding passengers to an evacuation shelter. This enables rapid and safe evacuation during an earthquake.

[0908] "Specific areas" refers to areas that may be affected by earthquake disasters, specifically areas where earthquakes are predicted to occur.

[0909] "Means for evaluating the risk of earthquake disasters" refers to methods or devices for quantifying or ranking the risk of earthquake disasters based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[0910] "Means of providing evacuation shelter information" refers to methods and devices for collecting and providing information such as the location, capacity, and facilities of evacuation shelters to users.

[0911] "Means of guiding users to appropriate evacuation routes" refers to methods or devices that calculate and present the optimal evacuation route to users based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[0912] "Means for selecting the optimal evacuation route in an autonomous vehicle during an earthquake and guiding passengers to a shelter" refers to methods or devices that enable autonomous vehicles to collect information in real time during an earthquake, select the optimal evacuation route, and safely transport passengers to a shelter.

[0913] To implement this invention, a server collects data in real time using earthquake information APIs, map APIs, and traffic information APIs, and evaluates the risk level to earthquake disasters. Based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake, the server quantifies the risk level of a specific area.

[0914] The terminal receives evacuation shelter information from the server and presents the user with details such as the location, capacity, and facilities of the shelter. Furthermore, the terminal calculates the optimal evacuation route based on information such as the user's current location, evacuation shelter information, road conditions, and traffic conditions, and guides the user along that route.

[0915] Based on information from a server, the autonomous vehicle will select the optimal evacuation route during an earthquake and safely guide passengers to a shelter. This will enable rapid and safe evacuation during an earthquake.

[0916] As a concrete example, when an earthquake occurs, inputting the prompt message "An earthquake has occurred. Your current location is Shibuya Ward, Tokyo. Please tell me the best evacuation route." into the AI ​​model can provide the optimal evacuation route.

[0917] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0918] Step 1:

[0919] The server retrieves data such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake from the earthquake information API. Based on this data, it evaluates the risk of earthquake disaster in a specific area. The input is data from the earthquake information API, and the output is the risk evaluation result. The server analyzes this data and quantifies the risk level.

[0920] Step 2:

[0921] The server uses map APIs and traffic information APIs to collect information such as the location, capacity, facilities, road conditions, and traffic conditions of evacuation shelters. Input is data from the map APIs and traffic information APIs, and output is evacuation shelter information and traffic information. The server organizes this information to understand the details of the evacuation shelters.

[0922] Step 3:

[0923] The terminal receives evacuation shelter information and traffic information provided by the server and presents it to the user. The input is evacuation shelter information and traffic information from the server, and the output is the information presented to the user. The terminal displays this information on the screen so that the user can confirm it.

[0924] Step 4:

[0925] The terminal acquires current location information and calculates the optimal evacuation route based on information from the server. Inputs include current location information, evacuation shelter information, and traffic information, while output is the optimal evacuation route. The terminal uses this information to calculate the route and guide the user.

[0926] Step 5:

[0927] The autonomous vehicle safely guides passengers to the evacuation shelter based on the optimal evacuation route provided by the terminal. The input is the optimal evacuation route, and the output is the passengers' arrival at the evacuation shelter. The autonomous vehicle drives according to the route and transports the passengers to their destination.

[0928] (Example 2)

[0929] Next, we will describe Example 2 of the Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0930] During earthquake disasters, it is crucial to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. However, conventional systems struggle to collect information in real time and propose optimal evacuation routes, hindering users from taking swift evacuation action.

[0931] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0932] In this invention, the server includes means for collecting earthquake information, means for evaluating the degree of risk based on the collected information, means for acquiring information such as the location, capacity, and facilities of evacuation shelters, means for proposing the optimal evacuation route based on the current location information, and means for providing information to the user. This makes it possible to quickly and accurately evaluate the degree of risk in a specific area during an earthquake disaster and to provide appropriate evacuation shelters and evacuation routes in real time.

[0933] "Means for collecting earthquake information" refers to functions for acquiring data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time.

[0934] A "means for assessing risk" refers to a function that calculates the risk of earthquake disasters in a specific area based on collected earthquake information.

[0935] "Means for obtaining information such as the location, capacity, and facilities of evacuation shelters" refers to a function for obtaining detailed information about evacuation shelters based on public databases and information from local governments.

[0936] "A means of proposing the optimal evacuation route based on current location information" refers to a function that takes the user's current location into consideration and calculates and proposes the optimal evacuation route based on traffic conditions and road passability.

[0937] "Means of providing information to users" refers to functions that visually or audibly communicate risk assessments, shelter information, and suggested evacuation routes to users.

[0938] This disaster prevention AI system is designed to assess the risk level of specific areas during earthquakes and guide people to appropriate evacuation shelters and routes. The server uses an API from an earthquake information service to collect earthquake information. Specifically, it obtains data such as earthquake magnitude, epicenter, depth, and time of occurrence in real time.

[0939] The server uses a specific algorithm to assess the level of risk based on the collected earthquake information. This algorithm is designed so that the risk level increases with the magnitude of the earthquake, the proximity of the epicenter, and the shallow depth of the earthquake.

[0940] The terminal retrieves information such as the location, capacity, and facilities of evacuation shelters from public databases and local government disaster prevention information systems. This allows users to view detailed information about evacuation shelters.

[0941] The server uses the Google Maps API to calculate the optimal evacuation route based on the user's current location. It considers traffic conditions and road passability to suggest the safest and fastest route.

[0942] Users receive risk assessments, shelter information, and suggested evacuation routes through their devices. The devices display information using a visually easy-to-understand interface and provide voice guidance to navigate the routes.

[0943] As a concrete example, when a user enters "I am in Shibuya Ward, Tokyo," the server immediately collects earthquake information and assesses the level of risk. Next, the device retrieves information on evacuation shelters within Shibuya Ward, and the server uses the Google Maps API to calculate the optimal evacuation route. Finally, the user confirms the evacuation route on the device screen and begins evacuating according to the voice guidance.

[0944] Examples of prompts to input into a generative AI model:

[0945] "I am currently in Shibuya Ward, Tokyo. Please assess the risk level in the event of an earthquake and tell me the best evacuation shelters and routes."

[0946] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0947] Step 1:

[0948] The server calls the API of an earthquake information service to obtain data such as earthquake magnitude, epicenter, depth, and time of occurrence. The input is earthquake information from the API, and the output is a dataset containing this information. The server uses this dataset to prepare for the risk assessment in the next step.

[0949] Step 2:

[0950] The server evaluates the risk level based on the earthquake information obtained in Step 1. The input is a dataset of earthquake information, and the output is a risk score for a specific region. The server applies an algorithm that increases the risk level as the earthquake magnitude increases, the epicenter is closer, and the earthquake depth decreases.

[0951] Step 3:

[0952] The terminal accesses public databases and local government disaster prevention information systems to obtain information such as the location, capacity, and facilities of evacuation shelters. The input is the user's current location, and the output is information about evacuation shelters in that area. The terminal displays this information in a format that is easy for the user to understand.

[0953] Step 4:

[0954] The server uses the Google Maps API to calculate the optimal evacuation route based on the user's current location. The input is the user's current location and evacuation shelter information, and the output is the optimal evacuation route. The server considers traffic conditions and road passability to propose the safest and fastest route.

[0955] Step 5:

[0956] The user receives risk assessments, shelter information, and suggested evacuation routes through the terminal. Input is information from the server, and output is visual and audio guidance to the user. The terminal displays evacuation routes on a map and provides voice guidance to navigate the route.

[0957] (Application Example 2)

[0958] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0959] During earthquake disasters, there is a need to quickly and accurately assess the level of risk in specific areas and guide users to the most suitable evacuation shelters and routes in real time. However, conventional systems have shortcomings in suggesting evacuation routes that do not take into account the user's current location, and real-time risk assessment is difficult.

[0960] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0961] In this invention, the server includes means for evaluating the degree of disaster risk in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, means for acquiring the user's location information, and means for evaluating the degree of risk in real time and guiding users to the optimal evacuation shelter and evacuation route. This enables quick and accurate guidance to evacuation shelters and evacuation routes based on the user's current location.

[0962] "Specific areas" refers to the geographical area that is subject to risk assessment and evacuation guidance during a disaster.

[0963] "Means for assessing the degree of risk to disaster" refers to a device or program that has the function of calculating the degree of disaster risk in a specific area based on information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the passage of time.

[0964] "Means of providing evacuation facility information" refers to a device or program for providing users with information such as the location, capacity, and facilities of evacuation shelters.

[0965] "Means of guiding users to appropriate evacuation routes" refers to a device or program that has the function of suggesting the optimal evacuation route, taking into account the user's current location information, road conditions, and traffic conditions.

[0966] "Means for obtaining user location information" refers to a device or program that uses technologies such as GPS to obtain the user's current geographical location.

[0967] "A means of assessing the level of risk in real time and guiding users to the optimal shelter and evacuation route" refers to a device or program that has the function of immediately assessing the level of risk in the event of a disaster and providing users with the optimal shelter and evacuation route.

[0968] To implement this invention, a server, a user terminal, and an associated database are required. The server acquires earthquake information and assesses the degree of disaster risk in a specific area. This uses data such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed. Based on this data, the server calculates the degree of risk relevant to the user's current location in real time.

[0969] The user's device obtains its current location using GPS functionality. The device receives evacuation facility information from the server and guides the user to the most suitable evacuation shelter and evacuation route. Road conditions and traffic conditions are also taken into consideration when guiding the evacuation route.

[0970] Specifically, the server utilizes an external earthquake data API to obtain earthquake information. The user's device obtains location information using a GPS module and sends it to the server. The server combines the received location information with earthquake data to assess the level of risk and calculate the optimal evacuation shelter and route. This enables the user to take quick and accurate evacuation action.

[0971] For example, if a user is in Tokyo, the server will immediately acquire earthquake information when an earthquake occurs and guide the user to the optimal evacuation shelter and route based on their current location. It is also possible to provide guidance to the user in natural language using a generative AI model.

[0972] An example of a prompt message could be: "An earthquake has occurred in Tokyo. Your current location is 35.6895, 139.6917. Please tell me the best evacuation shelter and evacuation route." By using this prompt message, the AI ​​model can provide the user with appropriate evacuation information.

[0973] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0974] Step 1:

[0975] The server obtains the latest earthquake information from an external earthquake data API. It receives earthquake data from the API as input and extracts information such as earthquake magnitude, epicenter, depth, and time of occurrence. Based on this data, it generates basic data for assessing the risk level in specific regions.

[0976] Step 2:

[0977] The user's device obtains current location information using its GPS function. It receives location information from the GPS module as input and sends latitude and longitude data to the server. This location information serves as basic data for guiding the user to the most suitable evacuation shelter and evacuation route.

[0978] Step 3:

[0979] The server combines user location information and earthquake data to assess the risk level in a specific area in real time. It receives user location information and earthquake data as input and applies an algorithm to calculate the risk level. As output, it generates a risk level related to the user's current location.

[0980] Step 4:

[0981] The server searches the evacuation facility information database for the most suitable evacuation shelter for the user's current location. It receives the user's location and risk level as input, and selects the optimal shelter considering information such as location, capacity, and facilities. The server then generates information about the optimal shelter as output.

[0982] Step 5:

[0983] The server calculates the optimal evacuation route for the user, taking into account road and traffic conditions. It receives the user's location information, evacuation shelter information, and road condition data as input, and applies a route calculation algorithm. As output, it generates the optimal evacuation route to guide the user.

[0984] Step 6:

[0985] The user's device displays evacuation shelter information and evacuation routes received from the server. It receives evacuation shelter and route information from the server as input and displays it to the user in a visually easy-to-understand format. This enables users to take swift and safe evacuation actions.

[0986] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0987] "Example of form 1"

[0988] One embodiment of the present invention provides a disaster prevention AI system that incorporates an emotion engine. This system assesses the risk of earthquake disasters, provides appropriate shelter information, and guides users along appropriate evacuation routes. Furthermore, it recognizes the user's emotions and provides appropriate evacuation information according to those emotions. Specifically, it recognizes emotions from the user's voice, facial expressions, and actions, and provides appropriate evacuation information according to those emotions. For example, if the user is panicking, it provides evacuation information accompanied by a message to calm them down. Also, if the user is confused, it provides more specific and concise evacuation information. In addition, it adjusts the method of guiding users along evacuation routes according to the user's emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them along the safest and fastest evacuation route.

[0989] "Example of form 2"

[0990] One embodiment of the present invention provides a disaster prevention AI system that incorporates an emotion engine. This system assesses the risk of earthquake disasters, provides appropriate shelter information, and guides users along appropriate evacuation routes. Furthermore, it recognizes the user's emotions and provides appropriate evacuation information according to those emotions. Specifically, it recognizes emotions from the user's voice, facial expressions, and actions, and provides appropriate evacuation information according to those emotions. For example, if the user is panicking, it provides evacuation information accompanied by a message to calm them down. Also, if the user is confused, it provides more specific and concise evacuation information. In addition, it adjusts the method of guiding users along evacuation routes according to the user's emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them along the safest and fastest evacuation route.

[0991] The following describes the processing flow for each example of the form.

[0992] "Example of form 1"

[0993] Step 1: When an earthquake occurs, the disaster prevention AI system determines the magnitude and epicenter of the earthquake.

[0994] Step 2: The disaster prevention AI system assesses the risk level for each region in Tokyo, Kanagawa, Chiba, and Saitama prefectures based on the magnitude and epicenter of the earthquake.

[0995] Step 3: The disaster prevention AI system provides appropriate evacuation shelter information and evacuation routes for each area based on the assessed risk level.

[0996] Step 4: The emotion engine recognizes emotions from the user's voice, facial expressions, and actions.

[0997] Step 5: The emotion engine provides appropriate evacuation information based on the recognized emotion. For example, if the user is panicking, it provides evacuation information accompanied by a calming message. If the user is confused, it provides more specific and concise evacuation information.

[0998] Step 6: The emotion engine adjusts how it guides users through evacuation routes according to their emotional state. For example, if the user is calm, it suggests multiple evacuation routes and allows the user to choose. On the other hand, if the user is panicking, it emphasizes and guides them through the safest and quickest evacuation route.

[0999] (Example 1)

[1000] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1001] When a natural disaster occurs, it is necessary to quickly and accurately assess the level of danger and guide people to appropriate evacuation facilities and routes. However, conventional systems have the problem of not adequately providing evacuation information that takes into account the emotional state of users, and are unable to provide appropriate support to users who are in a panic state.

[1002] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1003] In this invention, the server includes means for evaluating the degree of risk to natural disasters in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, means for recognizing the emotional state of the user, and means for providing evacuation information according to the emotional state. This makes it possible to provide appropriate evacuation information according to the emotional state of the user.

[1004] "Specific areas" refers to areas that are subject to risk assessment and evacuation information provision in the event of a natural disaster.

[1005] "Natural disaster" refers to a disaster caused by natural phenomena such as earthquakes and typhoons.

[1006] "Means for assessing risk" refers to methods and devices for assessing the risk level of a specific area based on information such as the scale, location, depth, time of occurrence, and time elapsed since the occurrence of a natural disaster.

[1007] "Means of providing evacuation facility information" refers to methods and devices for collecting and providing information such as the location, capacity, and facilities of evacuation facilities to users.

[1008] "Means of guiding evacuation routes" refers to methods and devices that propose the optimal evacuation route based on information such as current location, evacuation facility information, road conditions, and traffic conditions.

[1009] "Means for recognizing the emotional state of a user" refers to methods or devices for recognizing emotions from a user's voice, facial expressions, behavior, etc.

[1010] "Means of providing evacuation information according to emotional state" refers to methods or devices for providing appropriate evacuation information according to the recognized emotional state of the user.

[1011] In one embodiment of this invention, the server provides a system that assesses the risk level of a specific area when a natural disaster occurs and guides users to appropriate evacuation facilities and routes. The server uses seismometer data and weather information APIs to collect information such as the magnitude of the natural disaster, its location, depth, time of occurrence, and the time elapsed since the disaster. This allows for real-time assessment of the risk level.

[1012] The server utilizes local government databases and open data to collect information such as the location, capacity, and facilities of evacuation centers, and provides this information to users. Furthermore, it uses the Google Maps API and traffic information API to suggest the optimal evacuation route based on current location information, evacuation center information, road conditions, and traffic conditions.

[1013] The device uses voice recognition software and facial recognition technology with a camera to recognize emotions from the user's voice, facial expressions, and actions. This makes it possible to provide appropriate evacuation information according to the user's emotional state.

[1014] As a specific example, if the user is using a smartphone, the device will automatically acquire earthquake information when an earthquake occurs and display the level of danger on the screen. Next, it will display the nearest evacuation facility and its route on a map and begin voice guidance. If the device detects that the user is in a state of panic, it will voice a message such as, "Please stay calm. The nearest evacuation facility is XX." If the device detects that the user is calm, it will present multiple evacuation routes and prompt the user to choose, "Which route would you like to take?"

[1015] An example of a prompt to input into a generating AI model would be, "Please suggest evacuation facilities and routes in the event of a magnitude 7 earthquake in Tokyo." Based on this prompt, the AI ​​model will generate appropriate evacuation information.

[1016] The flow of the specific processing in Example 1 will be explained using Figure 15.

[1017] Step 1:

[1018] The server uses seismometer data and weather information APIs to collect information such as the magnitude, location, depth, time of occurrence, and time elapsed since the occurrence of a natural disaster. It receives seismometer data and information from APIs as input, analyzes this data, and generates basic data for risk assessment. Specifically, the server periodically calls APIs to obtain the latest earthquake information.

[1019] Step 2:

[1020] The server evaluates the risk level of a specific area based on the collected earthquake information. Using the basic data generated in Step 1 as input, it applies a risk assessment algorithm to output a risk score. Specifically, the server calculates the risk score considering factors such as the magnitude of the earthquake and the distance to the epicenter.

[1021] Step 3:

[1022] The server collects information such as the location, capacity, and facilities of evacuation facilities by utilizing local government databases and open data. It receives evacuation facility data provided by local governments as input, analyzes it, and outputs evacuation facility information for users. Specifically, the server periodically updates the database to maintain the latest evacuation facility information.

[1023] Step 4:

[1024] The server proposes the optimal evacuation route based on current location information, evacuation facility information, road conditions, and traffic conditions. Using the user's current location information and the evacuation facility information obtained in step 3 as input, it calculates the optimal route using the Google Maps API and traffic information API, and provides the evacuation route as output. Specifically, the server obtains the user's current location and calculates the route to the nearest evacuation facility.

[1025] Step 5:

[1026] The device recognizes emotions from the user's voice, facial expressions, and actions. It uses audio and video data acquired from the device's microphone and camera as input, and applies an emotion recognition algorithm to output the user's emotional state. Specifically, the device analyzes the user's emotions in real time using voice recognition software and facial recognition technology.

[1027] Step 6:

[1028] The device provides appropriate evacuation information based on the recognized user's emotional state. Using the emotional state obtained in step 5 as input, it generates and outputs messages and evacuation information tailored to that emotion. Specifically, the device sends calming messages to panicked users and presents multiple options to calm users.

[1029] (Application Example 1)

[1030] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1031] During earthquake disasters, it is essential to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. Furthermore, providing appropriate evacuation information tailored to the emotional state of users is crucial to preventing panic and confusion and supporting safe evacuation. In addition, autonomous vehicles are required to select and guide passengers along the optimal evacuation route based on their emotions.

[1032] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1033] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding appropriate evacuation routes, means for recognizing the user's emotions and providing evacuation information corresponding to those emotions, and means for selecting and guiding passengers on evacuation routes based on their emotions in an autonomous vehicle. This enables rapid and appropriate evacuation support during earthquake disasters and ensures the safety of users.

[1034] "Specific areas" refers to areas that are subject to risk assessment and evacuation information provision during earthquake disasters, and specifically includes areas such as Tokyo, Kanagawa, Chiba, and Saitama prefectures.

[1035] "Means for assessing the risk of earthquake disasters" refers to methods and devices for assessing the risk level of a specific area based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[1036] "Means of providing evacuation shelter information" refers to methods or devices for providing information such as the location, capacity, and facilities of evacuation shelters in a specific area.

[1037] "Means of guiding appropriate evacuation routes" refers to methods and devices that propose and guide users to the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[1038] "Means of recognizing users' emotions and providing evacuation information in accordance with those emotions" refers to methods or devices for recognizing users' emotions from their voice, facial expressions, and actions, and providing appropriate evacuation information in accordance with those emotions.

[1039] "Means for selecting and guiding passengers on evacuation routes based on their emotions in autonomous vehicles" refers to methods or devices for recognizing passengers' emotions within an autonomous vehicle and selecting and guiding them on the optimal evacuation route based on those emotions.

[1040] The system for carrying out this invention includes a server, a terminal, and an autonomous vehicle. The server runs a program to assess the risk of earthquake disasters in a specific area, provide information on evacuation shelters, and guide appropriate evacuation routes. The server collects information such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of the earthquake, and the time elapsed since the earthquake, and assesses the risk based on this data.

[1041] The device senses the user's voice, facial expressions, and actions, and executes a program to recognize emotions. Emotion recognition uses speech recognition software and image analysis software. Specifically, it uses a "Speech Recognition API" for speech recognition and an "Image Analysis API" for image analysis.

[1042] The autonomous vehicle runs a program to select and guide passengers along the optimal evacuation route based on their emotions. It uses in-vehicle cameras and microphones to analyze passengers' emotions in real time and provide evacuation information tailored to those emotions. A "map API" is used for route selection, and a "generative AI model" is used for generating emotion-based messages.

[1043] As a concrete example, during an earthquake, cameras inside the autonomous vehicle capture passengers' facial expressions and microphones collect their voices. This data is analyzed using an "image analysis API" and a "speech recognition API," and if it is determined that the passengers are in a state of panic, a "generative AI model" generates a message such as, "Please stay calm. We will guide you to the safest evacuation route."

[1044] An example of a prompt message is, "The passenger is in a panic. Generate a calming message."

[1045] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[1046] Step 1:

[1047] The server collects information such as earthquake magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake. This information is obtained from earthquake observation agencies and meteorological databases. The input is earthquake-related data, and the output is basic data for risk assessment. The server analyzes this data to assess the risk level of a specific area.

[1048] Step 2:

[1049] The device uses a camera and microphone to collect data in order to sense the user's voice, facial expressions, and actions. The input is the user's voice and image data, and the output is the emotion recognition result. The device uses a "Voice Recognition API" and an "Image Analysis API" to analyze this data and recognize the user's emotions.

[1050] Step 3:

[1051] The server selects appropriate evacuation shelter information and evacuation routes based on the risk assessment results and the user's sentiment recognition results. The inputs are the risk assessment results and sentiment recognition results, and the outputs are evacuation shelter information and evacuation route information. The server uses the "Map API" to calculate the optimal evacuation route.

[1052] Step 4:

[1053] The autonomous vehicle guides passengers along evacuation routes based on evacuation route information received from a server. The input is evacuation route information, and the output is guidance messages for passengers. The vehicle uses a "generative AI model" to generate messages that respond to the passengers' emotions and provides guidance via voice or display.

[1054] Step 5:

[1055] The user begins a safe evacuation based on evacuation information provided by the vehicle. The input is guidance messages from the vehicle, and the output is the user's evacuation actions. The user selects the optimal evacuation route and carries out the evacuation according to the provided information.

[1056] (Example 2)

[1057] Next, we will describe Example 2 of the Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1058] In the event of a natural disaster, it is essential to quickly and accurately assess the level of risk in specific areas and provide appropriate evacuation facilities and routes. Furthermore, there is a lack of information provision tailored to the emotional state of users, necessitating measures to prevent panic and confusion.

[1059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1060] In this invention, the server includes means for evaluating the risk of natural disasters in a specific area, means for providing information on evacuation facilities in the area, means for guiding users to appropriate evacuation routes, and means for recognizing the user's emotions and providing evacuation information that corresponds to those emotions. This enables rapid and accurate risk assessment in the event of a natural disaster and the provision of appropriate evacuation information that corresponds to the user's emotions.

[1061] "Specific areas" refers to areas that may be affected by natural disasters, and specifically includes administrative divisions and geographical boundaries.

[1062] "Natural disasters" refer to disasters caused by natural phenomena such as earthquakes, typhoons, and floods.

[1063] "Means for assessing risk" refers to methods and devices that use information about natural disasters to indicate the degree of their impact using numerical values ​​or indicators.

[1064] "Means of providing information on evacuation facilities" refers to methods and devices for providing users with information such as the location, capacity, and equipment status of evacuation shelters and facilities.

[1065] "Means of guiding evacuation routes" refers to methods and devices that present the optimal route for users to safely reach evacuation facilities.

[1066] "Means of recognizing emotions" refers to methods and devices for determining a user's emotional state from their voice, facial expressions, behavior, etc.

[1067] "Means of providing evacuation information in accordance with emotions" refers to methods and devices for providing appropriate evacuation information and messages according to the emotional state of the user.

[1068] This invention is a system aimed at rapid and accurate risk assessment during natural disasters and providing appropriate evacuation information tailored to the user's emotions. Specific embodiments of this system are described below.

[1069] The server collects information on natural disasters and assesses the risk level of specific areas. This involves using APIs from the Japan Meteorological Agency and earthquake research institutions to obtain data such as earthquake magnitude, location, depth, time of occurrence, and time elapsed since the event. Using this data, the server employs machine learning models based on historical disaster data to quantify the risk level.

[1070] The terminal receives risk information transmitted from the server and notifies the user. The terminal also uses GPS to obtain the user's current location and calculates the optimal evacuation route in real time based on evacuation facility information provided by the server. This enables the user to evacuate safely and quickly.

[1071] Furthermore, the device detects the user's voice, facial expressions, and actions through its camera and microphone, and recognizes their emotions. If it determines that the user is in a state of panic, the device displays a message such as, "Please stay calm. We will guide you to a safe evacuation route." If the user is calm, it presents multiple evacuation routes and prompts them to choose one.

[1072] As a concrete example, by inputting the prompt message "Please tell me how to assess the risk level and provide evacuation information during an earthquake" into an AI model during an earthquake, the AI ​​model will generate appropriate response methods for earthquake disasters. This prompt message allows the system to quickly present countermeasures and ensure the safety of users.

[1073] The flow of the specific processing in Example 2 will be explained using Figure 17.

[1074] Step 1:

[1075] The server collects information about natural disasters. As input, it obtains data such as earthquake magnitude, location, depth, time of occurrence, and time elapsed since the earthquake from APIs of the Japan Meteorological Agency and earthquake research institutions. Based on this data, the server processes the data to assess the level of risk and converts it into a format suitable for input into the assessment model. As output, it generates data ready for input into the assessment model.

[1076] Step 2:

[1077] The server uses the collected data and a machine learning model based on past disaster data to assess the risk level of a specific area. The data generated in Step 1 is used as input. The server performs data calculations using the machine learning model to quantify the risk level for each area. The server generates the risk assessment results for each area as output and sends them to the terminal.

[1078] Step 3:

[1079] The terminal receives the risk assessment results sent from the server and notifies the user. It receives the risk assessment results from the server as input. The terminal displays the assessment results on the screen and also provides audio notification. It notifies the user of the risk level as output.

[1080] Step 4:

[1081] The device uses GPS functionality to obtain the user's current location. The device's location services are used as input. The device obtains the current location information and sends it to the server. The current location information is provided to the server as output.

[1082] Step 5:

[1083] The server calculates the optimal evacuation route based on the user's current location and information on evacuation facilities. The inputs used are the user's current location from the terminal and pre-collected information on evacuation facilities. The server also considers road and traffic conditions, performing data calculations to determine the optimal route. The output is the generated information on the optimal evacuation route, which is then sent to the terminal.

[1084] Step 6:

[1085] The terminal receives evacuation route information transmitted from the server and guides the user. It receives evacuation route information from the server as input. The terminal presents the user with specific evacuation routes through screen displays and voice guidance. It provides evacuation route guidance to the user as output.

[1086] Step 7:

[1087] The device senses the user's voice, facial expressions, and actions through its camera and microphone, and recognizes their emotions. It uses data from its sensors as input. The device performs data processing using an emotion recognition algorithm to determine the user's emotional state. As output, it evaluates the user's emotional state and prepares to provide appropriate evacuation information.

[1088] Step 8:

[1089] The device provides appropriate evacuation information based on the user's emotional state. The emotional state assessed in step 7 is used as input. The device displays calming messages to panicked users and presents multiple evacuation routes to calm users. The output provides evacuation information tailored to the user's emotional state.

[1090] (Application Example 2)

[1091] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1092] During earthquake disasters, it is essential to quickly and accurately assess the risk level in specific areas and provide appropriate shelters and evacuation routes. However, conventional systems have a challenge in providing evacuation information that takes into account the emotional state of users, making it difficult to provide appropriate information to users in a panicked state.

[1093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1094] In this invention, the server includes means for evaluating the risk of earthquake disasters in a specific area, means for providing information on evacuation shelters in the area, means for guiding users to appropriate evacuation routes, and means for recognizing the user's emotions and providing evacuation information corresponding to those emotions. This makes it possible to provide appropriate evacuation information according to the user's emotional state.

[1095] "Specific areas" refer to areas that are subject to risk assessment and evacuation information provision during earthquake disasters.

[1096] "Methods for assessing the risk of earthquake disasters" are methods for assessing the risk to a specific area based on information such as the magnitude of the earthquake, its epicenter, its depth, the time of occurrence, and the time elapsed since the earthquake.

[1097] "Means of providing information on evacuation shelters" refers to means of providing information such as the location, capacity, and facilities of evacuation shelters in a specific area.

[1098] "Means of guiding people to appropriate evacuation routes" refers to methods for suggesting the optimal evacuation route based on information such as current location, evacuation shelter information, road conditions, and traffic conditions.

[1099] "Means of recognizing users' emotions and providing evacuation information in accordance with those emotions" refers to means of recognizing users' emotions from their voice, facial expressions, and actions, and providing appropriate evacuation information in accordance with those emotions.

[1100] The system for carrying out this invention includes a server and a terminal. The server runs a program to assess the risk level of a specific area during an earthquake disaster and provide information on evacuation shelters and appropriate evacuation routes. Specifically, the server obtains earthquake information using the Japan Meteorological Agency API and analyzes data such as the magnitude of the earthquake, the epicenter, the depth of the earthquake, the time of occurrence, and the time elapsed since the earthquake. This allows the server to assess the risk level of a specific area.

[1101] The server also retrieves evacuation shelter information from local government databases and calculates the optimal evacuation route using the Google Maps API. Furthermore, the terminal acquires the user's voice and facial expressions using the smartphone's microphone and camera, and analyzes them with OpenAI's emotion recognition model. This allows the system to recognize the user's emotional state and provide evacuation information tailored to that emotion.

[1102] As a concrete example, when an earthquake occurs, the server sends information to the terminal such as, "An earthquake with a seismic intensity of 6 has occurred. The nearest evacuation center is XX Park. Please evacuate using this route." If the user is in a panic state, the terminal displays a message such as, "Please stay calm. We will guide you to a safe evacuation center."

[1103] An example of a prompt message for a generative AI model is, "Recognize the user's emotions from their voice and facial expressions, and generate appropriate evacuation information."

[1104] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[1105] Step 1:

[1106] The server retrieves earthquake information from the Japan Meteorological Agency API. It uses the timestamp of the earthquake as input. The output includes data such as earthquake magnitude, epicenter, depth, and time of occurrence. This data is then processed to assess the risk level for specific regions.

[1107] Step 2:

[1108] The server retrieves evacuation shelter information from local government databases. It uses a specific regional identifier as input. The output includes information such as the location, capacity, and facilities of the evacuation shelters. Based on this information, the server selects the appropriate evacuation shelter.

[1109] Step 3:

[1110] The server uses the Google Maps API to calculate the optimal evacuation route. It uses the user's current location and evacuation shelter information as input. The output is the optimal evacuation route. Based on this route information, the server processes the data to guide the user.

[1111] Step 4:

[1112] The device uses the smartphone's microphone and camera to capture the user's voice and facial expressions. Real-time audio and video data of the user is used as input. Audio and facial expression data are obtained as output. This data is then processed for emotion recognition.

[1113] Step 5:

[1114] The device analyzes the user's emotions using OpenAI's emotion recognition model. It uses voice and facial expression data as input. The output is the user's emotional state. Based on this emotional information, it performs data calculations to generate appropriate evacuation information.

[1115] Step 6:

[1116] The server generates evacuation information based on the user's emotional state and sends it to the terminal. It uses emotional information and evacuation route information as input. The output is evacuation information to be displayed to the user. Specifically, if the user is in a panic state, it generates a message such as, "Please stay calm. We will guide you to a safe evacuation center."

[1117] (Other examples)

[1118] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[1119] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1120] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1121] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are examples.

[1122] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1123] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1124] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1125] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1126] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1127] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right hal...

Claims

[Claim 1] Equipped with a processor, The aforementioned processor, Using sensors and databases for collecting earthquake information, we acquire earthquake information including the magnitude, epicenter, depth, time of occurrence, and time elapsed since the earthquake. Using the earthquake information as input data, a prompt is generated to evaluate the risk level, which is an indicator showing the degree of impact caused by the earthquake. The prompt is input to the generating AI model, and the risk assessment result is obtained as the output from the generating AI model. Based on the acquired risk assessment results, a prompt is generated instructing the system to obtain shelter information, including the location, capacity, and facilities of the shelters. By using this prompt to send a query to the evacuation shelter database, the evacuation shelter information is obtained. Based on the information on the evacuation shelter, the user's current location, road conditions, and traffic conditions, a prompt is generated to suggest the optimal evacuation route. By inputting the prompt into the generating AI model, the optimal evacuation route is obtained as output from the generating AI model. The audio and video data acquired from the user's terminal are input to the emotion engine, and the emotional state of the user, recognized from the user's voice, facial expressions, and actions, is obtained as output from the emotion engine. Depending on the acquired emotional state, the content and guidance method of the optimal evacuation route are switched, and the evacuation shelter information and the optimal evacuation route are transmitted to the user's terminal. system.

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