system

Abstract

We provide the system. [Solution] Means of obtaining biometric information from users, A means of obtaining the current location using a geographic information system, Methods for obtaining disaster information from external databases, A means for generating the optimal evacuation route based on acquired biometric information, current location, and disaster information, A system that includes means for displaying and guiding users through generated evacuation routes on their terminals.

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 performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, various natural disasters have occurred in many regions, and rapid and safe evacuation is required at such times. In particular, it is complicated and difficult to formulate an evacuation plan considering the physical strength and physical limitations of each individual and the geographical characteristics of the place of residence. Since there are almost no means for presenting evacuation routes that can cope with such diverse conditions, there is a demand for providing a technology that proposes an optimal evacuation method according to individual situations.

Means for Solving the Problems

[0005] This invention is a system that generates an evacuation route optimized for individual conditions using the user's biometric information, current location, and real-time disaster information obtained from external sources, and provides it quickly to the user's terminal. This allows the user to complete evacuation quickly via the most suitable route, and furthermore, the evacuation route can be adjusted as needed based on real-time information updates. This system can also take into account the user's physical strength and physical limitations, and can present a route that is particularly safe.

[0006] "User" refers to an individual who uses this system to obtain an evacuation route.

[0007] "Biometric information" refers to data that indicates an individual's physical characteristics, such as age, gender, fitness level, and physical limitations.

[0008] A "Geographic Information System" refers to a technological infrastructure for acquiring location information and performing route calculations on a map.

[0009] "Current location" refers to the geographical point where the user is located, and is obtained using technologies such as GPS.

[0010] "Disaster information" refers to the latest data on earthquakes, tsunamis, floods, etc., provided by the Japan Meteorological Agency and disaster prevention administrative agencies.

[0011] An "evacuation route" refers to a designated route used to safely and quickly guide people to their evacuation destination during a disaster.

[0012] "External database" refers to an external source of information that this system accesses to obtain disaster information.

[0013] "User terminal" refers to a mobile information terminal such as a smartphone or tablet that displays evacuation routes. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides a system for presenting evacuation routes suitable for each user in the event of a disaster. This system combines the user's biometric information, current location information, and real-time acquired disaster information to generate the optimal evacuation route and display it on the user's terminal.

[0036] First, during initial setup, users enter their biometric information, including age, gender, fitness level, and physical limitations, into the device. This information is securely transmitted from the device to a server and later stored in a database. Location information is also periodically updated on the server using the device's GPS function. The server further acquires real-time disaster information from external databases such as the Japan Meteorological Agency, enabling a rapid response when evacuation becomes necessary.

[0037] When a disaster occurs, the server uses an AI algorithm to calculate the optimal evacuation route based on acquired biometric information, location information, and disaster information. In this process, the server can select barrier-free routes that take into account the user's physical strength and limitations, as well as efficient and safe routes that consider traffic congestion and road closures. The generated evacuation route is immediately notified to the terminal, guiding the user through maps and voice prompts.

[0038] For example, suppose a user is affected by an earthquake at home. Because this user has previously registered a mild walking disability, the server prioritizes calculating a route that avoids stairs. Based on this calculation, the evacuation route is displayed on the device along with a map, and specific voice guidance such as "Turn right at the next traffic light" is also provided.

[0039] In this way, the system of the present invention provides evacuation support tailored to the individual circumstances of each user, enabling safer and faster evacuation. This is an important tool for improving the accuracy and speed of evacuation during disasters.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] When a user uses the application for the first time, they enter biometric information such as age, gender, fitness level, and physical limitations into their device. The device then encrypts this information, sends it to a server, and securely stores it in a database.

[0043] Step 2:

[0044] The device periodically uses its GPS function to obtain the user's current location. This location information is sent to a server for updating and used as basic information when generating evacuation routes.

[0045] Step 3:

[0046] The server obtains real-time disaster information through APIs provided by the Japan Meteorological Agency and disaster prevention administrative agencies. This information is then analyzed, and the system determines the necessity of evacuation.

[0047] Step 4:

[0048] In the event of a disaster, the server uses AI algorithms to generate the optimal evacuation route based on the user's biometric information, location information, and disaster information. It also evaluates terrain data and the status of transportation infrastructure to consider safe and rapid evacuation routes.

[0049] Step 5:

[0050] The server sends the generated evacuation route to the terminal. The terminal receives this information and displays and guides the user through the evacuation route using a map and voice prompts.

[0051] Step 6:

[0052] The device continuously tracks the user's location while they are on the move and sends updated data to the server as needed. Based on this data, the server recalculates evacuation routes in real time in response to changing circumstances and resends the latest information to the device.

[0053] (Example 1)

[0054] Next, we will describe 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."

[0055] One challenge during a disaster is the difficulty in quickly providing appropriate evacuation routes based on the individual user's physical characteristics and current location. Conventional methods typically provide uniform evacuation routes, failing to adequately consider the individual circumstances of each user.

[0056] 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.

[0057] In this invention, the server includes means for acquiring biometric information from the user, means for acquiring the current location using a function for measuring location information, and means for acquiring disaster information from an external storage device. This makes it possible to generate an optimal evacuation route tailored to the individual situation, taking into account the user's physical abilities and physical conditions, and to make real-time adjustments along that route.

[0058] "Biometric information" refers to individual physical data such as the user's age, gender, fitness level, and physical limitations.

[0059] "Location information" refers to the user's current geographical location, which is obtained in real time through the device's measurement function.

[0060] "Disaster information" refers to geographically and meteorologically relevant emergency information obtained from external storage devices.

[0061] A "generation processing model" refers to a set of algorithms and programs used to calculate and generate the optimal evacuation route based on acquired information.

[0062] "User terminal" refers to a device or equipment owned by a user for receiving information.

[0063] The "real-time adjustment function" refers to a function that dynamically updates the generated evacuation routes based on the latest information.

[0064] "Physical ability" refers to the characteristics related to the user's physical strength and motor functions.

[0065] "Physical conditions" refer to individual physical characteristics or limitations that affect the user.

[0066] This evacuation route generation system is designed to present the optimal evacuation route for each individual user in the event of a disaster. The system mainly consists of user terminals, a server, and external storage devices.

[0067] The terminal provides an interface for users to input biometric information as part of their initial setup. For example, users can input their age, gender, fitness level, physical limitations, etc. This information is securely transmitted from the terminal to the server.

[0068] Regarding location information, the device uses its built-in positioning function (e.g., GPS) to obtain the user's current location in real time. This location information is also periodically transmitted to the server.

[0069] The server retrieves disaster information from external storage devices in real time based on received biometric and location information. This disaster information is acquired using a publicly available API. The server integrates this information and designs the optimal evacuation route using a generative processing model. Frameworks such as TENSORFLOW® and PyTorch can be used to implement the generative processing model.

[0070] Once the optimal evacuation route is generated, the server sends that information to the user's device. The device then uses this information to provide visual and audio guidance, such as "Turn left at the next intersection."

[0071] For example, if a user experiences an earthquake and has previously registered a mild walking disability, the server will prioritize calculating a route that avoids stairs. The result will then be displayed on the device as a map and audio guide.

[0072] An example of a prompt message might be, "In the event of an earthquake, please provide a barrier-free evacuation route for a user with mild mobility impairment." In this way, the system can provide a quick and safe evacuation route tailored to the individual characteristics and circumstances of each user.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user enters biometric information into the device. Specifically, the user enters their age, gender, fitness level, and physical limitations into a form displayed on the application screen. The entered data is converted to JSON format and sent to the server.

[0076] Input: User's physical information (age, gender, etc.)

[0077] Output: Biometric data in JSON format

[0078] Step 2:

[0079] The device measures location information. Using its built-in GPS function, the device periodically measures the user's current location. The measured location information is transmitted to the server in real time.

[0080] Input: GPS signal

[0081] Output: Real-time location information (latitude, longitude)

[0082] Step 3:

[0083] The server retrieves disaster information from external storage devices. The server obtains the necessary disaster information via a public API using GET requests. The retrieved data is analyzed, and the server updates the information regarding the current disaster situation.

[0084] Input: API Request

[0085] Output: Analyzed disaster information

[0086] Step 4:

[0087] The server generates the optimal evacuation route using a generative processing model. An AI algorithm calculates the evacuation route using acquired biometric information, location information, and disaster information as input. Optimization is performed using a deep learning library during this process.

[0088] Input: Biometric information, real-time location information, disaster information

[0089] Output: Optimized evacuation routes

[0090] Step 5:

[0091] The server sends the generated evacuation route to the user's terminal. The data is transmitted to the terminal via a secure communication protocol.

[0092] Input: Evacuation route data

[0093] Output: Route information displayed on the terminal

[0094] Step 6:

[0095] The device displays the evacuation route and begins voice guidance. It generates a map using a map SDK and visually displays the route. It also provides voice guidance using text-to-speech functionality.

[0096] Input: Received evacuation route data

[0097] Output: Map display and voice route guidance

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] During disaster evacuations, there is a need to provide optimal evacuation routes based on each individual's physical characteristics and location. However, conventional technologies have struggled to provide real-time information updates and to perform complex route adjustments that take into account traffic conditions and the congestion level of evacuation centers. As a result, there is a problem in that appropriate evacuation routes are not provided, making efficient evacuation difficult.

[0101] 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.

[0102] In this invention, the server includes means for acquiring biological data from users, means for acquiring geographic location using geographic information technology, and means for acquiring disaster information from external sources. This enables the dynamic generation and adjustment of evacuation routes according to individual physical abilities and circumstances, allowing for rapid and safe evacuation.

[0103] "Biological data" refers to personal biometric information such as the user's age, gender, athletic ability, and health status.

[0104] "Geographic information technology" refers to technologies for acquiring and processing geographical location and route information. Examples include GPS and digital map services.

[0105] "Geographic location" refers to coordinate information that indicates the user's current physical location.

[0106] "Disaster information" refers to real-time information about natural disasters such as weather and earthquakes. It is obtained from external sources.

[0107] "External information sources" refer to information provided from databases or APIs outside the system.

[0108] An "evacuation route" is a recommended route for users to evacuate safely and quickly in the event of a disaster.

[0109] "User devices" refer to devices used to display evacuation route information and provide voice guidance. Specific examples include smartphones and tablets.

[0110] "Dynamic adjustment" refers to modifying evacuation plans in real time as needed, based on changes in circumstances and new information.

[0111] "Information and communication technology" refers to all technologies that utilize computers and networks to process and transmit information.

[0112] To realize this invention, the user's mobile device plays a crucial role. Specifically, a device such as a smartphone functions as hardware for inputting the user's biological data and acquiring location information. This device uses geographic information technology to track geographic location in real time. A specific example is the GPS function.

[0113] The server executes an AI algorithm developed using Python, integrating various pieces of information to calculate the optimal evacuation route. The server is built as a cloud service, enabling scalable processing using services such as AWS Lambda. The server also has the capability to acquire data in real time from external sources, such as the Japan Meteorological Agency and other disaster information services.

[0114] Evacuation routes are individually customized to take into account the user's motor skills and physical limitations. This process is automated by an AI model, and the generated route information is transmitted to the user's device, where it is guided visually and audibly. Evacuation routes are updated in real time using information and communication technology, and are dynamically adjusted according to road conditions and the congestion level of evacuation shelters.

[0115] For example, in the event of heavy rain or a sudden earthquake, the server quickly recalculates evacuation routes and guides the user's device to a shelter. At this time, prompts such as, "You need to evacuate from your current location. Please tell me the optimal route considering your physical condition and road conditions," are used, allowing the user to input necessary information into their terminal and take appropriate action. This enables rapid and safe evacuation tailored to individual needs.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user enters biological data into the terminal. This data includes age, gender, and athletic ability. The entered data is securely transmitted from the terminal to the server and stored in a database. The input here is the user's personal information, and the output is the process of saving it to the server's database.

[0119] Step 2:

[0120] The user's device uses GPS functionality to obtain its current geographical location. This location data is periodically transmitted to the server. The input is the location information obtained by the device, and the output is the transmission to the server.

[0121] Step 3:

[0122] The server retrieves real-time disaster information from external sources. This process involves retrieving data via APIs from the Japan Meteorological Agency and disaster information service providers. The input is disaster information from external sources, and the output is the data retrieved to the server.

[0123] Step 4:

[0124] The server uses a generated AI model to calculate the optimal evacuation route by combining biological data, geographical location, and disaster information. The inputs are personal data saved in step 1, location data obtained in step 2, and disaster information obtained in step 3, and the output is the calculated evacuation route.

[0125] Step 5:

[0126] The server calculates the evacuation route and transmits it to the terminal, which then presents it to the user through visual and audio guidance. The input is the evacuation route data from the server, and the output is the display and audio guidance on the user's terminal.

[0127] Step 6:

[0128] The server uses information and communication technology to update evacuation routes in real time. It dynamically adjusts routes according to changing conditions, taking into account traffic information and the congestion level of evacuation shelters. The input is traffic data and evacuation shelter information, and the output is the updated evacuation route.

[0129] 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.

[0130] This invention is a system designed to support rapid and safe evacuation during disasters. By combining it with an emotion engine that monitors and analyzes the user's emotional state, it provides more personalized evacuation guidance. This emotion engine allows the system to sense the user's anxiety and tension and present appropriate evacuation guidance accordingly.

[0131] First, the user enters biometric information into the device during the initial system setup. This information includes age, gender, fitness level, and physical limitations. The device encrypts this information and sends it to a server for storage in a database. Furthermore, the device periodically obtains the user's current location using GPS and sends this information to the server, making it possible to track the user's location.

[0132] Even under normal circumstances, the server has a system in place to obtain disaster information in real time by coordinating effectively with the Japan Meteorological Agency and disaster prevention administrative information agencies. Based on this information, the server determines the need for user evacuation in the event of a disaster and immediately begins taking action.

[0133] A distinctive feature of this invention is that the emotion engine analyzes the user's emotions from their voice and input patterns. Based on this analysis, the server can adjust the method of providing evacuation route guidance. For example, if the system determines that the user is emotionally unstable, it can provide appropriate feedback, such as delivering guidance comments in a calm voice.

[0134] As a concrete example, consider a scenario where a user experiences an earthquake and their anxiety and panic increase. The emotion engine detects this and instructs the terminal to provide calm guidance appropriate to the situation. In this case, the terminal plays the voice guidance in a calmer tone than usual, guiding the user to safety. Furthermore, users who need to avoid stairs are given priority guidance to elevators or barrier-free routes.

[0135] In this way, the present invention provides a personalized evacuation route based on the user's individual circumstances and emotional state, thereby realizing a safer and more comfortable evacuation experience.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] When a user sets up the application for the first time, they enter biometric information (age, gender, physical fitness, physical limitations, etc.) into the device. The device encrypts this data, sends it to a server, and securely stores it in a database.

[0139] Step 2:

[0140] The device periodically uses GPS functionality to obtain the user's current location. This location information is transmitted to a server and used to quickly and accurately generate evacuation routes in the event of a disaster.

[0141] Step 3:

[0142] The server retrieves real-time disaster information from an external database. This information includes earthquakes, tsunamis, and floods, and the system uses this information to determine the necessity of evacuation.

[0143] Step 4:

[0144] When a disaster occurs, the server uses an AI algorithm to calculate the optimal evacuation route based on the user's biometric information, current location, and disaster information. This calculation also evaluates terrain data and transportation infrastructure to aim for safer and faster evacuation.

[0145] Step 5:

[0146] The server sends the calculated evacuation route to the terminal. The terminal then displays the route on a map and provides voice guidance to the user.

[0147] Step 6:

[0148] The emotion engine analyzes the user's emotions from their voice input and operation patterns. For example, if the user's voice is trembling, the emotion engine detects anxiety or fear.

[0149] Step 7:

[0150] The server adjusts evacuation instructions based on feedback from the emotion engine. For example, it might soften the tone of the voice guidance on the device or simplify the message to enhance the user's sense of security.

[0151] Step 8:

[0152] The device continuously monitors the user's emotions and location in real time. If the server detects a change in the situation, it immediately recalculates the evacuation route and provides the user with updated information.

[0153] This allows the system to provide optimal evacuation support tailored to the user's situation, resulting in a safer and more secure evacuation experience.

[0154] (Example 2)

[0155] Next, we will describe 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".

[0156] In the event of a disaster, it is essential to ensure the rapid and safe evacuation of individuals, taking into account their individual emotional and physical states. However, conventional evacuation systems have failed to consider the emotional state of individuals, merely providing uniform evacuation route guidance. As a result, there is a problem in that optimal evacuation cannot be achieved when individuals are experiencing anxiety or tension.

[0157] 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.

[0158] In this invention, the server includes means for acquiring biometric information from the user, means for acquiring the current location using location information technology, and means for analyzing the user's emotional state using a generative AI model. This enables the adjustment of the optimal evacuation route based on the user's individual circumstances and provides guidance that takes their emotions into consideration.

[0159] "User" refers to an individual who receives evacuation route guidance using the system.

[0160] "Biometric information" refers to information that indicates the unique biological or physical characteristics of an individual user, including age, gender, physical ability, and physical limitations.

[0161] "Location-based technology" refers to all technologies used to determine a user's current location, primarily utilizing the Global Positioning System (GPS).

[0162] "Disaster information" refers to information obtained from external databases related to natural disasters and other disasters, and includes data related to the occurrence of weather, earthquakes, tsunamis, etc.

[0163] "Generative AI models" refer to artificial intelligence technologies used to analyze a user's emotional state, and include models that analyze emotions from voice and input data.

[0164] "Emotional state" refers to information that describes the user's psychological state, including sensations and emotions such as anxiety, tension, and composure.

[0165] An "evacuation route" refers to a path established to enable safe and efficient evacuation in the event of a disaster.

[0166] "Adjustment" refers to modifications or changes made when optimizing generated evacuation routes according to the emotional state and physical condition of the users.

[0167] "Guidance" refers to the act of informing and guiding users to evacuation routes through visually or audibly presented information.

[0168] This invention is a system that supports rapid and safe evacuation during disasters, providing personalized evacuation guidance tailored to the user's individual circumstances. The system incorporates an emotion engine equipped with a generative AI model that analyzes the user's emotional state.

[0169] First, during the initial system setup, the user enters biometric information into the terminal. This biometric information includes age, gender, fitness level, and physical limitations. The terminal encrypts this information and sends it to the server. The AES algorithm is used for encryption. The server stores the encrypted data in a database. Standard data management software is used for this database.

[0170] Next, the device periodically obtains the user's current location using GPS technology and sends this location information to the server. The location information is sent to the server in JSON format, and the server stores this in its database.

[0171] Disaster information is retrieved in real time by the server from an external database. A RESTful API is used in this process. The server compares the retrieved disaster information with the user's location information and issues evacuation instructions as needed.

[0172] The emotion engine acquires the user's voice input and input patterns from the device and analyzes their emotional state using a generative AI model. The results of this analysis are sent to a server, which then adjusts evacuation route guidance based on this information.

[0173] As a concrete example, consider a scenario where a user experiences an earthquake and becomes anxious. The emotion engine detects this anxiety, and the server instructs the terminal to provide evacuation guidance in a calmer tone through voice prompts. This allows the user to evacuate calmly.

[0174] An example of a prompt is, "Please describe in detail how to set up an evacuation scenario during a disaster and analyze the emotional state of the users." By inputting this prompt into the AI ​​generation model, an appropriate evacuation guidance method will be generated.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The user enters biometric information into the terminal, including age, gender, fitness level, and physical limitations. The terminal encrypts this biometric information using the AES algorithm and securely transmits it to the server. It receives biometric information as input data and generates encrypted data as output. This encrypted data is sent to the server and stored in a database.

[0178] Step 2:

[0179] The device periodically obtains the user's current location using GPS technology. This location information is sent to the server in JSON format as latitude and longitude data. The device receives the current GPS data as input and processes it to send it to the server. The server receives this information and registers it in its database.

[0180] Step 3:

[0181] The server retrieves disaster information from an external database. Using a RESTful API, it saves the retrieved disaster information to the database in real time. The input is external disaster data, which the server processes and compares with the user's location information.

[0182] Step 4:

[0183] The device acquires the user's voice input and input patterns. The emotion engine uses this as input and a generating AI model to analyze the user's emotional state. The analysis results are output as states such as anxiety and tension and sent to the server.

[0184] Step 5:

[0185] The server adjusts evacuation route guidance based on the analysis results received from the emotion engine. It generates optimal evacuation guidance considering the user's emotional state and biometric information. The server receives emotion analysis data and biometric information as input, generates an adjusted evacuation route as output, and sends guidance instructions to the terminal.

[0186] Step 6:

[0187] The terminal provides voice guidance to the user based on instructions received from the server. It adjusts the tone using speech synthesis technology and provides specific evacuation route instructions. The terminal receives instructions from the server as input and provides voice guidance to the user as output. In this process, appropriate tone and route selection enable the terminal to guide the user calmly.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0190] In the event of a disaster, in order for users to evacuate appropriately and quickly, personalized guidance that takes into account users' emotional instability is necessary, in addition to geographical route information. Furthermore, existing systems have difficulty responding to users' changing emotional states, which can result in panic and an inability to take appropriate evacuation actions.

[0191] 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.

[0192] In this invention, the server includes means for acquiring the user's biometric information, means for acquiring the current location using a geographic information system, means for acquiring disaster information from an external database, means for analyzing the user's emotional state and adjusting the content of evacuation guidance based on that analysis, and means for changing the tone of the voice guide according to the emotional state. This enables flexible evacuation guidance tailored to the individual emotional state of the user, thereby realizing safer and more efficient evacuation actions.

[0193] "Biometric information" refers to physiological data such as the user's physical fitness, health status, and heart rate.

[0194] A "geographic information system" is a technology used to determine a user's current location, and it includes map information.

[0195] "Disaster information" refers to data related to natural disasters such as earthquakes and typhoons, and is information obtained from external databases.

[0196] An "evacuation route" refers to a route that allows users to evacuate safely during a disaster, and is generated by a system.

[0197] "User terminal" refers to devices operated by users, such as smartphones and portable information devices.

[0198] "Emotional state" refers to the user's psychological state, such as anxiety or tension, and is analyzed from their voice tone and input patterns.

[0199] "Audio guide" refers to a means of providing instructions and guidance to users through audio.

[0200] To implement this invention, it is necessary to construct an integrated system that provides optimized evacuation guidance during disasters. This system uses smartphones or portable information devices to acquire the user's biometric information. These devices have built-in heart rate sensors and microphones, allowing for real-time acquisition of the user's heart rate and voice data. The acquired voice data is sent to an emotion engine and converted to text using the Google® Cloud Speech-to-Text API. Subsequently, the emotional state is analyzed using natural language processing technology based on TensorFlow. Based on the analyzed emotional state, the server personalizes the user's evacuation guidance in real time.

[0201] The geographic information system uses GPS technology to determine the user's current location. This location information is linked with the latest disaster data obtained from a disaster information database, and an optimized evacuation route is generated. The server adjusts the evacuation route while considering the user's physical condition, physical limitations, and emotional state, and provides this information to the terminal as an audio guide. Real-time notifications via voice and text are enabled using services such as Twilio.

[0202] As a concrete example, consider the scenario of an earthquake. Suppose the user's heart rate increases and they feel anxious. At this point, the emotion engine detects the user's anxiety and instructs the device to provide a calming voice guidance. This guidance provides messages such as, "Take a deep breath and calm down. We will guide you to a safe route," reassuring the user.

[0203] Examples of prompt statements to input into a generative AI model include the following:

[0204] "Please share your ideas for an application that determines a user's situation during an earthquake and recommends the calmest and safest evacuation route."

[0205] This system helps users take optimal evacuation actions during disasters, ensuring safe and efficient evacuations.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The device uses the smartphone's heart rate sensor and microphone to acquire biometric and voice data. This data is sent to the server as input information. This allows for real-time monitoring of biometric information.

[0209] Step 2:

[0210] The server converts the received audio data into text using the Google Cloud Speech-to-Text API. The input is audio data, and the output is text information. This text data is used as foundational information for sentiment analysis.

[0211] Step 3:

[0212] The server inputs this text information into a generative AI model using TensorFlow to analyze the emotional state. The input is text data, and the output is the user's emotional state (e.g., anxious, stable). This analysis evaluates the user's psychological state.

[0213] Step 4:

[0214] The server uses GPS technology to obtain the user's current location information from a geographic information system. The input is location data, and the output is the user's current latitude and longitude information. This ensures the location information necessary for generating evacuation routes.

[0215] Step 5:

[0216] The server calculates the optimal evacuation route based on disaster information obtained from an external database, along with biometric information, location information, and emotion analysis results. The input is this various data, and the output is the evacuation route. The algorithm generates a safe and efficient route.

[0217] Step 6:

[0218] The terminal uses evacuation route and audio guidance information received from the server to begin providing real-time guidance to the user. In particular, the tone of the audio guidance changes according to the user's emotional state. The input is guidance information from the server, and the output is audio guidance from the terminal to the user. The user can take evacuation actions with confidence thanks to the audio guidance.

[0219] Step 7:

[0220] The server can also use services like Twilio to send additional text notifications at the user's request. The input is instructional information from the server, and the output is a text message to the user. This allows the user to receive situation-specific evacuation instructions.

[0221] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0222] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search)<url: https: gemini.google.com ?hl="ja">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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0237] This invention provides a system for presenting evacuation routes suitable for each user in the event of a disaster. This system combines the user's biometric information, current location information, and real-time acquired disaster information to generate the optimal evacuation route and display it on the user's terminal.

[0238] First, during initial setup, users enter their biometric information, including age, gender, fitness level, and physical limitations, into the device. This information is securely transmitted from the device to a server and later stored in a database. Location information is also periodically updated on the server using the device's GPS function. The server further acquires real-time disaster information from external databases such as the Japan Meteorological Agency, enabling a rapid response when evacuation becomes necessary.

[0239] When a disaster occurs, the server uses an AI algorithm to calculate the optimal evacuation route based on acquired biometric information, location information, and disaster information. In this process, the server can select barrier-free routes that take into account the user's physical strength and limitations, as well as efficient and safe routes that consider traffic congestion and road closures. The generated evacuation route is immediately notified to the terminal, guiding the user through maps and voice prompts.

[0240] For example, suppose a user is affected by an earthquake at home. Because this user has previously registered a mild walking disability, the server prioritizes calculating a route that avoids stairs. Based on this calculation, the evacuation route is displayed on the device along with a map, and specific voice guidance such as "Turn right at the next traffic light" is also provided.

[0241] In this way, the system of the present invention provides evacuation support tailored to the individual circumstances of each user, enabling safer and faster evacuation. This is an important tool for improving the accuracy and speed of evacuation during disasters.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] When a user uses the application for the first time, they enter biometric information such as age, gender, fitness level, and physical limitations into their device. The device then encrypts this information, sends it to a server, and securely stores it in a database.

[0245] Step 2:

[0246] The device periodically uses its GPS function to obtain the user's current location. This location information is sent to a server for updating and used as basic information when generating evacuation routes.

[0247] Step 3:

[0248] The server obtains real-time disaster information through APIs provided by the Japan Meteorological Agency and disaster prevention administrative agencies. This information is then analyzed, and the system determines the necessity of evacuation.

[0249] Step 4:

[0250] In the event of a disaster, the server uses AI algorithms to generate the optimal evacuation route based on the user's biometric information, location information, and disaster information. It also evaluates terrain data and the status of transportation infrastructure to consider safe and rapid evacuation routes.

[0251] Step 5:

[0252] The server sends the generated evacuation route to the terminal. The terminal receives this information and displays and guides the user through the evacuation route using a map and voice prompts.

[0253] Step 6:

[0254] The device continuously tracks the user's location while they are on the move and sends updated data to the server as needed. Based on this data, the server recalculates evacuation routes in real time in response to changing circumstances and resends the latest information to the device.

[0255] (Example 1)

[0256] Next, we will describe 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."

[0257] One challenge during a disaster is the difficulty in quickly providing appropriate evacuation routes based on the individual user's physical characteristics and current location. Conventional methods typically provide uniform evacuation routes, failing to adequately consider the individual circumstances of each user.

[0258] 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.

[0259] In this invention, the server includes means for acquiring biometric information from the user, means for acquiring the current location using a function for measuring location information, and means for acquiring disaster information from an external storage device. This makes it possible to generate an optimal evacuation route tailored to the individual situation, taking into account the user's physical abilities and physical conditions, and to make real-time adjustments along that route.

[0260] "Biometric information" refers to individual physical data such as the user's age, gender, fitness level, and physical limitations.

[0261] "Location information" refers to the user's current geographical location, which is obtained in real time through the device's measurement function.

[0262] "Disaster information" refers to geographically and meteorologically relevant emergency information obtained from external storage devices.

[0263] A "generation processing model" refers to a set of algorithms and programs used to calculate and generate the optimal evacuation route based on acquired information.

[0264] "User terminal" refers to a device or equipment owned by a user for receiving information.

[0265] The "real-time adjustment function" refers to a function that dynamically updates the generated evacuation routes based on the latest information.

[0266] "Physical ability" refers to the characteristics related to the user's physical strength and motor functions.

[0267] "Physical conditions" refer to individual physical characteristics or limitations that affect the user.

[0268] This evacuation route generation system is designed to present the optimal evacuation route for each individual user in the event of a disaster. The system mainly consists of user terminals, a server, and external storage devices.

[0269] The terminal provides an interface for users to input biometric information as part of their initial setup. For example, users can input their age, gender, fitness level, physical limitations, etc. This information is securely transmitted from the terminal to the server.

[0270] Regarding location information, the device uses its built-in positioning function (e.g., GPS) to obtain the user's current location in real time. This location information is also periodically transmitted to the server.

[0271] The server retrieves disaster information from external storage devices in real time based on received biometric and location data. This disaster information is acquired using a publicly available API. The server integrates this information and designs the optimal evacuation route using a generative processing model. Frameworks such as TensorFlow and PyTorch can be used to implement the generative processing model.

[0272] Once the optimal evacuation route is generated, the server sends that information to the user's device. The device then uses this information to provide visual and audio guidance, such as "Turn left at the next intersection."

[0273] For example, if a user experiences an earthquake and has previously registered a mild walking disability, the server will prioritize calculating a route that avoids stairs. The result will then be displayed on the device as a map and audio guide.

[0274] As an example of a prompt sentence, "Please show a barrier-free evacuation route for users with mild walking disabilities during an earthquake" can be considered. In this way, the system enables the provision of a prompt and safe evacuation route according to the individual characteristics and situations of users.

[0275] The flow of the specific process in Example 1 will be described using FIG. 11.

[0276] Step 1:

[0277] The user inputs biometric information into the terminal. As a specific operation, the user inputs age, gender, physical strength level, and physical constraints into the form shown on the application screen. The input data is converted into JSON format and sent to the server.

[0278] Input: User's physical information (age, gender, etc.)

[0279] Output: Biometric information data in JSON format

[0280] Step 2:

[0281] The terminal measures the location information. The terminal uses the built-in GPS function to regularly measure the current location of the user. The measured location information is sent to the server in real time.

[0282] Input: GPS signal

[0283] Output: Real-time location information (latitude, longitude)

[0284] Step 3:

[0285] The server obtains disaster information from an external storage device. The server obtains the necessary disaster information through a public API in a get request. The obtained data is analyzed and the information regarding the current disaster situation is updated.

[0286] Input: API request

[0287] Output: Analyzed disaster information

[0288] Step 4:

[0289] The server generates the optimal evacuation route using a generative processing model. An AI algorithm calculates the evacuation route using acquired biometric information, location information, and disaster information as input. Optimization is performed using a deep learning library during this process.

[0290] Input: Biometric information, real-time location information, disaster information

[0291] Output: Optimized evacuation routes

[0292] Step 5:

[0293] The server sends the generated evacuation route to the user's terminal. The data is transmitted to the terminal via a secure communication protocol.

[0294] Input: Evacuation route data

[0295] Output: Route information displayed on the terminal

[0296] Step 6:

[0297] The device displays the evacuation route and begins voice guidance. It generates a map using a map SDK and visually displays the route. It also provides voice guidance using text-to-speech functionality.

[0298] Input: Received evacuation route data

[0299] Output: Map display and voice route guidance

[0300] (Application Example 1)

[0301] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0302] In the event of evacuation during a disaster, there is a demand for presenting an optimal evacuation route based on the physical characteristics and location of each individual. However, with conventional technologies, it has been difficult to update information in real time and to perform complex route adjustments that include traffic conditions and the congestion level of evacuation shelters. For this reason, there is a problem in that an appropriate evacuation route is not provided and efficient evacuation becomes difficult.

[0303] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0304] In this invention, the server includes means for acquiring biological data from a user, means for acquiring a geographical position using geographical information technology, and means for acquiring disaster information from an external information source. Thereby, an evacuation route corresponding to individual movement ability and situation can be dynamically generated and adjusted, enabling quick and safe evacuation.

[0305] "Biological data" refers to personal biological information such as the age, gender, movement ability, and health condition of the user.

[0306] "Geographical information technology" refers to technologies for acquiring and processing geographical positions and route information. Examples include GPS and digital map services.

[0307] "Geographical position" is coordinate information indicating the current physical position of the user.

[0308] "Disaster information" refers to real-time information regarding natural disasters such as weather and earthquakes. It is acquired from an external information source.

[0309] "External information source" refers to information provided from databases and APIs outside the system.

[0310] "Evacuation route" is a recommended route for the user to evacuate safely and quickly during a disaster.

[0311] "User devices" refer to devices used to display evacuation route information and provide voice guidance. Specific examples include smartphones and tablets.

[0312] "Dynamic adjustment" refers to modifying evacuation plans in real time as needed, based on changes in circumstances and new information.

[0313] "Information and communication technology" refers to all technologies that utilize computers and networks to process and transmit information.

[0314] To realize this invention, the user's mobile device plays a crucial role. Specifically, a device such as a smartphone functions as hardware for inputting the user's biological data and acquiring location information. This device uses geographic information technology to track geographic location in real time. A specific example is the GPS function.

[0315] The server executes an AI algorithm developed using Python, integrating various pieces of information to calculate the optimal evacuation route. The server is built as a cloud service, enabling scalable processing using, for example, AWS Lambda. The server also has the capability to acquire data in real time from external sources, such as the Japan Meteorological Agency and other disaster information services.

[0316] Evacuation routes are individually customized to take into account the user's motor skills and physical limitations. This process is automated by an AI model, and the generated route information is transmitted to the user's device, where it is guided visually and audibly. Evacuation routes are updated in real time using information and communication technology, and are dynamically adjusted according to road conditions and the congestion level of evacuation shelters.

[0317] For example, in the event of heavy rain or a sudden earthquake, the server quickly recalculates evacuation routes and guides the user's device to a shelter. At this time, prompts such as, "You need to evacuate from your current location. Please tell me the optimal route considering your physical condition and road conditions," are used, allowing the user to input necessary information into their terminal and take appropriate action. This enables rapid and safe evacuation tailored to individual needs.

[0318] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0319] Step 1:

[0320] The user enters biological data into the terminal. This data includes age, gender, and athletic ability. The entered data is securely transmitted from the terminal to the server and stored in a database. The input here is the user's personal information, and the output is the process of saving it to the server's database.

[0321] Step 2:

[0322] The user's device uses GPS functionality to obtain its current geographical location. This location data is periodically transmitted to the server. The input is the location information obtained by the device, and the output is the transmission to the server.

[0323] Step 3:

[0324] The server retrieves real-time disaster information from external sources. This process involves retrieving data via APIs from the Japan Meteorological Agency and disaster information service providers. The input is disaster information from external sources, and the output is the data retrieved to the server.

[0325] Step 4:

[0326] The server uses a generated AI model to calculate the optimal evacuation route by combining biological data, geographical location, and disaster information. The inputs are personal data saved in step 1, location data obtained in step 2, and disaster information obtained in step 3, and the output is the calculated evacuation route.

[0327] Step 5:

[0328] The server calculates the evacuation route and transmits it to the terminal, which then presents it to the user through visual and audio guidance. The input is the evacuation route data from the server, and the output is the display and audio guidance on the user's terminal.

[0329] Step 6:

[0330] The server uses information and communication technology to update evacuation routes in real time. It dynamically adjusts routes according to changing conditions, taking into account traffic information and the congestion level of evacuation shelters. The input is traffic data and evacuation shelter information, and the output is the updated evacuation route.

[0331] 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.

[0332] This invention is a system designed to support rapid and safe evacuation during disasters. By combining it with an emotion engine that monitors and analyzes the user's emotional state, it provides more personalized evacuation guidance. This emotion engine allows the system to sense the user's anxiety and tension and present appropriate evacuation guidance accordingly.

[0333] First, the user enters biometric information into the device during the initial system setup. This information includes age, gender, fitness level, and physical limitations. The device encrypts this information and sends it to a server for storage in a database. Furthermore, the device periodically obtains the user's current location using GPS and sends this information to the server, making it possible to track the user's location.

[0334] Even under normal circumstances, the server has a system in place to obtain disaster information in real time by coordinating effectively with the Japan Meteorological Agency and disaster prevention administrative information agencies. Based on this information, the server determines the need for user evacuation in the event of a disaster and immediately begins taking action.

[0335] A distinctive feature of this invention is that the emotion engine analyzes the user's emotions from their voice and input patterns. Based on this analysis, the server can adjust the method of providing evacuation route guidance. For example, if the system determines that the user is emotionally unstable, it can provide appropriate feedback, such as delivering guidance comments in a calm voice.

[0336] As a concrete example, consider a scenario where a user experiences an earthquake and their anxiety and panic increase. The emotion engine detects this and instructs the terminal to provide calm guidance appropriate to the situation. In this case, the terminal plays the voice guidance in a calmer tone than usual, guiding the user to safety. Furthermore, users who need to avoid stairs are given priority guidance to elevators or barrier-free routes.

[0337] In this way, the present invention provides a personalized evacuation route based on the user's individual circumstances and emotional state, thereby realizing a safer and more comfortable evacuation experience.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] When a user sets up the application for the first time, they enter biometric information (age, gender, physical fitness, physical limitations, etc.) into the device. The device encrypts this data, sends it to a server, and securely stores it in a database.

[0341] Step 2:

[0342] The device periodically uses GPS functionality to obtain the user's current location. This location information is transmitted to a server and used to quickly and accurately generate evacuation routes in the event of a disaster.

[0343] Step 3:

[0344] The server retrieves real-time disaster information from an external database. This information includes earthquakes, tsunamis, and floods, and the system uses this information to determine the necessity of evacuation.

[0345] Step 4:

[0346] When a disaster occurs, the server uses an AI algorithm to calculate the optimal evacuation route based on the user's biometric information, current location, and disaster information. This calculation also evaluates terrain data and transportation infrastructure to aim for safer and faster evacuation.

[0347] Step 5:

[0348] The server sends the calculated evacuation route to the terminal. The terminal then displays the route on a map and provides voice guidance to the user.

[0349] Step 6:

[0350] The emotion engine analyzes the user's emotions from their voice input and operation patterns. For example, if the user's voice is trembling, the emotion engine detects anxiety or fear.

[0351] Step 7:

[0352] The server adjusts evacuation instructions based on feedback from the emotion engine. For example, it might soften the tone of the voice guidance on the device or simplify the message to enhance the user's sense of security.

[0353] Step 8:

[0354] The device continuously monitors the user's emotions and location in real time. If the server detects a change in the situation, it immediately recalculates the evacuation route and provides the user with updated information.

[0355] This allows the system to provide optimal evacuation support tailored to the user's situation, resulting in a safer and more secure evacuation experience.

[0356] (Example 2)

[0357] Next, we will describe 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".

[0358] In the event of a disaster, it is essential to ensure the rapid and safe evacuation of individuals, taking into account their individual emotional and physical states. However, conventional evacuation systems have failed to consider the emotional state of individuals, merely providing uniform evacuation route guidance. As a result, there is a problem in that optimal evacuation cannot be achieved when individuals are experiencing anxiety or tension.

[0359] 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.

[0360] In this invention, the server includes means for acquiring biometric information from the user, means for acquiring the current location using location information technology, and means for analyzing the user's emotional state using a generative AI model. This enables the adjustment of the optimal evacuation route based on the user's individual circumstances and provides guidance that takes their emotions into consideration.

[0361] "User" refers to an individual who receives evacuation route guidance using the system.

[0362] "Biometric information" refers to information that indicates the unique biological or physical characteristics of an individual user, including age, gender, physical ability, and physical limitations.

[0363] "Location-based technology" refers to all technologies used to determine a user's current location, primarily utilizing the Global Positioning System (GPS).

[0364] "Disaster information" refers to information obtained from external databases related to natural disasters and other disasters, and includes data related to the occurrence of weather, earthquakes, tsunamis, etc.

[0365] "Generative AI models" refer to artificial intelligence technologies used to analyze a user's emotional state, and include models that analyze emotions from voice and input data.

[0366] "Emotional state" refers to information that describes the user's psychological state, including sensations and emotions such as anxiety, tension, and composure.

[0367] An "evacuation route" refers to a path established to enable safe and efficient evacuation in the event of a disaster.

[0368] "Adjustment" refers to modifications or changes made when optimizing generated evacuation routes according to the emotional state and physical condition of the users.

[0369] "Guidance" refers to the act of informing and guiding users to evacuation routes through visually or audibly presented information.

[0370] This invention is a system that supports rapid and safe evacuation during disasters, providing personalized evacuation guidance tailored to the user's individual circumstances. The system incorporates an emotion engine equipped with a generative AI model that analyzes the user's emotional state.

[0371] First, during the initial system setup, the user enters biometric information into the terminal. This biometric information includes age, gender, fitness level, and physical limitations. The terminal encrypts this information and sends it to the server. The AES algorithm is used for encryption. The server stores the encrypted data in a database. Standard data management software is used for this database.

[0372] Next, the device periodically obtains the user's current location using GPS technology and sends this location information to the server. The location information is sent to the server in JSON format, and the server stores this in its database.

[0373] Disaster information is retrieved in real time by the server from an external database. A RESTful API is used in this process. The server compares the retrieved disaster information with the user's location information and issues evacuation instructions as needed.

[0374] The emotion engine acquires the user's voice input and input patterns from the device and analyzes their emotional state using a generative AI model. The results of this analysis are sent to a server, which then adjusts evacuation route guidance based on this information.

[0375] As a concrete example, consider a scenario where a user experiences an earthquake and becomes anxious. The emotion engine detects this anxiety, and the server instructs the terminal to provide evacuation guidance in a calmer tone through voice prompts. This allows the user to evacuate calmly.

[0376] An example of a prompt is, "Please describe in detail how to set up an evacuation scenario during a disaster and analyze the emotional state of the users." By inputting this prompt into the AI ​​generation model, an appropriate evacuation guidance method will be generated.

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] The user enters biometric information into the terminal, including age, gender, fitness level, and physical limitations. The terminal encrypts this biometric information using the AES algorithm and securely transmits it to the server. It receives biometric information as input data and generates encrypted data as output. This encrypted data is sent to the server and stored in a database.

[0380] Step 2:

[0381] The device periodically obtains the user's current location using GPS technology. This location information is sent to the server in JSON format as latitude and longitude data. The device receives the current GPS data as input and processes it to send it to the server. The server receives this information and registers it in its database.

[0382] Step 3:

[0383] The server retrieves disaster information from an external database. Using a RESTful API, it saves the retrieved disaster information to the database in real time. The input is external disaster data, which the server processes and compares with the user's location information.

[0384] Step 4:

[0385] The device acquires the user's voice input and input patterns. The emotion engine uses this as input and a generating AI model to analyze the user's emotional state. The analysis results are output as states such as anxiety and tension and sent to the server.

[0386] Step 5:

[0387] The server adjusts evacuation route guidance based on the analysis results received from the emotion engine. It generates optimal evacuation guidance considering the user's emotional state and biometric information. The server receives emotion analysis data and biometric information as input, generates an adjusted evacuation route as output, and sends guidance instructions to the terminal.

[0388] Step 6:

[0389] The terminal provides voice guidance to the user based on instructions received from the server. It adjusts the tone using speech synthesis technology and provides specific evacuation route instructions. The terminal receives instructions from the server as input and provides voice guidance to the user as output. In this process, appropriate tone and route selection enable the terminal to guide the user calmly.

[0390] (Application Example 2)

[0391] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0392] In the event of a disaster, in order for users to evacuate appropriately and quickly, personalized guidance that takes into account users' emotional instability is necessary, in addition to geographical route information. Furthermore, existing systems have difficulty responding to users' changing emotional states, which can result in panic and an inability to take appropriate evacuation actions.

[0393] 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.

[0394] In this invention, the server includes means for acquiring the user's biometric information, means for acquiring the current location using a geographic information system, means for acquiring disaster information from an external database, means for analyzing the user's emotional state and adjusting the content of evacuation guidance based on that analysis, and means for changing the tone of the voice guide according to the emotional state. This enables flexible evacuation guidance tailored to the individual emotional state of the user, thereby realizing safer and more efficient evacuation actions.

[0395] "Biometric information" refers to physiological data such as the user's physical fitness, health status, and heart rate.

[0396] A "geographic information system" is a technology used to determine a user's current location, and it includes map information.

[0397] "Disaster information" refers to data related to natural disasters such as earthquakes and typhoons, and is information obtained from external databases.

[0398] An "evacuation route" refers to a route that allows users to evacuate safely during a disaster, and is generated by a system.

[0399] "User terminal" refers to devices operated by users, such as smartphones and portable information devices.

[0400] "Emotional state" refers to the user's psychological state, such as anxiety or tension, and is analyzed from their voice tone and input patterns.

[0401] "Audio guide" refers to a means of providing instructions and guidance to users through audio.

[0402] To implement this invention, it is necessary to construct an integrated system that provides optimized evacuation guidance during disasters. This system uses smartphones or portable information devices to acquire the user's biometric information. These devices have built-in heart rate sensors and microphones, allowing for real-time acquisition of the user's heart rate and voice data. The acquired voice data is sent to an emotion engine and converted to text using the Google Cloud Speech-to-Text API. Subsequently, the emotional state is analyzed using natural language processing technology based on TensorFlow. Based on the analyzed emotional state, the server personalizes the user's evacuation guidance in real time.

[0403] The geographic information system uses GPS technology to determine the user's current location. This location information is linked with the latest disaster data obtained from a disaster information database, and an optimized evacuation route is generated. The server adjusts the evacuation route while considering the user's physical condition, physical limitations, and emotional state, and provides this information to the terminal as an audio guide. Real-time notifications via voice and text are enabled using services such as Twilio.

[0404] As a concrete example, consider the scenario of an earthquake. Suppose the user's heart rate increases and they feel anxious. At this point, the emotion engine detects the user's anxiety and instructs the device to provide a calming voice guidance. This guidance provides messages such as, "Take a deep breath and calm down. We will guide you to a safe route," reassuring the user.

[0405] Examples of prompt statements to input into a generative AI model include the following:

[0406] "Please share your ideas for an application that determines a user's situation during an earthquake and recommends the calmest and safest evacuation route."

[0407] This system helps users take optimal evacuation actions during disasters, ensuring safe and efficient evacuations.

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] The device uses the smartphone's heart rate sensor and microphone to acquire biometric and voice data. This data is sent to the server as input information. This allows for real-time monitoring of biometric information.

[0411] Step 2:

[0412] The server converts the received audio data into text using the Google Cloud Speech-to-Text API. The input is audio data, and the output is text information. This text data is used as foundational information for sentiment analysis.

[0413] Step 3:

[0414] The server inputs this text information into a generative AI model using TensorFlow to analyze the emotional state. The input is text data, and the output is the user's emotional state (e.g., anxious, stable). This analysis evaluates the user's psychological state.

[0415] Step 4:

[0416] The server uses GPS technology to obtain the user's current location information from a geographic information system. The input is location data, and the output is the user's current latitude and longitude information. This ensures the location information necessary for generating evacuation routes.

[0417] Step 5:

[0418] The server calculates the optimal evacuation route based on disaster information obtained from an external database, along with biometric information, location information, and emotion analysis results. The input is this various data, and the output is the evacuation route. The algorithm generates a safe and efficient route.

[0419] Step 6:

[0420] The terminal uses evacuation route and audio guidance information received from the server to begin providing real-time guidance to the user. In particular, the tone of the audio guidance changes according to the user's emotional state. The input is guidance information from the server, and the output is audio guidance from the terminal to the user. The user can take evacuation actions with confidence thanks to the audio guidance.

[0421] Step 7:

[0422] The server can also use services like Twilio to send additional text notifications at the user's request. The input is instructional information from the server, and the output is a text message to the user. This allows the user to receive situation-specific evacuation instructions.

[0423] 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.

[0424] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[0425] 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.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0439] This invention provides a system for presenting evacuation routes suitable for each user in the event of a disaster. This system combines the user's biometric information, current location information, and real-time acquired disaster information to generate the optimal evacuation route and display it on the user's terminal.

[0440] First, during initial setup, users enter their biometric information, including age, gender, fitness level, and physical limitations, into the device. This information is securely transmitted from the device to a server and later stored in a database. Location information is also periodically updated on the server using the device's GPS function. The server further acquires real-time disaster information from external databases such as the Japan Meteorological Agency, enabling a rapid response when evacuation becomes necessary.

[0441] When a disaster occurs, the server uses an AI algorithm to calculate the optimal evacuation route based on acquired biometric information, location information, and disaster information. In this process, the server can select barrier-free routes that take into account the user's physical strength and limitations, as well as efficient and safe routes that consider traffic congestion and road closures. The generated evacuation route is immediately notified to the terminal, guiding the user through maps and voice prompts.

[0442] For example, suppose a user is affected by an earthquake at home. Because this user has previously registered a mild walking disability, the server prioritizes calculating a route that avoids stairs. Based on this calculation, the evacuation route is displayed on the device along with a map, and specific voice guidance such as "Turn right at the next traffic light" is also provided.

[0443] In this way, the system of the present invention provides evacuation support tailored to the individual circumstances of each user, enabling safer and faster evacuation. This is an important tool for improving the accuracy and speed of evacuation during disasters.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] When a user uses the application for the first time, they enter biometric information such as age, gender, fitness level, and physical limitations into their device. The device then encrypts this information, sends it to a server, and securely stores it in a database.

[0447] Step 2:

[0448] The device periodically uses its GPS function to obtain the user's current location. This location information is sent to a server for updating and used as basic information when generating evacuation routes.

[0449] Step 3:

[0450] The server obtains real-time disaster information through APIs provided by the Japan Meteorological Agency and disaster prevention administrative agencies. This information is then analyzed, and the system determines the necessity of evacuation.

[0451] Step 4:

[0452] In the event of a disaster, the server uses AI algorithms to generate the optimal evacuation route based on the user's biometric information, location information, and disaster information. It also evaluates terrain data and the status of transportation infrastructure to consider safe and rapid evacuation routes.

[0453] Step 5:

[0454] The server sends the generated evacuation route to the terminal. The terminal receives this information and displays and guides the user through the evacuation route using a map and voice prompts.

[0455] Step 6:

[0456] The device continuously tracks the user's location while they are on the move and sends updated data to the server as needed. Based on this data, the server recalculates evacuation routes in real time in response to changing circumstances and resends the latest information to the device.

[0457] (Example 1)

[0458] Next, we will describe 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."

[0459] One challenge during a disaster is the difficulty in quickly providing appropriate evacuation routes based on the individual user's physical characteristics and current location. Conventional methods typically provide uniform evacuation routes, failing to adequately consider the individual circumstances of each user.

[0460] 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.

[0461] In this invention, the server includes means for acquiring biometric information from the user, means for acquiring the current location using a function for measuring location information, and means for acquiring disaster information from an external storage device. This makes it possible to generate an optimal evacuation route tailored to the individual situation, taking into account the user's physical abilities and physical conditions, and to make real-time adjustments along that route.

[0462] "Biometric information" refers to individual physical data such as the user's age, gender, fitness level, and physical limitations.

[0463] "Location information" refers to the user's current geographical location, which is obtained in real time through the device's measurement function.

[0464] "Disaster information" refers to geographically and meteorologically relevant emergency information obtained from external storage devices.

[0465] A "generation processing model" refers to a set of algorithms and programs used to calculate and generate the optimal evacuation route based on acquired information.

[0466] "User terminal" refers to a device or equipment owned by a user for receiving information.

[0467] The "real-time adjustment function" refers to a function that dynamically updates the generated evacuation routes based on the latest information.

[0468] "Physical ability" refers to the characteristics related to the user's physical strength and motor functions.

[0469] "Physical conditions" refer to individual physical characteristics or limitations that affect the user.

[0470] This evacuation route generation system is designed to present the optimal evacuation route for each individual user in the event of a disaster. The system mainly consists of user terminals, a server, and external storage devices.

[0471] The terminal provides an interface for users to input biometric information as part of their initial setup. For example, users can input their age, gender, fitness level, physical limitations, etc. This information is securely transmitted from the terminal to the server.

[0472] Regarding location information, the device uses its built-in positioning function (e.g., GPS) to obtain the user's current location in real time. This location information is also periodically transmitted to the server.

[0473] The server retrieves disaster information from external storage devices in real time based on received biometric and location data. This disaster information is acquired using a publicly available API. The server integrates this information and designs the optimal evacuation route using a generative processing model. Frameworks such as TensorFlow and PyTorch can be used to implement the generative processing model.

[0474] Once the optimal evacuation route is generated, the server sends that information to the user's device. The device then uses this information to provide visual and audio guidance, such as "Turn left at the next intersection."

[0475] For example, if a user experiences an earthquake and has previously registered a mild walking disability, the server will prioritize calculating a route that avoids stairs. The result will then be displayed on the device as a map and audio guide.

[0476] An example of a prompt message might be, "In the event of an earthquake, please provide a barrier-free evacuation route for a user with mild mobility impairment." In this way, the system can provide a quick and safe evacuation route tailored to the individual characteristics and circumstances of each user.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user enters biometric information into the device. Specifically, the user enters their age, gender, fitness level, and physical limitations into a form displayed on the application screen. The entered data is converted to JSON format and sent to the server.

[0480] Input: User's physical information (age, gender, etc.)

[0481] Output: Biometric data in JSON format

[0482] Step 2:

[0483] The device measures location information. Using its built-in GPS function, the device periodically measures the user's current location. The measured location information is transmitted to the server in real time.

[0484] Input: GPS signal

[0485] Output: Real-time location information (latitude, longitude)

[0486] Step 3:

[0487] The server retrieves disaster information from external storage devices. The server obtains the necessary disaster information via a public API using GET requests. The retrieved data is analyzed, and the server updates the information regarding the current disaster situation.

[0488] Input: API Request

[0489] Output: Analyzed disaster information

[0490] Step 4:

[0491] The server generates the optimal evacuation route using a generative processing model. An AI algorithm calculates the evacuation route using acquired biometric information, location information, and disaster information as input. Optimization is performed using a deep learning library during this process.

[0492] Input: Biometric information, real-time location information, disaster information

[0493] Output: Optimized evacuation routes

[0494] Step 5:

[0495] The server sends the generated evacuation route to the user's terminal. The data is transmitted to the terminal via a secure communication protocol.

[0496] Input: Evacuation route data

[0497] Output: Route information displayed on the terminal

[0498] Step 6:

[0499] The device displays the evacuation route and begins voice guidance. It generates a map using a map SDK and visually displays the route. It also provides voice guidance using text-to-speech functionality.

[0500] Input: Received evacuation route data

[0501] Output: Map display and voice route guidance

[0502] (Application Example 1)

[0503] Next, we will explain Application Example 1. In the following explanation, 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."

[0504] During disaster evacuations, there is a need to provide optimal evacuation routes based on each individual's physical characteristics and location. However, conventional technologies have struggled to provide real-time information updates and to perform complex route adjustments that take into account traffic conditions and the congestion level of evacuation centers. As a result, there is a problem in that appropriate evacuation routes are not provided, making efficient evacuation difficult.

[0505] 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.

[0506] In this invention, the server includes means for acquiring biological data from users, means for acquiring geographic location using geographic information technology, and means for acquiring disaster information from external sources. This enables the dynamic generation and adjustment of evacuation routes according to individual physical abilities and circumstances, allowing for rapid and safe evacuation.

[0507] "Biological data" refers to personal biometric information such as the user's age, gender, athletic ability, and health status.

[0508] "Geographic information technology" refers to technologies for acquiring and processing geographical location and route information. Examples include GPS and digital map services.

[0509] "Geographic location" refers to coordinate information that indicates the user's current physical location.

[0510] "Disaster information" refers to real-time information about natural disasters such as weather and earthquakes. It is obtained from external sources.

[0511] "External information sources" refer to information provided from databases or APIs outside the system.

[0512] An "evacuation route" is a recommended route for users to evacuate safely and quickly in the event of a disaster.

[0513] "User devices" refer to devices used to display evacuation route information and provide voice guidance. Specific examples include smartphones and tablets.

[0514] "Dynamic adjustment" refers to modifying evacuation plans in real time as needed, based on changes in circumstances and new information.

[0515] "Information and communication technology" refers to all technologies that utilize computers and networks to process and transmit information.

[0516] To realize this invention, the user's mobile device plays a crucial role. Specifically, a device such as a smartphone functions as hardware for inputting the user's biological data and acquiring location information. This device uses geographic information technology to track geographic location in real time. A specific example is the GPS function.

[0517] The server executes an AI algorithm developed using Python, integrating various pieces of information to calculate the optimal evacuation route. The server is built as a cloud service, enabling scalable processing using, for example, AWS Lambda. The server also has the capability to acquire data in real time from external sources, such as the Japan Meteorological Agency and other disaster information services.

[0518] Evacuation routes are individually customized to take into account the user's motor skills and physical limitations. This process is automated by an AI model, and the generated route information is transmitted to the user's device, where it is guided visually and audibly. Evacuation routes are updated in real time using information and communication technology, and are dynamically adjusted according to road conditions and the congestion level of evacuation shelters.

[0519] For example, in the event of heavy rain or a sudden earthquake, the server quickly recalculates evacuation routes and guides the user's device to a shelter. At this time, prompts such as, "You need to evacuate from your current location. Please tell me the optimal route considering your physical condition and road conditions," are used, allowing the user to input necessary information into their terminal and take appropriate action. This enables rapid and safe evacuation tailored to individual needs.

[0520] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0521] Step 1:

[0522] The user enters biological data into the terminal. This data includes age, gender, and athletic ability. The entered data is securely transmitted from the terminal to the server and stored in a database. The input here is the user's personal information, and the output is the process of saving it to the server's database.

[0523] Step 2:

[0524] The user's device uses GPS functionality to obtain its current geographical location. This location data is periodically transmitted to the server. The input is the location information obtained by the device, and the output is the transmission to the server.

[0525] Step 3:

[0526] The server retrieves real-time disaster information from external sources. This process involves retrieving data via APIs from the Japan Meteorological Agency and disaster information service providers. The input is disaster information from external sources, and the output is the data retrieved to the server.

[0527] Step 4:

[0528] The server uses a generated AI model to calculate the optimal evacuation route by combining biological data, geographical location, and disaster information. The inputs are personal data saved in step 1, location data obtained in step 2, and disaster information obtained in step 3, and the output is the calculated evacuation route.

[0529] Step 5:

[0530] The server calculates the evacuation route and transmits it to the terminal, which then presents it to the user through visual and audio guidance. The input is the evacuation route data from the server, and the output is the display and audio guidance on the user's terminal.

[0531] Step 6:

[0532] The server uses information and communication technology to update evacuation routes in real time. It dynamically adjusts routes according to changing conditions, taking into account traffic information and the congestion level of evacuation shelters. The input is traffic data and evacuation shelter information, and the output is the updated evacuation route.

[0533] 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.

[0534] This invention is a system designed to support rapid and safe evacuation during disasters. By combining it with an emotion engine that monitors and analyzes the user's emotional state, it provides more personalized evacuation guidance. This emotion engine allows the system to sense the user's anxiety and tension and present appropriate evacuation guidance accordingly.

[0535] First, the user enters biometric information into the device during the initial system setup. This information includes age, gender, fitness level, and physical limitations. The device encrypts this information and sends it to a server for storage in a database. Furthermore, the device periodically obtains the user's current location using GPS and sends this information to the server, making it possible to track the user's location.

[0536] Even under normal circumstances, the server has a system in place to obtain disaster information in real time by coordinating effectively with the Japan Meteorological Agency and disaster prevention administrative information agencies. Based on this information, the server determines the need for user evacuation in the event of a disaster and immediately begins taking action.

[0537] A distinctive feature of this invention is that the emotion engine analyzes the user's emotions from their voice and input patterns. Based on this analysis, the server can adjust the method of providing evacuation route guidance. For example, if the system determines that the user is emotionally unstable, it can provide appropriate feedback, such as delivering guidance comments in a calm voice.

[0538] As a concrete example, consider a scenario where a user experiences an earthquake and their anxiety and panic increase. The emotion engine detects this and instructs the terminal to provide calm guidance appropriate to the situation. In this case, the terminal plays the voice guidance in a calmer tone than usual, guiding the user to safety. Furthermore, users who need to avoid stairs are given priority guidance to elevators or barrier-free routes.

[0539] In this way, the present invention provides a personalized evacuation route based on the user's individual circumstances and emotional state, thereby realizing a safer and more comfortable evacuation experience.

[0540] The following describes the processing flow.

[0541] Step 1:

[0542] When a user sets up the application for the first time, they enter biometric information (age, gender, physical fitness, physical limitations, etc.) into the device. The device encrypts this data, sends it to a server, and securely stores it in a database.

[0543] Step 2:

[0544] The device periodically uses GPS functionality to obtain the user's current location. This location information is transmitted to a server and used to quickly and accurately generate evacuation routes in the event of a disaster.

[0545] Step 3:

[0546] The server retrieves real-time disaster information from an external database. This information includes earthquakes, tsunamis, and floods, and the system uses this information to determine the necessity of evacuation.

[0547] Step 4:

[0548] When a disaster occurs, the server uses an AI algorithm to calculate the optimal evacuation route based on the user's biometric information, current location, and disaster information. This calculation also evaluates terrain data and transportation infrastructure to aim for safer and faster evacuation.

[0549] Step 5:

[0550] The server sends the calculated evacuation route to the terminal. The terminal then displays the route on a map and provides voice guidance to the user.

[0551] Step 6:

[0552] The emotion engine analyzes the user's emotions from their voice input and operation patterns. For example, if the user's voice is trembling, the emotion engine detects anxiety or fear.

[0553] Step 7:

[0554] The server adjusts evacuation instructions based on feedback from the emotion engine. For example, it might soften the tone of the voice guidance on the device or simplify the message to enhance the user's sense of security.

[0555] Step 8:

[0556] The device continuously monitors the user's emotions and location in real time. If the server detects a change in the situation, it immediately recalculates the evacuation route and provides the user with updated information.

[0557] This allows the system to provide optimal evacuation support tailored to the user's situation, resulting in a safer and more secure evacuation experience.

[0558] (Example 2)

[0559] Next, we will describe 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."

[0560] In the event of a disaster, it is essential to ensure the rapid and safe evacuation of individuals, taking into account their individual emotional and physical states. However, conventional evacuation systems have failed to consider the emotional state of individuals, merely providing uniform evacuation route guidance. As a result, there is a problem in that optimal evacuation cannot be achieved when individuals are experiencing anxiety or tension.

[0561] 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.

[0562] In this invention, the server includes means for acquiring biometric information from the user, means for acquiring the current location using location information technology, and means for analyzing the user's emotional state using a generative AI model. This enables the adjustment of the optimal evacuation route based on the user's individual circumstances and provides guidance that takes their emotions into consideration.

[0563] "User" refers to an individual who receives evacuation route guidance using the system.

[0564] "Biometric information" refers to information that indicates the unique biological or physical characteristics of an individual user, including age, gender, physical ability, and physical limitations.

[0565] "Location-based technology" refers to all technologies used to determine a user's current location, primarily utilizing the Global Positioning System (GPS).

[0566] "Disaster information" refers to information obtained from external databases related to natural disasters and other disasters, and includes data related to the occurrence of weather, earthquakes, tsunamis, etc.

[0567] "Generative AI models" refer to artificial intelligence technologies used to analyze a user's emotional state, and include models that analyze emotions from voice and input data.

[0568] "Emotional state" refers to information that describes the user's psychological state, including sensations and emotions such as anxiety, tension, and composure.

[0569] An "evacuation route" refers to a path established to enable safe and efficient evacuation in the event of a disaster.

[0570] "Adjustment" refers to modifications or changes made when optimizing generated evacuation routes according to the emotional state and physical condition of the users.

[0571] "Guidance" refers to the act of informing and guiding users to evacuation routes through visually or audibly presented information.

[0572] This invention is a system that supports rapid and safe evacuation during disasters, providing personalized evacuation guidance tailored to the user's individual circumstances. The system incorporates an emotion engine equipped with a generative AI model that analyzes the user's emotional state.

[0573] First, during the initial system setup, the user enters biometric information into the terminal. This biometric information includes age, gender, fitness level, and physical limitations. The terminal encrypts this information and sends it to the server. The AES algorithm is used for encryption. The server stores the encrypted data in a database. Standard data management software is used for this database.

[0574] Next, the device periodically obtains the user's current location using GPS technology and sends this location information to the server. The location information is sent to the server in JSON format, and the server stores this in its database.

[0575] Disaster information is retrieved in real time by the server from an external database. A RESTful API is used in this process. The server compares the retrieved disaster information with the user's location information and issues evacuation instructions as needed.

[0576] The emotion engine acquires the user's voice input and input patterns from the device and analyzes their emotional state using a generative AI model. The results of this analysis are sent to a server, which then adjusts evacuation route guidance based on this information.

[0577] As a concrete example, consider a scenario where a user experiences an earthquake and becomes anxious. The emotion engine detects this anxiety, and the server instructs the terminal to provide evacuation guidance in a calmer tone through voice prompts. This allows the user to evacuate calmly.

[0578] An example of a prompt is, "Please describe in detail how to set up an evacuation scenario during a disaster and analyze the emotional state of the users." By inputting this prompt into the AI ​​generation model, an appropriate evacuation guidance method will be generated.

[0579] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0580] Step 1:

[0581] The user enters biometric information into the terminal, including age, gender, fitness level, and physical limitations. The terminal encrypts this biometric information using the AES algorithm and securely transmits it to the server. It receives biometric information as input data and generates encrypted data as output. This encrypted data is sent to the server and stored in a database.

[0582] Step 2:

[0583] The device periodically obtains the user's current location using GPS technology. This location information is sent to the server in JSON format as latitude and longitude data. The device receives the current GPS data as input and processes it to send it to the server. The server receives this information and registers it in its database.

[0584] Step 3:

[0585] The server retrieves disaster information from an external database. Using a RESTful API, it saves the retrieved disaster information to the database in real time. The input is external disaster data, which the server processes and compares with the user's location information.

[0586] Step 4:

[0587] The device acquires the user's voice input and input patterns. The emotion engine uses this as input and a generating AI model to analyze the user's emotional state. The analysis results are output as states such as anxiety and tension and sent to the server.

[0588] Step 5:

[0589] The server adjusts evacuation route guidance based on the analysis results received from the emotion engine. It generates optimal evacuation guidance considering the user's emotional state and biometric information. The server receives emotion analysis data and biometric information as input, generates an adjusted evacuation route as output, and sends guidance instructions to the terminal.

[0590] Step 6:

[0591] The terminal provides voice guidance to the user based on instructions received from the server. It adjusts the tone using speech synthesis technology and provides specific evacuation route instructions. The terminal receives instructions from the server as input and provides voice guidance to the user as output. In this process, appropriate tone and route selection enable the terminal to guide the user calmly.

[0592] (Application Example 2)

[0593] Next, we will explain application example 2. In the following explanation, 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."

[0594] In the event of a disaster, in order for users to evacuate appropriately and quickly, personalized guidance that takes into account users' emotional instability is necessary, in addition to geographical route information. Furthermore, existing systems have difficulty responding to users' changing emotional states, which can result in panic and an inability to take appropriate evacuation actions.

[0595] 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.

[0596] In this invention, the server includes means for acquiring the user's biometric information, means for acquiring the current location using a geographic information system, means for acquiring disaster information from an external database, means for analyzing the user's emotional state and adjusting the content of evacuation guidance based on that analysis, and means for changing the tone of the voice guide according to the emotional state. This enables flexible evacuation guidance tailored to the individual emotional state of the user, thereby realizing safer and more efficient evacuation actions.

[0597] "Biometric information" refers to physiological data such as the user's physical fitness, health status, and heart rate.

[0598] A "geographic information system" is a technology used to determine a user's current location, and it includes map information.

[0599] "Disaster information" refers to data related to natural disasters such as earthquakes and typhoons, and is information obtained from external databases.

[0600] An "evacuation route" refers to a route that allows users to evacuate safely during a disaster, and is generated by a system.

[0601] "User terminal" refers to devices operated by users, such as smartphones and portable information devices.

[0602] "Emotional state" refers to the user's psychological state, such as anxiety or tension, and is analyzed from their voice tone and input patterns.

[0603] "Audio guide" refers to a means of providing instructions and guidance to users through audio.

[0604] To implement this invention, it is necessary to construct an integrated system that provides optimized evacuation guidance during disasters. This system uses smartphones or portable information devices to acquire the user's biometric information. These devices have built-in heart rate sensors and microphones, allowing for real-time acquisition of the user's heart rate and voice data. The acquired voice data is sent to an emotion engine and converted to text using the Google Cloud Speech-to-Text API. Subsequently, the emotional state is analyzed using natural language processing technology based on TensorFlow. Based on the analyzed emotional state, the server personalizes the user's evacuation guidance in real time.

[0605] The geographic information system uses GPS technology to determine the user's current location. This location information is linked with the latest disaster data obtained from a disaster information database, and an optimized evacuation route is generated. The server adjusts the evacuation route while considering the user's physical condition, physical limitations, and emotional state, and provides this information to the terminal as an audio guide. Real-time notifications via voice and text are enabled using services such as Twilio.

[0606] As a concrete example, consider the scenario of an earthquake. Suppose the user's heart rate increases and they feel anxious. At this point, the emotion engine detects the user's anxiety and instructs the device to provide a calming voice guidance. This guidance provides messages such as, "Take a deep breath and calm down. We will guide you to a safe route," reassuring the user.

[0607] Examples of prompt statements to input into a generative AI model include the following:

[0608] "Please share your ideas for an application that determines a user's situation during an earthquake and recommends the calmest and safest evacuation route."

[0609] This system helps users take optimal evacuation actions during disasters, ensuring safe and efficient evacuations.

[0610] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0611] Step 1:

[0612] The device uses the smartphone's heart rate sensor and microphone to acquire biometric and voice data. This data is sent to the server as input information. This allows for real-time monitoring of biometric information.

[0613] Step 2:

[0614] The server converts the received audio data into text using the Google Cloud Speech-to-Text API. The input is audio data, and the output is text information. This text data is used as foundational information for sentiment analysis.

[0615] Step 3:

[0616] The server inputs this text information into a generative AI model using TensorFlow to analyze the emotional state. The input is text data, and the output is the user's emotional state (e.g., anxious, stable). This analysis evaluates the user's psychological state.

[0617] Step 4:

[0618] The server uses GPS technology to obtain the user's current location information from a geographic information system. The input is location data, and the output is the user's current latitude and longitude information. This ensures the location information necessary for generating evacuation routes.

[0619] Step 5:

[0620] The server calculates the optimal evacuation route based on disaster information obtained from an external database, along with biometric information, location information, and emotion analysis results. The input is this various data, and the output is the evacuation route. The algorithm generates a safe and efficient route.

[0621] Step 6:

[0622] The terminal uses evacuation route and audio guidance information received from the server to begin providing real-time guidance to the user. In particular, the tone of the audio guidance changes according to the user's emotional state. The input is guidance information from the server, and the output is audio guidance from the terminal to the user. The user can take evacuation actions with confidence thanks to the audio guidance.

[0623] Step 7:

[0624] The server can also use services like Twilio to send additional text notifications at the user's request. The input is instructional information from the server, and the output is a text message to the user. This allows the user to receive situation-specific evacuation instructions.

[0625] 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.

[0626] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[0627] 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.

[0628] [Fourth Embodiment]

[0629] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0630] 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.

[0631] 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).

[0632] 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.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0642] This invention provides a system for presenting evacuation routes suitable for each user in the event of a disaster. This system combines the user's biometric information, current location information, and real-time acquired disaster information to generate the optimal evacuation route and display it on the user's terminal.

[0643] First, during initial setup, users enter their biometric information, including age, gender, fitness level, and physical limitations, into the device. This information is securely transmitted from the device to a server and later stored in a database. Location information is also periodically updated on the server using the device's GPS function. The server further acquires real-time disaster information from external databases such as the Japan Meteorological Agency, enabling a rapid response when evacuation becomes necessary.

[0644] When a disaster occurs, the server uses an AI algorithm to calculate the optimal evacuation route based on acquired biometric information, location information, and disaster information. In this process, the server can select barrier-free routes that take into account the user's physical strength and limitations, as well as efficient and safe routes that consider traffic congestion and road closures. The generated evacuation route is immediately notified to the terminal, guiding the user through maps and voice prompts.

[0645] For example, suppose a user is affected by an earthquake at home. Because this user has previously registered a mild walking disability, the server prioritizes calculating a route that avoids stairs. Based on this calculation, the evacuation route is displayed on the device along with a map, and specific voice guidance such as "Turn right at the next traffic light" is also provided.

[0646] In this way, the system of the present invention provides evacuation support tailored to the individual circumstances of each user, enabling safer and faster evacuation. This is an important tool for improving the accuracy and speed of evacuation during disasters.

[0647] The following describes the processing flow.

[0648] Step 1:

[0649] When a user uses the application for the first time, they enter biometric information such as age, gender, fitness level, and physical limitations into their device. The device then encrypts this information, sends it to a server, and securely stores it in a database.

[0650] Step 2:

[0651] The device periodically uses its GPS function to obtain the user's current location. This location information is sent to a server for updating and used as basic information when generating evacuation routes.

[0652] Step 3:

[0653] The server obtains real-time disaster information through APIs provided by the Japan Meteorological Agency and disaster prevention administrative agencies. This information is then analyzed, and the system determines the necessity of evacuation.

[0654] Step 4:

[0655] In the event of a disaster, the server uses AI algorithms to generate the optimal evacuation route based on the user's biometric information, location information, and disaster information. It also evaluates terrain data and the status of transportation infrastructure to consider safe and rapid evacuation routes.

[0656] Step 5:

[0657] The server sends the generated evacuation route to the terminal. The terminal receives this information and displays and guides the user through the evacuation route using a map and voice prompts.

[0658] Step 6:

[0659] The device continuously tracks the user's location while they are on the move and sends updated data to the server as needed. Based on this data, the server recalculates evacuation routes in real time in response to changing circumstances and resends the latest information to the device.

[0660] (Example 1)

[0661] Next, we will describe 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".

[0662] One challenge during a disaster is the difficulty in quickly providing appropriate evacuation routes based on the individual user's physical characteristics and current location. Conventional methods typically provide uniform evacuation routes, failing to adequately consider the individual circumstances of each user.

[0663] 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.

[0664] In this invention, the server includes means for acquiring biometric information from the user, means for acquiring the current location using a function for measuring location information, and means for acquiring disaster information from an external storage device. This makes it possible to generate an optimal evacuation route tailored to the individual situation, taking into account the user's physical abilities and physical conditions, and to make real-time adjustments along that route.

[0665] "Biometric information" refers to individual physical data such as the user's age, gender, fitness level, and physical limitations.

[0666] "Location information" refers to the user's current geographical location, which is obtained in real time through the device's measurement function.

[0667] "Disaster information" refers to geographically and meteorologically relevant emergency information obtained from external storage devices.

[0668] A "generation processing model" refers to a set of algorithms and programs used to calculate and generate the optimal evacuation route based on acquired information.

[0669] "User terminal" refers to a device or equipment owned by a user for receiving information.

[0670] The "real-time adjustment function" refers to a function that dynamically updates the generated evacuation routes based on the latest information.

[0671] "Physical ability" refers to the characteristics related to the user's physical strength and motor functions.

[0672] "Physical conditions" refer to individual physical characteristics or limitations that affect the user.

[0673] This evacuation route generation system is designed to present the optimal evacuation route for each individual user in the event of a disaster. The system mainly consists of user terminals, a server, and external storage devices.

[0674] The terminal provides an interface for users to input biometric information as part of their initial setup. For example, users can input their age, gender, fitness level, physical limitations, etc. This information is securely transmitted from the terminal to the server.

[0675] Regarding location information, the device uses its built-in positioning function (e.g., GPS) to obtain the user's current location in real time. This location information is also periodically transmitted to the server.

[0676] The server retrieves disaster information from external storage devices in real time based on received biometric and location data. This disaster information is acquired using a publicly available API. The server integrates this information and designs the optimal evacuation route using a generative processing model. Frameworks such as TensorFlow and PyTorch can be used to implement the generative processing model.

[0677] Once the optimal evacuation route is generated, the server sends that information to the user's device. The device then uses this information to provide visual and audio guidance, such as "Turn left at the next intersection."

[0678] For example, if a user experiences an earthquake and has previously registered a mild walking disability, the server will prioritize calculating a route that avoids stairs. The result will then be displayed on the device as a map and audio guide.

[0679] An example of a prompt message might be, "In the event of an earthquake, please provide a barrier-free evacuation route for a user with mild mobility impairment." In this way, the system can provide a quick and safe evacuation route tailored to the individual characteristics and circumstances of each user.

[0680] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0681] Step 1:

[0682] The user enters biometric information into the device. Specifically, the user enters their age, gender, fitness level, and physical limitations into a form displayed on the application screen. The entered data is converted to JSON format and sent to the server.

[0683] Input: User's physical information (age, gender, etc.)

[0684] Output: Biometric data in JSON format

[0685] Step 2:

[0686] The device measures location information. Using its built-in GPS function, the device periodically measures the user's current location. The measured location information is transmitted to the server in real time.

[0687] Input: GPS signal

[0688] Output: Real-time location information (latitude, longitude)

[0689] Step 3:

[0690] The server retrieves disaster information from external storage devices. The server obtains the necessary disaster information via a public API using GET requests. The retrieved data is analyzed, and the server updates the information regarding the current disaster situation.

[0691] Input: API Request

[0692] Output: Analyzed disaster information

[0693] Step 4:

[0694] The server generates the optimal evacuation route using a generative processing model. An AI algorithm calculates the evacuation route using acquired biometric information, location information, and disaster information as input. Optimization is performed using a deep learning library during this process.

[0695] Input: Biometric information, real-time location information, disaster information

[0696] Output: Optimized evacuation routes

[0697] Step 5:

[0698] The server sends the generated evacuation route to the user's terminal. The data is transmitted to the terminal via a secure communication protocol.

[0699] Input: Evacuation route data

[0700] Output: Route information displayed on the terminal

[0701] Step 6:

[0702] The device displays the evacuation route and begins voice guidance. It generates a map using a map SDK and visually displays the route. It also provides voice guidance using text-to-speech functionality.

[0703] Input: Received evacuation route data

[0704] Output: Map display and voice route guidance

[0705] (Application Example 1)

[0706] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0707] During disaster evacuations, there is a need to provide optimal evacuation routes based on each individual's physical characteristics and location. However, conventional technologies have struggled to provide real-time information updates and to perform complex route adjustments that take into account traffic conditions and the congestion level of evacuation centers. As a result, there is a problem in that appropriate evacuation routes are not provided, making efficient evacuation difficult.

[0708] 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.

[0709] In this invention, the server includes means for acquiring biological data from users, means for acquiring geographic location using geographic information technology, and means for acquiring disaster information from external sources. This enables the dynamic generation and adjustment of evacuation routes according to individual physical abilities and circumstances, allowing for rapid and safe evacuation.

[0710] "Biological data" refers to personal biometric information such as the user's age, gender, athletic ability, and health status.

[0711] "Geographic information technology" refers to technologies for acquiring and processing geographical location and route information. Examples include GPS and digital map services.

[0712] "Geographic location" refers to coordinate information that indicates the user's current physical location.

[0713] "Disaster information" refers to real-time information about natural disasters such as weather and earthquakes. It is obtained from external sources.

[0714] "External information sources" refer to information provided from databases or APIs outside the system.

[0715] An "evacuation route" is a recommended route for users to evacuate safely and quickly in the event of a disaster.

[0716] "User devices" refer to devices used to display evacuation route information and provide voice guidance. Specific examples include smartphones and tablets.

[0717] "Dynamic adjustment" refers to modifying evacuation plans in real time as needed, based on changes in circumstances and new information.

[0718] "Information and communication technology" refers to all technologies that utilize computers and networks to process and transmit information.

[0719] To realize this invention, the user's mobile device plays a crucial role. Specifically, a device such as a smartphone functions as hardware for inputting the user's biological data and acquiring location information. This device uses geographic information technology to track geographic location in real time. A specific example is the GPS function.

[0720] The server executes an AI algorithm developed using Python, integrating various pieces of information to calculate the optimal evacuation route. The server is built as a cloud service, enabling scalable processing using, for example, AWS Lambda. The server also has the capability to acquire data in real time from external sources, such as the Japan Meteorological Agency and other disaster information services.

[0721] Evacuation routes are individually customized to take into account the user's motor skills and physical limitations. This process is automated by an AI model, and the generated route information is transmitted to the user's device, where it is guided visually and audibly. Evacuation routes are updated in real time using information and communication technology, and are dynamically adjusted according to road conditions and the congestion level of evacuation shelters.

[0722] For example, in the event of heavy rain or a sudden earthquake, the server quickly recalculates evacuation routes and guides the user's device to a shelter. At this time, prompts such as, "You need to evacuate from your current location. Please tell me the optimal route considering your physical condition and road conditions," are used, allowing the user to input necessary information into their terminal and take appropriate action. This enables rapid and safe evacuation tailored to individual needs.

[0723] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0724] Step 1:

[0725] The user enters biological data into the terminal. This data includes age, gender, and athletic ability. The entered data is securely transmitted from the terminal to the server and stored in a database. The input here is the user's personal information, and the output is the process of saving it to the server's database.

[0726] Step 2:

[0727] The user's device uses GPS functionality to obtain its current geographical location. This location data is periodically transmitted to the server. The input is the location information obtained by the device, and the output is the transmission to the server.

[0728] Step 3:

[0729] The server retrieves real-time disaster information from external sources. This process involves retrieving data via APIs from the Japan Meteorological Agency and disaster information service providers. The input is disaster information from external sources, and the output is the data retrieved to the server.

[0730] Step 4:

[0731] The server uses a generated AI model to calculate the optimal evacuation route by combining biological data, geographical location, and disaster information. The inputs are personal data saved in step 1, location data obtained in step 2, and disaster information obtained in step 3, and the output is the calculated evacuation route.

[0732] Step 5:

[0733] The server calculates the evacuation route and transmits it to the terminal, which then presents it to the user through visual and audio guidance. The input is the evacuation route data from the server, and the output is the display and audio guidance on the user's terminal.

[0734] Step 6:

[0735] The server uses information and communication technology to update evacuation routes in real time. It dynamically adjusts routes according to changing conditions, taking into account traffic information and the congestion level of evacuation shelters. The input is traffic data and evacuation shelter information, and the output is the updated evacuation route.

[0736] 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.

[0737] This invention is a system designed to support rapid and safe evacuation during disasters. By combining it with an emotion engine that monitors and analyzes the user's emotional state, it provides more personalized evacuation guidance. This emotion engine allows the system to sense the user's anxiety and tension and present appropriate evacuation guidance accordingly.

[0738] First, the user enters biometric information into the device during the initial system setup. This information includes age, gender, fitness level, and physical limitations. The device encrypts this information and sends it to a server for storage in a database. Furthermore, the device periodically obtains the user's current location using GPS and sends this information to the server, making it possible to track the user's location.

[0739] Even under normal circumstances, the server has a system in place to obtain disaster information in real time by coordinating effectively with the Japan Meteorological Agency and disaster prevention administrative information agencies. Based on this information, the server determines the need for user evacuation in the event of a disaster and immediately begins taking action.

[0740] A distinctive feature of this invention is that the emotion engine analyzes the user's emotions from their voice and input patterns. Based on this analysis, the server can adjust the method of providing evacuation route guidance. For example, if the system determines that the user is emotionally unstable, it can provide appropriate feedback, such as delivering guidance comments in a calm voice.

[0741] As a concrete example, consider a scenario where a user experiences an earthquake and their anxiety and panic increase. The emotion engine detects this and instructs the terminal to provide calm guidance appropriate to the situation. In this case, the terminal plays the voice guidance in a calmer tone than usual, guiding the user to safety. Furthermore, users who need to avoid stairs are given priority guidance to elevators or barrier-free routes.

[0742] In this way, the present invention provides a personalized evacuation route based on the user's individual circumstances and emotional state, thereby realizing a safer and more comfortable evacuation experience.

[0743] The following describes the processing flow.

[0744] Step 1:

[0745] When a user sets up the application for the first time, they enter biometric information (age, gender, physical fitness, physical limitations, etc.) into the device. The device encrypts this data, sends it to a server, and securely stores it in a database.

[0746] Step 2:

[0747] The device periodically uses GPS functionality to obtain the user's current location. This location information is transmitted to a server and used to quickly and accurately generate evacuation routes in the event of a disaster.

[0748] Step 3:

[0749] The server retrieves real-time disaster information from an external database. This information includes earthquakes, tsunamis, and floods, and the system uses this information to determine the necessity of evacuation.

[0750] Step 4:

[0751] When a disaster occurs, the server uses an AI algorithm to calculate the optimal evacuation route based on the user's biometric information, current location, and disaster information. This calculation also evaluates terrain data and transportation infrastructure to aim for safer and faster evacuation.

[0752] Step 5:

[0753] The server sends the calculated evacuation route to the terminal. The terminal then displays the route on a map and provides voice guidance to the user.

[0754] Step 6:

[0755] The emotion engine analyzes the user's emotions from their voice input and operation patterns. For example, if the user's voice is trembling, the emotion engine detects anxiety or fear.

[0756] Step 7:

[0757] The server adjusts evacuation instructions based on feedback from the emotion engine. For example, it might soften the tone of the voice guidance on the device or simplify the message to enhance the user's sense of security.

[0758] Step 8:

[0759] The device continuously monitors the user's emotions and location in real time. If the server detects a change in the situation, it immediately recalculates the evacuation route and provides the user with updated information.

[0760] This allows the system to provide optimal evacuation support tailored to the user's situation, resulting in a safer and more secure evacuation experience.

[0761] (Example 2)

[0762] Next, we will describe 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".

[0763] In the event of a disaster, it is essential to ensure the rapid and safe evacuation of individuals, taking into account their individual emotional and physical states. However, conventional evacuation systems have failed to consider the emotional state of individuals, merely providing uniform evacuation route guidance. As a result, there is a problem in that optimal evacuation cannot be achieved when individuals are experiencing anxiety or tension.

[0764] 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.

[0765] In this invention, the server includes means for acquiring biometric information from the user, means for acquiring the current location using location information technology, and means for analyzing the user's emotional state using a generative AI model. This enables the adjustment of the optimal evacuation route based on the user's individual circumstances and provides guidance that takes their emotions into consideration.

[0766] "User" refers to an individual who receives evacuation route guidance using the system.

[0767] "Biometric information" refers to information that indicates the unique biological or physical characteristics of an individual user, including age, gender, physical ability, and physical limitations.

[0768] "Location-based technology" refers to all technologies used to determine a user's current location, primarily utilizing the Global Positioning System (GPS).

[0769] "Disaster information" refers to information obtained from external databases related to natural disasters and other disasters, and includes data related to the occurrence of weather, earthquakes, tsunamis, etc.

[0770] "Generative AI models" refer to artificial intelligence technologies used to analyze a user's emotional state, and include models that analyze emotions from voice and input data.

[0771] "Emotional state" refers to information that describes the user's psychological state, including sensations and emotions such as anxiety, tension, and composure.

[0772] An "evacuation route" refers to a path established to enable safe and efficient evacuation in the event of a disaster.

[0773] "Adjustment" refers to modifications or changes made when optimizing generated evacuation routes according to the emotional state and physical condition of the users.

[0774] "Guidance" refers to the act of informing and guiding users to evacuation routes through visually or audibly presented information.

[0775] This invention is a system that supports rapid and safe evacuation during disasters, providing personalized evacuation guidance tailored to the user's individual circumstances. The system incorporates an emotion engine equipped with a generative AI model that analyzes the user's emotional state.

[0776] First, during the initial system setup, the user enters biometric information into the terminal. This biometric information includes age, gender, fitness level, and physical limitations. The terminal encrypts this information and sends it to the server. The AES algorithm is used for encryption. The server stores the encrypted data in a database. Standard data management software is used for this database.

[0777] Next, the device periodically obtains the user's current location using GPS technology and sends this location information to the server. The location information is sent to the server in JSON format, and the server stores this in its database.

[0778] Disaster information is retrieved in real time by the server from an external database. A RESTful API is used in this process. The server compares the retrieved disaster information with the user's location information and issues evacuation instructions as needed.

[0779] The emotion engine acquires the user's voice input and input patterns from the device and analyzes their emotional state using a generative AI model. The results of this analysis are sent to a server, which then adjusts evacuation route guidance based on this information.

[0780] As a concrete example, consider a scenario where a user experiences an earthquake and becomes anxious. The emotion engine detects this anxiety, and the server instructs the terminal to provide evacuation guidance in a calmer tone through voice prompts. This allows the user to evacuate calmly.

[0781] An example of a prompt is, "Please describe in detail how to set up an evacuation scenario during a disaster and analyze the emotional state of the users." By inputting this prompt into the AI ​​generation model, an appropriate evacuation guidance method will be generated.

[0782] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0783] Step 1:

[0784] The user enters biometric information into the terminal, including age, gender, fitness level, and physical limitations. The terminal encrypts this biometric information using the AES algorithm and securely transmits it to the server. It receives biometric information as input data and generates encrypted data as output. This encrypted data is sent to the server and stored in a database.

[0785] Step 2:

[0786] The device periodically obtains the user's current location using GPS technology. This location information is sent to the server in JSON format as latitude and longitude data. The device receives the current GPS data as input and processes it to send it to the server. The server receives this information and registers it in its database.

[0787] Step 3:

[0788] The server retrieves disaster information from an external database. Using a RESTful API, it saves the retrieved disaster information to the database in real time. The input is external disaster data, which the server processes and compares with the user's location information.

[0789] Step 4:

[0790] The device acquires the user's voice input and input patterns. The emotion engine uses this as input and a generating AI model to analyze the user's emotional state. The analysis results are output as states such as anxiety and tension and sent to the server.

[0791] Step 5:

[0792] The server adjusts evacuation route guidance based on the analysis results received from the emotion engine. It generates optimal evacuation guidance considering the user's emotional state and biometric information. The server receives emotion analysis data and biometric information as input, generates an adjusted evacuation route as output, and sends guidance instructions to the terminal.

[0793] Step 6:

[0794] The terminal provides voice guidance to the user based on instructions received from the server. It adjusts the tone using speech synthesis technology and provides specific evacuation route instructions. The terminal receives instructions from the server as input and provides voice guidance to the user as output. In this process, appropriate tone and route selection enable the terminal to guide the user calmly.

[0795] (Application Example 2)

[0796] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0797] In the event of a disaster, in order for users to evacuate appropriately and quickly, personalized guidance that takes into account users' emotional instability is necessary, in addition to geographical route information. Furthermore, existing systems have difficulty responding to users' changing emotional states, which can result in panic and an inability to take appropriate evacuation actions.

[0798] 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.

[0799] In this invention, the server includes means for acquiring the user's biometric information, means for acquiring the current location using a geographic information system, means for acquiring disaster information from an external database, means for analyzing the user's emotional state and adjusting the content of evacuation guidance based on that analysis, and means for changing the tone of the voice guide according to the emotional state. This enables flexible evacuation guidance tailored to the individual emotional state of the user, thereby realizing safer and more efficient evacuation actions.

[0800] "Biometric information" refers to physiological data such as the user's physical fitness, health status, and heart rate.

[0801] A "geographic information system" is a technology used to determine a user's current location, and it includes map information.

[0802] "Disaster information" refers to data related to natural disasters such as earthquakes and typhoons, and is information obtained from external databases.

[0803] An "evacuation route" refers to a route that allows users to evacuate safely during a disaster, and is generated by a system.

[0804] "User terminal" refers to devices operated by users, such as smartphones and portable information devices.

[0805] "Emotional state" refers to the user's psychological state, such as anxiety or tension, and is analyzed from their voice tone and input patterns.

[0806] "Audio guide" refers to a means of providing instructions and guidance to users through audio.

[0807] To implement this invention, it is necessary to construct an integrated system that provides optimized evacuation guidance during disasters. This system uses smartphones or portable information devices to acquire the user's biometric information. These devices have built-in heart rate sensors and microphones, allowing for real-time acquisition of the user's heart rate and voice data. The acquired voice data is sent to an emotion engine and converted to text using the Google Cloud Speech-to-Text API. Subsequently, the emotional state is analyzed using natural language processing technology based on TensorFlow. Based on the analyzed emotional state, the server personalizes the user's evacuation guidance in real time.

[0808] The geographic information system uses GPS technology to determine the user's current location. This location information is linked with the latest disaster data obtained from a disaster information database, and an optimized evacuation route is generated. The server adjusts the evacuation route while considering the user's physical condition, physical limitations, and emotional state, and provides this information to the terminal as an audio guide. Real-time notifications via voice and text are enabled using services such as Twilio.

[0809] As a concrete example, consider the scenario of an earthquake. Suppose the user's heart rate increases and they feel anxious. At this point, the emotion engine detects the user's anxiety and instructs the device to provide a calming voice guidance. This guidance provides messages such as, "Take a deep breath and calm down. We will guide you to a safe route," reassuring the user.

[0810] Examples of prompt statements to input into a generative AI model include the following:

[0811] "Please share your ideas for an application that determines a user's situation during an earthquake and recommends the calmest and safest evacuation route."

[0812] This system helps users take optimal evacuation actions during disasters, ensuring safe and efficient evacuations.

[0813] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0814] Step 1:

[0815] The device uses the smartphone's heart rate sensor and microphone to acquire biometric and voice data. This data is sent to the server as input information. This allows for real-time monitoring of biometric information.

[0816] Step 2:

[0817] The server converts the received audio data into text using the Google Cloud Speech-to-Text API. The input is audio data, and the output is text information. This text data is used as foundational information for sentiment analysis.

[0818] Step 3:

[0819] The server inputs this text information into a generative AI model using TensorFlow to analyze the emotional state. The input is text data, and the output is the user's emotional state (e.g., anxious, stable). This analysis evaluates the user's psychological state.

[0820] Step 4:

[0821] The server uses GPS technology to obtain the user's current location information from a geographic information system. The input is location data, and the output is the user's current latitude and longitude information. This ensures the location information necessary for generating evacuation routes.

[0822] Step 5:

[0823] The server calculates the optimal evacuation route based on disaster information obtained from an external database, along with biometric information, location information, and emotion analysis results. The input is this various data, and the output is the evacuation route. The algorithm generates a safe and efficient route.

[0824] Step 6:

[0825] The terminal uses evacuation route and audio guidance information received from the server to begin providing real-time guidance to the user. In particular, the tone of the audio guidance changes according to the user's emotional state. The input is guidance information from the server, and the output is audio guidance from the terminal to the user. The user can take evacuation actions with confidence thanks to the audio guidance.

[0826] Step 7:

[0827] The server can also use services like Twilio to send additional text notifications at the user's request. The input is instructional information from the server, and the output is a text message to the user. This allows the user to receive situation-specific evacuation instructions.

[0828] 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.

[0829] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[0830] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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 half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0836] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0837] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0838] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0839] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0840] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0841] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0842] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0843] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0844] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0845] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0846] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0847] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0848] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0849] The following is further disclosed regarding the embodiments described above.

[0850] (Claim 1)

[0851] Means of obtaining biometric information from users,

[0852] A means of obtaining the current location using a geographic information system,

[0853] Methods for obtaining disaster information from external databases,

[0854] A means for generating the optimal evacuation route based on acquired biometric information, current location, and disaster information,

[0855] A system that includes means for displaying and guiding users through generated evacuation routes on their terminals.

[0856] (Claim 2)

[0857] The system according to claim 1, further comprising means for adjusting the generated evacuation routes in real time.

[0858] (Claim 3)

[0859] The system according to claim 1, comprising means for generating an optimal evacuation route that takes into account the user's physical strength and physical limitations.

[0860] "Example 1"

[0861] (Claim 1)

[0862] Means of obtaining biometric information from users,

[0863] A means of obtaining the current location using a function that measures location information,

[0864] A means of obtaining disaster information from an external storage device,

[0865] A means for generating the optimal evacuation route using a generation processing model based on acquired biometric information, current location, and disaster information,

[0866] A system that includes means for displaying and providing voice guidance on the generated evacuation route on the user's terminal.

[0867] (Claim 2)

[0868] The system according to claim 1, further comprising a function to link with external data for adjusting the generated evacuation routes in real time.

[0869] (Claim 3)

[0870] The system according to claim 1, which has a function to generate an optimal evacuation route taking into account the user's physical abilities and physical conditions.

[0871] "Application Example 1"

[0872] (Claim 1)

[0873] Means of obtaining biological data from users,

[0874] Means for obtaining geographic location using geographic information technology,

[0875] Means of obtaining disaster information from external sources,

[0876] A means for generating the optimal evacuation route based on acquired biological data, geographical location, and disaster information,

[0877] A means of guiding the user device through the generated evacuation route visually and audibly,

[0878] A system including means for dynamically adjusting generated evacuation routes, taking into account traffic conditions and the congestion level of evacuation shelters.

[0879] (Claim 2)

[0880] The system according to claim 1, comprising means for generating an optimal evacuation route based on the user's motor skills and physical limitations, or based on the congestion status of the evacuation shelter.

[0881] (Claim 3)

[0882] The system according to claim 1, comprising means for updating generated evacuation routes using information and communication technology and responding to real-time changes in the situation.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] Means of obtaining biometric information from users,

[0886] A means of obtaining the current location using location information technology,

[0887] Methods for obtaining disaster information from external databases,

[0888] A means for generating the optimal evacuation route based on acquired biometric information, current location, and disaster information,

[0889] A means of analyzing the emotional state of users using a generative AI model,

[0890] A means of adjusting the evacuation route generated based on the analysis results,

[0891] A system that includes means for displaying and guiding users to coordinated evacuation routes on their terminals.

[0892] (Claim 2)

[0893] The system according to claim 1, which adjusts the generated evacuation route in real time and provides guidance according to the user's emotional state.

[0894] (Claim 3)

[0895] The system according to claim 1, comprising means for generating an optimal evacuation route that takes into account the user's physical abilities and limitations, as well as their emotional state.

[0896] "Application example 2 when combining with an emotional engine"

[0897] (Claim 1)

[0898] Means of obtaining biometric information from users,

[0899] A means of obtaining the current location using a geographic information system,

[0900] Methods for obtaining disaster information from external databases,

[0901] A means for generating the optimal evacuation route based on acquired biometric information, current location, and disaster information,

[0902] A means for displaying and guiding users through the generated evacuation route on their terminals,

[0903] A means of analyzing the emotional state of users and adjusting the content of evacuation guidance based on that analysis,

[0904] A means of changing the tone of the audio guide according to the emotional state,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, further comprising means for adjusting the generated evacuation route in real time, as well as means for personalizing evacuation guidance based on emotion analysis results.

[0908] (Claim 3)

[0909] The system according to claim 1, comprising means for generating an optimal evacuation route that takes into account the user's physical strength, physical limitations, and emotional state, and providing that route along with an audio guide. [Explanation of symbols]

[0910] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining biometric information from users, A means of obtaining the current location using a geographic information system, Methods for obtaining disaster information from external databases, A means for generating the optimal evacuation route based on acquired biometric information, current location, and disaster information, A system that includes means for displaying and guiding users through generated evacuation routes on their terminals.

2. The system according to claim 1, further comprising means for adjusting the generated evacuation routes in real time.

3. The system according to claim 1, comprising means for generating an optimal evacuation route that takes into account the user's physical strength and physical limitations.

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