System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting position information held by an electronic device; means for acquiring acceleration data; means for transmitting the position information and the acceleration data to a server; means for estimating a road surface condition based on the received position information and acceleration data; means for analyzing a "passable road" by integrating the estimated road surface condition and the position information and storing the result in a database; means for generating an analysis result as visual information; and means for visually presenting the analysis result to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] During emergencies such as earthquakes and other natural disasters, or during events that impose traffic restrictions, normal routes often become unusable. Currently, there are limited means of determining which routes are passable in real time during an emergency, making it difficult for pedestrians to move safely. If this situation continues, the risk of confusion and secondary disasters increases, so a system that accurately and quickly shows passable routes is needed. [Means for solving the problem]

[0005] The present invention is a system that includes a means for collecting location information from electronic devices, a means for acquiring acceleration data, and a means for transmitting this data to a server. The server estimates road surface conditions based on the received location information and acceleration data, and then integrates the estimated road surface conditions and location information to analyze "passable roads." The system also includes a means for the server to generate visual information based on the analysis results and provide users with information on routes they have previously traveled, updated in real time. This allows pedestrians to travel safely based on the latest traffic information, reducing confusion in emergencies.

[0006] "Electronic devices" refer to mobile devices such as smartphones and tablets equipped with GPS functionality and acceleration sensors.

[0007] "Location information" refers to geographical data such as latitude, longitude, and altitude obtained using the GPS function of an electronic device.

[0008] "Acceleration data" refers to data relating to changes in acceleration collected through an acceleration sensor of an electronic device.

[0009] A "server" is a computer system that receives and analyzes data sent from electronic devices via a network, and stores and transmits the results.

[0010] "Road surface conditions" refers to information that indicates the physical conditions of the road on which pedestrians walk, such as flat ground, slopes, steps, and stairs.

[0011] A "passable road" is a route that has been analyzed as currently safe for pedestrians to travel based on the latest location information and road surface conditions.

[0012] "Visual information" refers to visual guidance information such as footprints and routes displayed on a map based on data generated by the server. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention is a system that uses electronic devices, a server, and a network for communication through them, and in particular, a system that collects and analyzes location information and acceleration data to provide passable routes in real time in the event of an emergency.

[0035] Data collection

[0036] First, when a user starts walking with an electronic device, the user launches the "Footprint Navigation" app. The device uses its GPS and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is sent to the server at regular intervals (e.g., every second).

[0037] Data analysis

[0038] The server receives the location information and acceleration data sent from the device. The server uses this data to estimate road surface conditions (e.g., flat ground, slopes, steps, etc.). The estimated road surface conditions and location information are stored in a database, and passable routes are analyzed based on this information. The server uses generative AI to analyze the latest data and identify routes that pedestrians can safely travel.

[0039] Information presentation

[0040] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the routes that other users have previously traveled. This footprint information is displayed in real time on the screen of the electronic device. This allows users to continue traveling safely while checking which routes are "passable now."

[0041] Specific examples

[0042] Example 1: Use at an event

[0043] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. User A launches "Footprint Navigation" and, as he walks from the event venue to the station, his device collects GPS and acceleration data. These data are sent to the server. The server also receives data from other users (e.g., User B and User C) who have just passed by, analyzes this data using the generation AI, and identifies a safe route. The device displays the results of this analysis on a map, allowing User A to safely reach the station.

[0044] Example 2: Evacuation during a disaster

[0045] After an earthquake occurs, User D launches the "Footprint Navigation" app and begins evacuation. The device collects GPS and acceleration data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the generation AI analyzes evacuation routes. The analysis results are integrated with data from other evacuees (Users E and F). The device displays this on a map, and User D can safely evacuate by following the visualized "footprints."

[0046] As described above, the present invention is a system for providing pedestrian-passable routes in real time during emergencies or traffic restrictions, and can support users in moving around safely.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] The user launches the "Footprint Navigation" app. The app initializes the GPS sensor and acceleration sensor, preparing to collect location and acceleration data.

[0050] Step 2:

[0051] The device begins collecting location information (latitude, longitude, altitude) and acceleration data. This data is collected at regular intervals (e.g., every second).

[0052] Step 3:

[0053] The device stores collected location and acceleration data in a temporary buffer, which is configured to hold the most recent data.

[0054] Step 4:

[0055] The terminal transmits the data held in the buffer to the server via the network at regular intervals.

[0056] Step 5:

[0057] The server stores the received location and acceleration data in a database in real time, and the stored data is tagged with the time.

[0058] Step 6:

[0059] The server analyzes the acceleration data and estimates the road surface conditions (flat ground, slopes, steps, stairs, etc.) using accumulated past data and machine learning models.

[0060] Step 7:

[0061] The server integrates estimated road surface conditions and location information to analyze passable routes. Data collected from multiple users is used in the analysis, and the generating AI identifies safe routes.

[0062] Step 8:

[0063] The server generates the analysis results as visual information (footprints or paths on a map), which shows the latest safe routes.

[0064] Step 9:

[0065] The server transmits the generated visual information to the terminal, and the transmission is set to be performed in real time.

[0066] Step 10:

[0067] The device displays the received visual information on a map, allowing the user to check which routes are currently passable.

[0068] Step 11:

[0069] The user moves safely by following the displayed "footprints." Each time the user moves, the device again collects data and sends it to the server to obtain the latest information.

[0070] These steps enable the system to provide safe and passable routes in real time during emergencies and traffic restrictions.

[0071] Example 1

[0072] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0073] In recent years, there has been a demand for systems that provide pedestrians with real-time guidance on appropriate routes to ensure safe travel during large-scale urban events and natural disasters. However, existing systems often do not fully utilize the latest sensor data, making it difficult to quickly and accurately provide information on road surface conditions and passable routes. Furthermore, they are unable to effectively utilize other users' travel data, making it difficult to provide realistic travel information. There is an urgent need to resolve these issues.

[0074] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0075] In this invention, the server includes a means for estimating road surface conditions based on received location information and acceleration data, a means for integrating the estimated road surface conditions and location information to analyze "passable roads" and store the results in a database, and a means for generating the analysis results as visual information using a generative AI model, thereby enabling the provision of accurate passable routes based on the latest data.

[0076] A "mobile terminal" is an electronic device carried by a user, and is a device equipped with a GPS function and an acceleration sensor.

[0077] "Location information" refers to geographical data such as latitude, longitude, and altitude collected by mobile devices.

[0078] "Acceleration data" refers to data relating to the speed and direction of movement collected by a mobile terminal.

[0079] A "server" is a remote computer system capable of receiving, analyzing, storing, and transmitting data sent from multiple mobile terminals.

[0080] "Road surface conditions" refers to information indicating the difficulty and characteristics of travel (e.g., flat ground, slopes, steps, stairs, etc.) that the server estimates based on location information and acceleration data.

[0081] "Generative AI models" refer to algorithms and software that use artificial intelligence technology to analyze data and generate optimal routes and visual information.

[0082] A "passable road" refers to a route that the server has analyzed and determined to be safe for the user to travel.

[0083] "User" refers to a person who uses a mobile device to check passable routes in emergencies or special situations through the "Footprint Navigation" app and attempts to travel safely.

[0084] A "database" is an information management system that allows a server to store location information, acceleration data, analysis results, etc.

[0085] "Visual information" is information generated by the server that displays passable roads, road surface conditions, etc. as maps or graphics.

[0086] "Real-time" refers to the state in which data collection, analysis, and display occur almost simultaneously, meaning that users can use their current location information without delay.

[0087] The present invention is a system that utilizes mobile terminals, a server, and a network for communication therethrough, and has been developed to provide passable roads in real time, particularly in emergencies and events. Specific embodiments of the program for this system are described below.

[0088] Data collection

[0089] A user holds a mobile device such as a smartphone and launches the "Footprint Navigation" application. The device uses a GPS module and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. The collected data is sent to a server at regular intervals (for example, every second). This allows the user's current location and movement status to be tracked in real time.

[0090] Hardware used:

[0091] Smartphone GPS module

[0092] Smartphone accelerometer

[0093] Software used:

[0094] Footprint navigation application

[0095] Data analysis

[0096] The server receives location information and acceleration data sent from the device. The received data is analyzed using a generative AI model to estimate road surface conditions (e.g., flat ground, slopes, steps, etc.). In addition, the estimated road surface conditions are integrated with the location information, and safe route information is stored in a database.

[0097] Hardware used:

[0098] Server with high-performance data processing capabilities

[0099] Software used:

[0100] Database management system (e.g., MySQL (registered trademark))

[0101] Generative AI models (e.g., TENSORFLOW®)

[0102] Information presentation

[0103] The server uses the generative AI model to generate the analysis results as visual information. This information is displayed as "passable routes" on a map that includes the user's current location. The displayed information is updated in real time, allowing users to travel safely while checking the latest passable routes.

[0104] Hardware used:

[0105] Smartphone displays

[0106] Software used:

[0107] User Interface (UI) for Footprint Navigation Application

[0108] Specific examples

[0109] Example 1: Use at an event

[0110] When a large-scale event is held in a particular city and some roads are closed to traffic, a user can launch "Footprint Navigation" and walk from the event venue to the station. The device collects GPS and acceleration data and sends it to a server. The server then integrates the data of other users and analyzes it with a generative AI model to identify a safe route. The analysis results are sent to the user's device and displayed on a map, allowing the user to reach the station safely.

[0111] Example 2: Evacuation during a disaster

[0112] When a user activates "Footprint Navigation" after an earthquake and begins evacuation, the device collects GPS and acceleration data in real time and sends it to the server. The server then analyzes this data using a generative AI model to predict road surface conditions. The analysis results also incorporate data from other evacuees, and the user's device displays this on a map. The user can then safely evacuate by following the visualized "footprints."

[0113] Prompt Sentence Examples

[0114] Below are some examples of prompt sentences:

[0115] Prompts for generating scenarios for people

[0116] "You are participating in a large-scale event in a certain city. After the event has ended, traffic restrictions have been put in place on the roads from the venue to the station, so you need to find the safest route. You will use the Footprint Navigation app to navigate by referring to the routes that other users have just taken. Please describe, step by step, how you will proceed."

[0117] Prompts for generating evacuation scenarios in the event of a disaster

[0118] "You're in a big city and experiencing an earthquake, and you need to evacuate immediately. Use the Footprint Navigation app for step-by-step instructions on how to find the safest route based on up-to-date data from other evacuees."

[0119] By introducing detailed processes to support users' safe movement in this way, this system becomes an effective means of providing pedestrian-safe routes in real time during emergencies or traffic restrictions.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1:

[0122] The user launches the app. The user launches the "Footprint Navigation" app using their smartphone. This prepares the app to start collecting location and acceleration data.

[0123] Specific behavior:

[0124] The user taps the "Footprint Navigation" icon on the smartphone's home screen to launch the app.

[0125] Input: Touch operation on smartphone

[0126] Output: App launch

[0127] Step 2:

[0128] The device collects location and acceleration data. When the app is launched, the device activates the GPS module and acceleration sensor to collect latitude, longitude, altitude, and acceleration data every second.

[0129] Specific behavior:

[0130] The device obtains latitude, longitude, and altitude data from the GPS every second, and current acceleration data from the acceleration sensor.

[0131] Input: Data from the GPS module and accelerometer

[0132] Output: A set of position and acceleration data updated every second

[0133] Step 3:

[0134] The device sends the collected data to the server. At regular intervals (e.g., every second), the collected location information and acceleration data are packetized and sent to the server.

[0135] Specific behavior:

[0136] The device packages the data into packets and sends them over the internet to a server.

[0137] Input: A set of location and acceleration data

[0138] Output: Data packets sent to the server

[0139] Step 4:

[0140] The server receives and analyzes the data. Based on the received location information and acceleration data, the server uses a generative AI model to estimate road surface conditions. The estimated road surface conditions and location information are integrated to generate passable route information and store it in a database.

[0141] Specific behavior:

[0142] The server stores the received data in a database and analyzes it using a generative AI model, which combines GPS information and acceleration data to predict road surface conditions (flat ground, slopes, steps, stairs, etc.).

[0143] Input: User location and acceleration data

[0144] Output: Estimated road surface conditions and passable route information

[0145] Step 5:

[0146] The server generates passable route information. The server integrates the latest data with existing data to identify safe routes. This information is generated in a visual format and provided to the user.

[0147] Specific behavior:

[0148] The server uses the generative AI model to generate the analysis results as visual information and draw them as map layers.

[0149] Input: Estimated road surface conditions and location information

[0150] Output: Visually displayable route information

[0151] Step 6:

[0152] The device receives and displays the route information sent from the server. The user's mobile device displays the analysis results on a map and updates them in real time. This allows the user to check the latest passable routes and travel safely.

[0153] Specific behavior:

[0154] The device receives the route information sent from the server and displays it on the map screen within the app. Users can travel safely while checking their route on the map in real time.

[0155] Input: Route information sent from the server

[0156] Output: Real-time updated map display and route information

[0157] (Application example 1)

[0158] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0159] The present invention relates to a system that provides passable routes in real time in emergencies, and aims to improve safety in emergencies by identifying and providing routes that autonomous vehicles can safely travel. Conventional systems have had difficulty in understanding and analyzing situations in real time, so there has been a demand for rapid and accurate responses in emergencies.

[0160] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0161] In this invention, the server includes a means for integrating the estimated road surface conditions and location information to analyze "passable roads" and store the results in a database, a means for generating visual information about the analysis results, and a means for an electronic device mounted on the autonomous vehicle to present a safe driving route based on the analysis results. This makes it possible to provide routes that autonomous vehicles can travel safely and efficiently even in emergencies.

[0162] An "electronic device" is a device that has the ability to collect location and acceleration data and transmit it to a server.

[0163] "Location information" refers to latitude, longitude, and altitude data obtained by a GPS device.

[0164] "Acceleration data" refers to data relating to acceleration acquired by a sensor built into an electronic device.

[0165] The "server" is a computer system that receives location information and acceleration data, estimates road surface conditions, and analyzes safe routes.

[0166] "Road surface condition" refers to the physical condition of the road estimated by the server based on the received position information and acceleration data.

[0167] A "passable road" is a road route that the server has analyzed and determined to be safe to travel.

[0168] The "database" is a recording medium for integrating and storing estimated road surface conditions and position information.

[0169] "Visual information" refers to visual representation data based on the analysis results that is generated by the server for presentation to the user.

[0170] An "autonomous vehicle" is a vehicle that can drive autonomously and present safe driving routes based on data from a server.

[0171] A "safe driving route" is the optimal route for an autonomous vehicle to travel based on information about passable roads.

[0172] This invention relates to a system that utilizes electronic devices, a server, and a network for communication therethrough. This system provides safe routes for autonomous vehicles in the event of an emergency in real time.

[0173] First, a user gets into an autonomous vehicle and starts up their electronic device. The electronic device is equipped with a GPS function and an acceleration sensor, which collects location information (latitude, longitude, altitude) and acceleration data. This data is sent to a server at regular intervals (for example, every second). The electronic device used can be a typical smartphone, tablet, or the vehicle's infotainment system.

[0174] The server receives the location information and acceleration data sent from the device. The server requires high-performance computing capabilities, so it is recommended to use dedicated server equipment or cloud computing services (e.g., AWS (registered trademark) or Google (registered trademark) Cloud). The server uses the received data to estimate the road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It is effective to use a generative AI model for this estimation.

[0175] The server combines the estimated road surface conditions and location information to analyze passable routes. The results of this analysis are stored in a database, which can be a standard SQL database or a NoSQL database (e.g., MySQL or MongoDB). The server then re-analyzes safe driving routes based on the latest data, enabling rapid response in emergencies.

[0176] The server then generates visual information based on the analysis results and sends it to the autonomous vehicle's onboard electronics, such as the infotainment system or a smartphone, allowing the user to visually check the safe driving route in real time. Map display services such as Google Maps API can be used to generate the visual information.

[0177] Examples of specific prompts include:

[0178] text

[0179] Analyze a safe route for an autonomous vehicle based on the following data:

[0180] Current location: Latitude 35.6895, Longitude 139.6917, Altitude 15m

[0181] Acceleration data: x=0.02, y=0.03, z=9.8

[0182] Other sensor data (e.g. road conditions, obstacles, weather, etc.)

[0183] Please return the analysis results as a safe route.

[0184] Specific use cases include evacuation during disasters and traffic restrictions during large-scale events. For example, after an earthquake, the system can identify safe evacuation routes for vehicles and display them on the infotainment system, effectively supporting evacuation.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] The user activates the electronic device installed in the vehicle and starts an application. The input is the activation of the application by the user's operation. The electronic device uses the GPS function and acceleration sensor to collect the vehicle's location information (latitude, longitude, altitude) and acceleration data. The output is the collected location information and acceleration data.

[0188] Step 2:

[0189] The electronic device sends the location information and acceleration data it collects to a server. The input is the collected location information and acceleration data, and the output is the data sent to the server. The data is collected and sent at regular intervals (e.g., every second).

[0190] Step 3:

[0191] The server estimates road surface conditions using the location information and acceleration data it receives. The input is the received location information and acceleration data, and the output is the estimated road surface conditions. A generative AI model on the server analyzes the data and estimates the type of road surface (flat ground, slope, steps, stairs, etc.).

[0192] Step 4:

[0193] The server integrates the estimated road surface conditions and location information, analyzes "passable roads," and stores the results in a database. The input is the estimated road surface conditions and location information, and the output is the analyzed "passable road" data. Analysis technology based on a generative AI model is used.

[0194] Step 5:

[0195] The server generates visual information based on the analysis results. The input is the analyzed "passable roads" and the latest road surface conditions and location information, and the output is information that can be displayed visually. To generate the visual information, map display services such as Google Maps API are used.

[0196] Step 6:

[0197] The server generates visual information and sends it to the electronics of the autonomous vehicle. The input is the visual information, and the output is the data sent to the electronics. This provides a safe driving route that is updated in real time.

[0198] Step 7:

[0199] The visual information received by the electronic devices of an autonomous vehicle is displayed on the infotainment system display. The input is visual information sent from the server, and the output is a safe driving route displayed on the display. The user can travel safely based on this information.

[0200] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0201] The present invention is a system that uses electronic devices, servers, and a network for communication therethrough, and collects and analyzes location information and acceleration data to provide passable routes in real time, especially in emergencies.Furthermore, the present invention is a system that dynamically changes the way information is presented based on the user's emotional state by combining an emotion engine that recognizes the user's emotions.

[0202] Data collection

[0203] First, when a user starts walking with an electronic device, the user launches the "Footprint Navigation" app. The device uses its GPS and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is sent to the server at regular intervals (e.g., every second).

[0204] Emotional Data Collection

[0205] At the same time, the device uses sensors such as a camera and microphone to recognize the user's emotions. The device uses the data collected from these sensors to obtain emotional data from the user's facial expressions and voice. The emotional data is also sent to the server along with location information and acceleration data.

[0206] Data analysis

[0207] The server receives location information and acceleration data transmitted from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It then analyzes the received emotion data using an emotion engine to estimate the user's emotional state (e.g., tension, relief, fear, etc.). In this way, the server integrates the estimated road surface conditions and emotional state to analyze passable routes.

[0208] Information presentation

[0209] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the paths previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of presenting information is optimized according to the user's emotional state. For example, more detailed directions are provided to a nervous user, and simple directions are provided to a relaxed user.

[0210] Specific examples

[0211] Example 1: Use at an event

[0212] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. When User A launches "Footprint Navigation" and walks from the event venue to the station, the device collects GPS data, acceleration data, and emotional data and sends it to the server. The server analyzes this data and uses a generative AI to identify a safe route. The analysis results are displayed on a map as visual information that corresponds to User A's state of mind. For example, if User A is feeling nervous, more detailed footprint information will be provided.

[0213] Example 2: Evacuation during a disaster

[0214] After an earthquake occurs, User D activates the "Footprint Navigation" app and begins evacuation. The device collects GPS data, acceleration data, and emotion data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the emotion engine analyzes User D's emotional state. The analysis results are integrated with data from other evacuees (User E and User F), and the generation AI identifies a safe evacuation route. The device displays this on a map, and User D can evacuate safely by following the visualized "footprints." If the emotional state is tense, an escort alarm and detailed instructions are provided.

[0215] As described above, the present invention can support the user's safe travels by providing safe and passable routes in real time during emergencies or traffic restrictions, and by optimizing the information presentation method based on the user's emotional state.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] The user launches the "Footprint Navigation" app. The app initializes the GPS sensor, acceleration sensor, and emotion recognition sensors such as the camera and microphone, and prepares to collect location information, acceleration data, and emotion data.

[0219] Step 2:

[0220] The device begins collecting location information (latitude, longitude, altitude) and acceleration data. This data is collected at regular intervals (e.g., every second).

[0221] Step 3:

[0222] The device uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the emotional data, which is then stored in a buffer in real time.

[0223] Step 4:

[0224] The location information, acceleration data, and emotion data collected by the terminal are transmitted to a server via a network at regular intervals.

[0225] Step 5:

[0226] The server receives location information and acceleration data and stores it in a database in real time. The location information also includes time information.

[0227] Step 6:

[0228] The server analyzes the acceleration data and estimates the road surface conditions (e.g., flat ground, slopes, steps, etc.) using accumulated past data and machine learning models.

[0229] Step 7:

[0230] The server uses an emotion engine to analyze the received emotion data and estimate the user's emotional state (e.g., tension, relief, fear, etc.).

[0231] Step 8:

[0232] The server integrates the estimated road surface conditions, location information, and emotional state to analyze passable paths, which are then identified as safe routes by the generation AI.

[0233] Step 9:

[0234] The server generates the analysis results as visual information (footprints or routes on a map), showing the latest safe routes and responding to the user's emotional state.

[0235] Step 10:

[0236] The server generates visual information and sends it to the terminal, with the transmission set to occur in real time.

[0237] Step 11:

[0238] The device displays the received visual information on a map, allowing the user to check which routes are currently passable.

[0239] Step 12:

[0240] The user follows the displayed "footprints" to move safely. During the movement, the device continues to collect data and transmits it to the server for the latest information. If the user's emotional state changes, the display method will also dynamically change accordingly.

[0241] These steps enable the system to provide safe and passable routes in real time during emergencies or traffic restrictions, while optimizing the way information is presented based on the user's emotional state.

[0242] Example 2

[0243] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0244] Conventional systems have had difficulty providing safe and passable routes in real time during emergencies or traffic restrictions. Furthermore, they lacked the ability to dynamically change the way information is presented depending on the user's emotional state, making it impossible to provide optimal route guidance suited to the situation. As a result, there was a problem in that the system did not adequately support users' safe travels.

[0245] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for estimating road surface conditions based on received position information and acceleration data, a means for integrating the estimated road surface conditions and position information to analyze "passable roads" and store the results in a database, a means for generating visual information based on the analysis results, a means for analyzing emotion data to estimate the user's emotional state, and a means for dynamically changing the information presentation method based on the estimated emotional state. This makes it possible to provide safe and passable roads in real time during emergencies or traffic restrictions, and to present optimal information according to the user's emotional state.

[0246] "Location information" is data indicating a geographical location, including latitude, longitude, and altitude.

[0247] "Acceleration data" is data that indicates changes in the speed of an object's movement, measured by an acceleration sensor in an electronic device.

[0248] A "server" is a centralized device that receives data from clients via a network, analyzes it, and stores it.

[0249] "Road surface condition" is data indicating the condition of the ground surface, such as flat ground, slopes, steps, and stairs.

[0250] "Passable roads" is data indicating routes that are estimated to be safe for the user to travel.

[0251] A "database" is a data storage system for systematically managing and storing information.

[0252] "Emotion data" is data that indicates the emotional state of the user analyzed from facial expressions, voice, and the like.

[0253] An "emotion engine" is a software or hardware system that analyzes emotion data to infer a user's emotional state.

[0254] "Visual information" refers to information such as maps and route guidance that is visually provided to the user.

[0255] "Electronic devices" refer to devices such as smartphones and tablets that users carry with them to process information.

[0256] "Real-time" is a term that refers to instant processing of data or information that reflects its current state.

[0257] A "sensor" is a device that detects environmental or operational conditions and transmits that data to electronic devices.

[0258] This invention relates to a system that utilizes electronic devices, a server, and a network for communication via them. The system has the function of collecting and analyzing location information and acceleration data to provide passable routes in real time, particularly in emergencies. It also has the function of dynamically changing the way information is presented based on the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[0259] Data collection

[0260] First, the user installs and launches the "Footprint Navigation" application on an electronic device such as a smartphone or tablet. When the user launches the application, the device uses its GPS function and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is then sent to a server at regular intervals (e.g., every second).

[0261] Emotional Data Collection

[0262] At the same time, the device uses sensors such as a camera and microphone to recognize the user's emotions. The device uses the data collected from these sensors to obtain emotional data from the user's facial expressions and voice. The emotional data is also sent to the server along with location information and acceleration data.

[0263] Data analysis

[0264] The server receives location information and acceleration data transmitted from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It then analyzes the received emotion data using an emotion engine to estimate the user's emotional state (e.g., tension, relief, fear, etc.). In this way, the server integrates the estimated road surface conditions and emotional state to analyze passable routes.

[0265] Information presentation

[0266] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the paths previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of presenting information is optimized according to the user's emotional state. For example, more detailed directions are provided to a nervous user, and simple directions are provided to a relaxed user.

[0267] Specific examples

[0268] Example 1: Use at an event

[0269] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. When User A launches "Footprint Navigation" and walks from the event venue to the station, the device collects GPS data, acceleration data, and emotional data and sends it to the server. The server analyzes this data and uses a generative AI to identify a safe route. The analysis results are displayed on a map as visual information that corresponds to User A's state of mind. For example, if User A is feeling nervous, more detailed footprint information will be provided.

[0270] Example 2: Evacuation during a disaster

[0271] After an earthquake occurs, User D activates the "Footprint Navigation" app and begins evacuation. The device collects GPS data, acceleration data, and emotion data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the emotion engine analyzes User D's emotional state. The analysis results are integrated with data from other evacuees (User E and User F), and the generation AI identifies a safe evacuation route. The device displays this on a map, and User D can evacuate safely by following the visualized "footprints." If the emotional state is tense, an escort alarm and detailed instructions are provided.

[0272] Prompt Sentence Examples

[0273] "We have launched a project to identify safe routes available in emergencies using a footprint navigation app. Please explain the detailed steps, the software and hardware used, and the specific data processing methods."

[0274] The above is an embodiment of the present invention.

[0275] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0276] Step 1:

[0277] The user installs the "Footprint Navigation" app on their electronic device and launches it. When the app launches, the main screen appears and the user is prompted to grant access to location services, the camera, and the microphone. As input, the app is launched by the user's operation. As output, the app's main screen appears and the use of sensors is permitted.

[0278] Step 2:

[0279] The user starts walking. The device begins to obtain the user's location information (latitude, longitude, and altitude) using its GPS function. At the same time, it uses the acceleration sensor to obtain acceleration data related to the user's movements. The camera and microphone are used to collect emotion data from the user's facial expressions and voice. Data is collected from the GPS sensor, acceleration sensor, camera, and microphone as input. Location information, acceleration data, and emotion data are obtained as output.

[0280] Step 3:

[0281] The device transmits the collected location information, acceleration data, and emotion data to the server at regular intervals (e.g., every second). Data transmission is performed using a secure communication protocol (e.g., HTTPS). The collected data exists as input. These data are transmitted to the server as output.

[0282] Step 4:

[0283] The server receives the location information and acceleration data sent from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate the road surface condition (flat ground, slope, step, stairs, etc.). The received data exists as input. The analysis results and road surface condition are stored in the database as output.

[0284] Step 5:

[0285] The server analyzes the received emotion data using an emotion engine to estimate the user's emotional state (tension, relief, fear, etc.). The estimated road surface condition and emotional state are integrated to analyze a passable road. The analyzed road surface condition and emotional state are input. The analysis result of a passable road is obtained as output.

[0286] Step 6:

[0287] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the routes previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of information presentation is optimized according to the user's emotional state. The input is the analysis result of passable routes. The output is visual information, and the method of information presentation is optimized based on the user's emotional state.

[0288] Step 7:

[0289] The user's electronic device receives the visual information previously generated by the server and displays it on its screen. The user uses this information to find a safe route and receives real-time updated guidance. As input, there is the visual information sent from the server. As output, there is the visual information displayed on the user's electronic device.

[0290] The above are the specific processing steps of the system according to the present invention.

[0291] (Application example 2)

[0292] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0293] Currently, autonomous vehicles are required to improve safety and efficiency, but there is a lack of systems that provide safe and passable routes in real time, especially in emergencies. Furthermore, technology that recognizes the emotional state of users while driving and provides appropriate information accordingly is immature. As a result, current systems do not adequately provide flexible and safe travel support based on the user's emotional state. Furthermore, existing technologies are limited in their ability to collect and analyze large amounts of data in real time, making it difficult to provide safe routes, especially during large-scale events and disasters.

[0294] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0295] In this invention, the server includes means for receiving position information and acceleration data and estimating road surface conditions, means for analyzing "passable roads" in real time based on the position information, acceleration data, and emotion data, means for generating visual information based on the analysis results, and means for analyzing the user's emotional state based on the emotion data and dynamically changing the information presentation method. This allows the server to provide the optimal route in real time according to the user's emotional state, both in emergencies and under normal circumstances, enabling safe and efficient travel.

[0296] An "electronic device" is a device that can be carried by a user and has the capability to collect location information and acceleration data.

[0297] "Location information" refers to latitude, longitude, and altitude data used by electronic devices to indicate their current location.

[0298] "Acceleration data" is data used to measure changes in the movement and speed of electronic devices.

[0299] A "server" is a computer system that has the function of receiving and analyzing location information, acceleration data, and emotion data transmitted from electronic devices.

[0300] "Road surface condition" refers to the condition of the ground for travel that the server estimates based on location information and acceleration data.

[0301] A "passable road" is a route that the server has analyzed and determined to be safe to travel.

[0302] "Emotional data" is data collected to recognize a user's emotional state.

[0303] An "emotion engine" is software or a system for analyzing emotion data and inferring a user's emotional state.

[0304] "Information presentation method" refers to the means or format by which the server presents analysis results and information to the user.

[0305] "Visual information" refers to an information display format that allows the user to visually understand the analysis results and passable roads.

[0306] "Real-time" means that data is collected, analyzed, and information is presented without delay.

[0307] This invention relates to a system for supporting safe travel for users, and in particular to a program for providing real-time passable routes for autonomous vehicles. The system includes a server that collects and analyzes user location information, acceleration data, and emotion data.

[0308] Data collection

[0309] When a user activates an electronic device (for example, the console of an autonomous vehicle), sensors within the vehicle are activated. Specifically, the following sensors are installed:

[0310] GPS sensor: Collects vehicle location information (latitude, longitude, altitude).

[0311] Acceleration sensor: Acquires vehicle acceleration data to understand road surface conditions.

[0312] Cameras and microphones: Capture video and audio from inside and outside the vehicle and collect user emotional data.

[0313] Data transmission

[0314] The collected location, acceleration, and emotion data is transmitted to a server by the on-board computer in the autonomous vehicle. The transmitted data includes:

[0315] Location information: latitude, longitude, altitude

[0316] Acceleration data: Vehicle speed changes and vibration state

[0317] Emotion data: Emotional state obtained from the user's facial expressions and voice

[0318] Data analysis

[0319] The server analyzes the received data using the following steps:

[0320] 1. Analysis of location information and acceleration data: Based on location information and acceleration data, road surface conditions (flat ground, slopes, steps, etc.) are estimated.

[0321] 2. Emotion data analysis: Using the emotion engine, the user's emotional state (e.g., tension, relief) is analyzed from the collected emotion data.

[0322] 3. Integrated analysis: Integrate the estimated road surface conditions and the analyzed emotional state to identify passable paths.

[0323] Information presentation

[0324] The server generates the analysis results as visual information. This information is displayed in real time on the vehicle's display. The way the information is presented changes dynamically depending on the user's emotional state. Specifically, the following display formats are possible:

[0325] 1. Detailed guidance: If the user is nervous, detailed routes and precautions will be displayed.

[0326] 2. Simple guidance: If the user is comfortable, simple route information is displayed.

[0327] Specific examples

[0328] For example, when a large-scale event is being held in a city, certain roads may be unavailable. The vehicle's "Safety Navigation AI" uses GPS sensors, acceleration sensors, cameras, and microphones to collect and transmit data in real time. The server analyzes the received data, identifies the optimal route, and displays detailed or simple directions according to the user's emotional state.

[0329] Prompt statement

[0330] Data collection prompt statements

[0331] gps_data = get_gps_data()

[0332] acceleration_data = get_acceleration_data()

[0333] emotion_data = get_emotion_data()

[0334] Data transmission and route acquisition prompts

[0335] response = send_all_data(gps_data, acceleration_data, emotion_data)

[0336] route_data = get_route(response)

[0337] Root display prompt statement

[0338] display_route(route_data)

[0339] As described above, analyzing the user's location information and emotional data and providing appropriate route guidance can improve the safety and efficiency of self-driving vehicles.

[0340] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0341] Step 1:

[0342] Data collection

[0343] When a user starts up an electronic device, the device collects data using a GPS sensor, accelerometer, camera, and microphone. This data includes the current location (latitude, longitude, and altitude), the vehicle's acceleration, and emotion data from the user's facial expressions and voice. The inputs are location information from the GPS sensor, movement data from the accelerometer, and video and audio data from the camera and microphone. The output is the collected location information, acceleration data, and emotion data.

[0344] Step 2:

[0345] Data transmission

[0346] The device transmits the collected location information, acceleration data, and emotion data to the server via the on-board computer. This data transmission process uses a specific communication protocol (e.g., HTTP or MQTT). The input is the location information, acceleration data, and emotion data collected in step 1. The output is these data transmitted to the server.

[0347] Step 3:

[0348] Location and acceleration data analysis

[0349] The server analyzes road surface conditions based on the location information and acceleration data received. This involves estimating road surface conditions, such as flat ground, slopes, and steps, by comparing them with past and current data in a database. The input is location information and acceleration data. The output is the estimated road surface conditions.

[0350] Step 4:

[0351] Emotional Data Analysis

[0352] The server uses an emotion engine to analyze the received emotion data, thereby identifying the user's current emotional state (e.g., nervous, relieved). The input is the emotion data sent in step 2. The output is the analyzed user's emotional state.

[0353] Step 5:

[0354] Integrated analysis

[0355] The server integrates the estimated road surface conditions and the analyzed emotional state to identify passable roads in real time. This process also takes into account past traffic data in the database. The input is the estimated road surface conditions and the analyzed emotional state of the user. The output is the analysis result of passable roads.

[0356] Step 6:

[0357] Information presentation generation

[0358] The server generates the analysis results of passable roads as visual information. The presentation method is dynamically changed to provide detailed or simple guidance depending on the user's emotional state. The input is the analysis results of passable roads obtained in step 5 and the user's emotional state. The output is visual information presentation.

[0359] Step 7:

[0360] Presentation of information

[0361] The terminal displays the visual information received from the server on the vehicle's display in real time, allowing the user to check the proposed route for safe and efficient travel. The input is the visual information sent from the server. The output is the information displayed on the terminal's display.

[0362] The above processing steps enable the user to travel safely and comfortably.

[0363] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0364] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0365] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0366] [Second embodiment]

[0367] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0368] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0369] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0370] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0371] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0372] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0373] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0374] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0375] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0376] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0377] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0378] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0379] The present invention is a system that uses electronic devices, a server, and a network for communication through them, and in particular, a system that collects and analyzes location information and acceleration data to provide passable routes in real time in the event of an emergency.

[0380] Data collection

[0381] First, when a user starts walking with an electronic device, the user launches the "Footprint Navigation" app. The device uses its GPS and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is sent to the server at regular intervals (e.g., every second).

[0382] Data analysis

[0383] The server receives the location information and acceleration data sent from the device. The server uses this data to estimate road surface conditions (e.g., flat ground, slopes, steps, etc.). The estimated road surface conditions and location information are stored in a database, and passable routes are analyzed based on this information. The server uses generative AI to analyze the latest data and identify routes that pedestrians can safely travel.

[0384] Information presentation

[0385] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the routes that other users have previously traveled. This footprint information is displayed in real time on the screen of the electronic device. This allows users to continue traveling safely while checking which routes are "passable now."

[0386] Specific examples

[0387] Example 1: Use at an event

[0388] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. User A launches "Footprint Navigation" and, as he walks from the event venue to the station, his device collects GPS and acceleration data. These data are sent to the server. The server also receives data from other users (e.g., User B and User C) who have just passed by, analyzes this data using the generation AI, and identifies a safe route. The device displays the results of this analysis on a map, allowing User A to safely reach the station.

[0389] Example 2: Evacuation during a disaster

[0390] After an earthquake occurs, User D launches the "Footprint Navigation" app and begins evacuation. The device collects GPS and acceleration data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the generation AI analyzes evacuation routes. The analysis results are integrated with data from other evacuees (Users E and F). The device displays this on a map, and User D can safely evacuate by following the visualized "footprints."

[0391] As described above, the present invention is a system for providing pedestrian-passable routes in real time during emergencies or traffic restrictions, and can support users in moving around safely.

[0392] The processing flow will be explained below.

[0393] Step 1:

[0394] The user launches the "Footprint Navigation" app. The app initializes the GPS sensor and acceleration sensor, preparing to collect location and acceleration data.

[0395] Step 2:

[0396] The device begins collecting location information (latitude, longitude, altitude) and acceleration data. This data is collected at regular intervals (e.g., every second).

[0397] Step 3:

[0398] The device stores collected location and acceleration data in a temporary buffer, which is configured to hold the most recent data.

[0399] Step 4:

[0400] The terminal transmits the data held in the buffer to the server via the network at regular intervals.

[0401] Step 5:

[0402] The server stores the received location and acceleration data in a database in real time, and the stored data is tagged with the time.

[0403] Step 6:

[0404] The server analyzes the acceleration data and estimates the road surface conditions (flat ground, slopes, steps, stairs, etc.) using accumulated past data and machine learning models.

[0405] Step 7:

[0406] The server integrates estimated road surface conditions and location information to analyze passable routes. Data collected from multiple users is used in the analysis, and the generating AI identifies safe routes.

[0407] Step 8:

[0408] The server generates the analysis results as visual information (footprints or paths on a map), which shows the latest safe routes.

[0409] Step 9:

[0410] The server transmits the generated visual information to the terminal, and the transmission is set to be performed in real time.

[0411] Step 10:

[0412] The device displays the received visual information on a map, allowing the user to check which routes are currently passable.

[0413] Step 11:

[0414] The user moves safely by following the displayed "footprints." Each time the user moves, the device again collects data and sends it to the server to obtain the latest information.

[0415] These steps enable the system to provide safe and passable routes in real time during emergencies and traffic restrictions.

[0416] Example 1

[0417] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0418] In recent years, there has been a demand for systems that provide pedestrians with real-time guidance on appropriate routes to ensure safe travel during large-scale urban events and natural disasters. However, existing systems often do not fully utilize the latest sensor data, making it difficult to quickly and accurately provide information on road surface conditions and passable routes. Furthermore, they are unable to effectively utilize other users' travel data, making it difficult to provide realistic travel information. There is an urgent need to resolve these issues.

[0419] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0420] In this invention, the server includes a means for estimating road surface conditions based on received location information and acceleration data, a means for integrating the estimated road surface conditions and location information to analyze "passable roads" and store the results in a database, and a means for generating the analysis results as visual information using a generative AI model, thereby enabling the provision of accurate passable routes based on the latest data.

[0421] A "mobile terminal" is an electronic device carried by a user, and is a device equipped with a GPS function and an acceleration sensor.

[0422] "Location information" refers to geographical data such as latitude, longitude, and altitude collected by mobile devices.

[0423] "Acceleration data" refers to data relating to the speed and direction of movement collected by a mobile terminal.

[0424] A "server" is a remote computer system capable of receiving, analyzing, storing, and transmitting data sent from multiple mobile terminals.

[0425] "Road surface conditions" refers to information indicating the difficulty and characteristics of travel (e.g., flat ground, slopes, steps, stairs, etc.) that the server estimates based on location information and acceleration data.

[0426] "Generative AI models" refer to algorithms and software that use artificial intelligence technology to analyze data and generate optimal routes and visual information.

[0427] A "passable road" refers to a route that the server has analyzed and determined to be safe for the user to travel.

[0428] "User" refers to a person who uses a mobile device to check passable routes in emergencies or special situations through the "Footprint Navigation" app and attempts to travel safely.

[0429] A "database" is an information management system that allows a server to store location information, acceleration data, analysis results, etc.

[0430] "Visual information" is information generated by the server that displays passable roads, road surface conditions, etc. as maps or graphics.

[0431] "Real-time" refers to the state in which data collection, analysis, and display occur almost simultaneously, meaning that users can use their current location information without delay.

[0432] The present invention is a system that utilizes mobile terminals, a server, and a network for communication therethrough, and has been developed to provide passable roads in real time, particularly in emergencies and events. Specific embodiments of the program for this system are described below.

[0433] Data collection

[0434] A user holds a mobile device such as a smartphone and launches the "Footprint Navigation" application. The device uses a GPS module and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. The collected data is sent to a server at regular intervals (for example, every second). This allows the user's current location and movement status to be tracked in real time.

[0435] Hardware used:

[0436] Smartphone GPS module

[0437] Smartphone accelerometer

[0438] Software used:

[0439] Footprint navigation application

[0440] Data analysis

[0441] The server receives location information and acceleration data sent from the device. The received data is analyzed using a generative AI model to estimate road surface conditions (e.g., flat ground, slopes, steps, etc.). In addition, the estimated road surface conditions are integrated with the location information, and safe route information is stored in a database.

[0442] Hardware used:

[0443] Server with high-performance data processing capabilities

[0444] Software used:

[0445] Database management system (e.g. MySQL)

[0446] Generative AI models (e.g., TensorFlow)

[0447] Information presentation

[0448] The server uses the generative AI model to generate the analysis results as visual information. This information is displayed as "passable routes" on a map that includes the user's current location. The displayed information is updated in real time, allowing users to travel safely while checking the latest passable routes.

[0449] Hardware used:

[0450] Smartphone displays

[0451] Software used:

[0452] User Interface (UI) for Footprint Navigation Application

[0453] Specific examples

[0454] Example 1: Use at an event

[0455] When a large-scale event is held in a particular city and some roads are closed to traffic, a user can launch "Footprint Navigation" and walk from the event venue to the station. The device collects GPS and acceleration data and sends it to a server. The server then integrates the data of other users and analyzes it with a generative AI model to identify a safe route. The analysis results are sent to the user's device and displayed on a map, allowing the user to reach the station safely.

[0456] Example 2: Evacuation during a disaster

[0457] When a user activates "Footprint Navigation" after an earthquake and begins evacuation, the device collects GPS and acceleration data in real time and sends it to the server. The server then analyzes this data using a generative AI model to predict road surface conditions. The analysis results also incorporate data from other evacuees, and the user's device displays this on a map. The user can then safely evacuate by following the visualized "footprints."

[0458] Prompt Sentence Examples

[0459] Below are some examples of prompt sentences:

[0460] Prompts for generating scenarios for people

[0461] "You are participating in a large-scale event in a certain city. After the event has ended, traffic restrictions have been put in place on the roads from the venue to the station, so you need to find the safest route. You will use the Footprint Navigation app to navigate by referring to the routes that other users have just taken. Please describe, step by step, how you will proceed."

[0462] Prompts for generating evacuation scenarios in the event of a disaster

[0463] "You're in a big city and experiencing an earthquake, and you need to evacuate immediately. Use the Footprint Navigation app for step-by-step instructions on how to find the safest route based on up-to-date data from other evacuees."

[0464] By introducing detailed processes to support users' safe movement in this way, this system becomes an effective means of providing pedestrian-safe routes in real time during emergencies or traffic restrictions.

[0465] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0466] Step 1:

[0467] The user launches the app. The user launches the "Footprint Navigation" app using their smartphone. This prepares the app to start collecting location and acceleration data.

[0468] Specific behavior:

[0469] The user taps the "Footprint Navigation" icon on the smartphone's home screen to launch the app.

[0470] Input: Touch operation on smartphone

[0471] Output: App launch

[0472] Step 2:

[0473] The device collects location and acceleration data. When the app is launched, the device activates the GPS module and acceleration sensor to collect latitude, longitude, altitude, and acceleration data every second.

[0474] Specific behavior:

[0475] The device obtains latitude, longitude, and altitude data from the GPS every second, and current acceleration data from the acceleration sensor.

[0476] Input: Data from the GPS module and accelerometer

[0477] Output: A set of position and acceleration data updated every second

[0478] Step 3:

[0479] The device sends the collected data to the server. At regular intervals (e.g., every second), the collected location information and acceleration data are packetized and sent to the server.

[0480] Specific behavior:

[0481] The device packages the data into packets and sends them over the internet to a server.

[0482] Input: A set of location and acceleration data

[0483] Output: Data packets sent to the server

[0484] Step 4:

[0485] The server receives and analyzes the data. Based on the received location information and acceleration data, the server uses a generative AI model to estimate road surface conditions. The estimated road surface conditions and location information are integrated to generate passable route information and store it in a database.

[0486] Specific behavior:

[0487] The server stores the received data in a database and analyzes it using a generative AI model, which combines GPS information and acceleration data to predict road surface conditions (flat ground, slopes, steps, stairs, etc.).

[0488] Input: User location and acceleration data

[0489] Output: Estimated road surface conditions and passable route information

[0490] Step 5:

[0491] The server generates passable route information. The server integrates the latest data with existing data to identify safe routes. This information is generated in a visual format and provided to the user.

[0492] Specific behavior:

[0493] The server uses the generative AI model to generate the analysis results as visual information and draw them as map layers.

[0494] Input: Estimated road surface conditions and location information

[0495] Output: Visually displayable route information

[0496] Step 6:

[0497] The device receives and displays the route information sent from the server. The user's mobile device displays the analysis results on a map and updates them in real time. This allows the user to check the latest passable routes and travel safely.

[0498] Specific behavior:

[0499] The device receives the route information sent from the server and displays it on the map screen within the app. Users can travel safely while checking their route on the map in real time.

[0500] Input: Route information sent from the server

[0501] Output: Real-time updated map display and route information

[0502] (Application example 1)

[0503] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0504] The present invention relates to a system that provides passable routes in real time in emergencies, and aims to improve safety in emergencies by identifying and providing routes that autonomous vehicles can safely travel. Conventional systems have had difficulty in understanding and analyzing situations in real time, so there has been a demand for rapid and accurate responses in emergencies.

[0505] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0506] In this invention, the server includes a means for integrating the estimated road surface conditions and location information to analyze "passable roads" and store the results in a database, a means for generating visual information about the analysis results, and a means for an electronic device mounted on the autonomous vehicle to present a safe driving route based on the analysis results. This makes it possible to provide routes that autonomous vehicles can travel safely and efficiently even in emergencies.

[0507] An "electronic device" is a device that has the ability to collect location and acceleration data and transmit it to a server.

[0508] "Location information" refers to latitude, longitude, and altitude data obtained by a GPS device.

[0509] "Acceleration data" refers to data relating to acceleration acquired by a sensor built into an electronic device.

[0510] The "server" is a computer system that receives location information and acceleration data, estimates road surface conditions, and analyzes safe routes.

[0511] "Road surface condition" refers to the physical condition of the road estimated by the server based on the received position information and acceleration data.

[0512] A "passable road" is a road route that the server has analyzed and determined to be safe to travel.

[0513] The "database" is a recording medium for integrating and storing estimated road surface conditions and position information.

[0514] "Visual information" refers to visual representation data based on the analysis results that is generated by the server for presentation to the user.

[0515] An "autonomous vehicle" is a vehicle that can drive autonomously and present safe driving routes based on data from a server.

[0516] A "safe driving route" is the optimal route for an autonomous vehicle to travel based on information about passable roads.

[0517] This invention relates to a system that utilizes electronic devices, a server, and a network for communication therethrough. This system provides safe routes for autonomous vehicles in the event of an emergency in real time.

[0518] First, a user gets into an autonomous vehicle and starts up their electronic device. The electronic device is equipped with a GPS function and an acceleration sensor, which collects location information (latitude, longitude, altitude) and acceleration data. This data is sent to a server at regular intervals (for example, every second). The electronic device used can be a typical smartphone, tablet, or the vehicle's infotainment system.

[0519] The server receives the location information and acceleration data sent from the device. The server requires high-performance computing capabilities, so it is recommended to use dedicated server equipment or cloud computing services (e.g., AWS or Google Cloud). The server uses the received data to estimate the road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It is effective to use a generative AI model for this estimation.

[0520] The server combines the estimated road surface conditions and location information to analyze passable routes. The results of this analysis are stored in a database, which can be a standard SQL database or a NoSQL database (e.g., MySQL or MongoDB). The server then re-analyzes safe driving routes based on the latest data, enabling rapid response in emergencies.

[0521] The server then generates visual information based on the analysis results and sends it to the autonomous vehicle's onboard electronics, such as the infotainment system or a smartphone, allowing the user to visually check the safe driving route in real time. Map display services such as Google Maps API can be used to generate the visual information.

[0522] Examples of specific prompts include:

[0523] text

[0524] Analyze a safe route for an autonomous vehicle based on the following data:

[0525] Current location: Latitude 35.6895, Longitude 139.6917, Altitude 15m

[0526] Acceleration data: x=0.02, y=0.03, z=9.8

[0527] Other sensor data (e.g. road conditions, obstacles, weather, etc.)

[0528] Please return the analysis results as a safe route.

[0529] Specific use cases include evacuation during disasters and traffic restrictions during large-scale events. For example, after an earthquake, the system can identify safe evacuation routes for vehicles and display them on the infotainment system, effectively supporting evacuation.

[0530] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0531] Step 1:

[0532] The user activates the electronic device installed in the vehicle and starts an application. The input is the activation of the application by the user's operation. The electronic device uses the GPS function and acceleration sensor to collect the vehicle's location information (latitude, longitude, altitude) and acceleration data. The output is the collected location information and acceleration data.

[0533] Step 2:

[0534] The electronic device sends the location information and acceleration data it collects to a server. The input is the collected location information and acceleration data, and the output is the data sent to the server. The data is collected and sent at regular intervals (e.g., every second).

[0535] Step 3:

[0536] The server estimates road surface conditions using the location information and acceleration data it receives. The input is the received location information and acceleration data, and the output is the estimated road surface conditions. A generative AI model on the server analyzes the data and estimates the type of road surface (flat ground, slope, steps, stairs, etc.).

[0537] Step 4:

[0538] The server integrates the estimated road surface conditions and location information, analyzes "passable roads," and stores the results in a database. The input is the estimated road surface conditions and location information, and the output is the analyzed "passable road" data. Analysis technology based on a generative AI model is used.

[0539] Step 5:

[0540] The server generates visual information based on the analysis results. The input is the analyzed "passable roads" and the latest road surface conditions and location information, and the output is information that can be displayed visually. To generate the visual information, map display services such as Google Maps API are used.

[0541] Step 6:

[0542] The server generates visual information and sends it to the electronics of the autonomous vehicle. The input is the visual information, and the output is the data sent to the electronics. This provides a safe driving route that is updated in real time.

[0543] Step 7:

[0544] The visual information received by the electronic devices of an autonomous vehicle is displayed on the infotainment system display. The input is visual information sent from the server, and the output is a safe driving route displayed on the display. The user can travel safely based on this information.

[0545] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0546] The present invention is a system that uses electronic devices, servers, and a network for communication therethrough, and collects and analyzes location information and acceleration data to provide passable routes in real time, especially in emergencies.Furthermore, the present invention is a system that dynamically changes the way information is presented based on the user's emotional state by combining an emotion engine that recognizes the user's emotions.

[0547] Data collection

[0548] First, when a user starts walking with an electronic device, the user launches the "Footprint Navigation" app. The device uses its GPS and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is sent to the server at regular intervals (e.g., every second).

[0549] Emotional Data Collection

[0550] At the same time, the device uses sensors such as a camera and microphone to recognize the user's emotions. The device uses the data collected from these sensors to obtain emotional data from the user's facial expressions and voice. The emotional data is also sent to the server along with location information and acceleration data.

[0551] Data analysis

[0552] The server receives location information and acceleration data transmitted from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It then analyzes the received emotion data using an emotion engine to estimate the user's emotional state (e.g., tension, relief, fear, etc.). In this way, the server integrates the estimated road surface conditions and emotional state to analyze passable routes.

[0553] Information presentation

[0554] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the paths previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of presenting information is optimized according to the user's emotional state. For example, more detailed directions are provided to a nervous user, and simple directions are provided to a relaxed user.

[0555] Specific examples

[0556] Example 1: Use at an event

[0557] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. When User A launches "Footprint Navigation" and walks from the event venue to the station, the device collects GPS data, acceleration data, and emotional data and sends it to the server. The server analyzes this data and uses a generative AI to identify a safe route. The analysis results are displayed on a map as visual information that corresponds to User A's state of mind. For example, if User A is feeling nervous, more detailed footprint information will be provided.

[0558] Example 2: Evacuation during a disaster

[0559] After an earthquake occurs, User D activates the "Footprint Navigation" app and begins evacuation. The device collects GPS data, acceleration data, and emotion data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the emotion engine analyzes User D's emotional state. The analysis results are integrated with data from other evacuees (User E and User F), and the generation AI identifies a safe evacuation route. The device displays this on a map, and User D can evacuate safely by following the visualized "footprints." If the emotional state is tense, an escort alarm and detailed instructions are provided.

[0560] As described above, the present invention can support the user's safe travels by providing safe and passable routes in real time during emergencies or traffic restrictions, and by optimizing the information presentation method based on the user's emotional state.

[0561] The processing flow will be explained below.

[0562] Step 1:

[0563] The user launches the "Footprint Navigation" app. The app initializes the GPS sensor, acceleration sensor, and emotion recognition sensors such as the camera and microphone, and prepares to collect location information, acceleration data, and emotion data.

[0564] Step 2:

[0565] The device begins collecting location information (latitude, longitude, altitude) and acceleration data. This data is collected at regular intervals (e.g., every second).

[0566] Step 3:

[0567] The device uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the emotional data, which is then stored in a buffer in real time.

[0568] Step 4:

[0569] The location information, acceleration data, and emotion data collected by the terminal are transmitted to a server via a network at regular intervals.

[0570] Step 5:

[0571] The server receives location information and acceleration data and stores it in a database in real time. The location information also includes time information.

[0572] Step 6:

[0573] The server analyzes the acceleration data and estimates the road surface conditions (e.g., flat ground, slopes, steps, etc.) using accumulated past data and machine learning models.

[0574] Step 7:

[0575] The server uses an emotion engine to analyze the received emotion data and estimate the user's emotional state (e.g., tension, relief, fear, etc.).

[0576] Step 8:

[0577] The server integrates the estimated road surface conditions, location information, and emotional state to analyze passable paths, which are then identified as safe routes by the generation AI.

[0578] Step 9:

[0579] The server generates the analysis results as visual information (footprints or routes on a map), showing the latest safe routes and responding to the user's emotional state.

[0580] Step 10:

[0581] The server generates visual information and sends it to the terminal, with the transmission set to occur in real time.

[0582] Step 11:

[0583] The device displays the received visual information on a map, allowing the user to check which routes are currently passable.

[0584] Step 12:

[0585] The user follows the displayed "footprints" to move safely. During the movement, the device continues to collect data and transmits it to the server for the latest information. If the user's emotional state changes, the display method will also dynamically change accordingly.

[0586] These steps enable the system to provide safe and passable routes in real time during emergencies or traffic restrictions, while optimizing the way information is presented based on the user's emotional state.

[0587] Example 2

[0588] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0589] Conventional systems have had difficulty providing safe and passable routes in real time during emergencies or traffic restrictions. Furthermore, they lacked the ability to dynamically change the way information is presented depending on the user's emotional state, making it impossible to provide optimal route guidance suited to the situation. As a result, there was a problem in that the system did not adequately support users' safe travels.

[0590] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for estimating road surface conditions based on received position information and acceleration data, a means for integrating the estimated road surface conditions and position information to analyze "passable roads" and store the results in a database, a means for generating visual information based on the analysis results, a means for analyzing emotion data to estimate the user's emotional state, and a means for dynamically changing the information presentation method based on the estimated emotional state. This makes it possible to provide safe and passable roads in real time during emergencies or traffic restrictions, and to present optimal information according to the user's emotional state.

[0591] "Location information" is data indicating a geographical location, including latitude, longitude, and altitude.

[0592] "Acceleration data" is data that indicates changes in the speed of an object's movement, measured by an acceleration sensor in an electronic device.

[0593] A "server" is a centralized device that receives data from clients via a network, analyzes it, and stores it.

[0594] "Road surface condition" is data indicating the condition of the ground surface, such as flat ground, slopes, steps, and stairs.

[0595] "Passable roads" is data indicating routes that are estimated to be safe for the user to travel.

[0596] A "database" is a data storage system for systematically managing and storing information.

[0597] "Emotion data" is data that indicates the emotional state of the user analyzed from facial expressions, voice, and the like.

[0598] An "emotion engine" is a software or hardware system that analyzes emotion data to infer a user's emotional state.

[0599] "Visual information" refers to information such as maps and route guidance that is visually provided to the user.

[0600] "Electronic devices" refer to devices such as smartphones and tablets that users carry with them to process information.

[0601] "Real-time" is a term that refers to instant processing of data or information that reflects its current state.

[0602] A "sensor" is a device that detects environmental or operational conditions and transmits that data to electronic devices.

[0603] This invention relates to a system that utilizes electronic devices, a server, and a network for communication via them. The system has the function of collecting and analyzing location information and acceleration data to provide passable routes in real time, particularly in emergencies. It also has the function of dynamically changing the way information is presented based on the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[0604] Data collection

[0605] First, the user installs and launches the "Footprint Navigation" application on an electronic device such as a smartphone or tablet. When the user launches the application, the device uses its GPS function and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is then sent to a server at regular intervals (e.g., every second).

[0606] Emotional Data Collection

[0607] At the same time, the device uses sensors such as a camera and microphone to recognize the user's emotions. The device uses the data collected from these sensors to obtain emotional data from the user's facial expressions and voice. The emotional data is also sent to the server along with location information and acceleration data.

[0608] Data analysis

[0609] The server receives location information and acceleration data transmitted from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It then analyzes the received emotion data using an emotion engine to estimate the user's emotional state (e.g., tension, relief, fear, etc.). In this way, the server integrates the estimated road surface conditions and emotional state to analyze passable routes.

[0610] Information presentation

[0611] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the paths previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of presenting information is optimized according to the user's emotional state. For example, more detailed directions are provided to a nervous user, and simple directions are provided to a relaxed user.

[0612] Specific examples

[0613] Example 1: Use at an event

[0614] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. When User A launches "Footprint Navigation" and walks from the event venue to the station, the device collects GPS data, acceleration data, and emotional data and sends it to the server. The server analyzes this data and uses a generative AI to identify a safe route. The analysis results are displayed on a map as visual information that corresponds to User A's state of mind. For example, if User A is feeling nervous, more detailed footprint information will be provided.

[0615] Example 2: Evacuation during a disaster

[0616] After an earthquake occurs, User D activates the "Footprint Navigation" app and begins evacuation. The device collects GPS data, acceleration data, and emotion data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the emotion engine analyzes User D's emotional state. The analysis results are integrated with data from other evacuees (User E and User F), and the generation AI identifies a safe evacuation route. The device displays this on a map, and User D can evacuate safely by following the visualized "footprints." If the emotional state is tense, an escort alarm and detailed instructions are provided.

[0617] Prompt Sentence Examples

[0618] "We have launched a project to identify safe routes available in emergencies using a footprint navigation app. Please explain the detailed steps, the software and hardware used, and the specific data processing methods."

[0619] The above is an embodiment of the present invention.

[0620] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0621] Step 1:

[0622] The user installs the "Footprint Navigation" app on their electronic device and launches it. When the app launches, the main screen appears and the user is prompted to grant access to location services, the camera, and the microphone. As input, the app is launched by the user's operation. As output, the app's main screen appears and the use of sensors is permitted.

[0623] Step 2:

[0624] The user starts walking. The device begins to obtain the user's location information (latitude, longitude, and altitude) using its GPS function. At the same time, it uses the acceleration sensor to obtain acceleration data related to the user's movements. The camera and microphone are used to collect emotion data from the user's facial expressions and voice. Data is collected from the GPS sensor, acceleration sensor, camera, and microphone as input. Location information, acceleration data, and emotion data are obtained as output.

[0625] Step 3:

[0626] The device transmits the collected location information, acceleration data, and emotion data to the server at regular intervals (e.g., every second). Data transmission is performed using a secure communication protocol (e.g., HTTPS). The collected data exists as input. These data are transmitted to the server as output.

[0627] Step 4:

[0628] The server receives the location information and acceleration data sent from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate the road surface condition (flat ground, slope, step, stairs, etc.). The received data exists as input. The analysis results and road surface condition are stored in the database as output.

[0629] Step 5:

[0630] The server analyzes the received emotion data using an emotion engine to estimate the user's emotional state (tension, relief, fear, etc.). The estimated road surface condition and emotional state are integrated to analyze a passable road. The analyzed road surface condition and emotional state are input. The analysis result of a passable road is obtained as output.

[0631] Step 6:

[0632] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the routes previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of information presentation is optimized according to the user's emotional state. The input is the analysis result of passable routes. The output is visual information, and the method of information presentation is optimized based on the user's emotional state.

[0633] Step 7:

[0634] The user's electronic device receives the visual information previously generated by the server and displays it on its screen. The user uses this information to find a safe route and receives real-time updated guidance. As input, there is the visual information sent from the server. As output, there is the visual information displayed on the user's electronic device.

[0635] The above are the specific processing steps of the system according to the present invention.

[0636] (Application example 2)

[0637] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0638] Currently, autonomous vehicles are required to improve safety and efficiency, but there is a lack of systems that provide safe and passable routes in real time, especially in emergencies. Furthermore, technology that recognizes the emotional state of users while driving and provides appropriate information accordingly is immature. As a result, current systems do not adequately provide flexible and safe travel support based on the user's emotional state. Furthermore, existing technologies are limited in their ability to collect and analyze large amounts of data in real time, making it difficult to provide safe routes, especially during large-scale events and disasters.

[0639] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0640] In this invention, the server includes means for receiving position information and acceleration data and estimating road surface conditions, means for analyzing "passable roads" in real time based on the position information, acceleration data, and emotion data, means for generating visual information based on the analysis results, and means for analyzing the user's emotional state based on the emotion data and dynamically changing the information presentation method. This allows the server to provide the optimal route in real time according to the user's emotional state, both in emergencies and under normal circumstances, enabling safe and efficient travel.

[0641] An "electronic device" is a device that can be carried by a user and has the capability to collect location information and acceleration data.

[0642] "Location information" refers to latitude, longitude, and altitude data used by electronic devices to indicate their current location.

[0643] "Acceleration data" is data used to measure changes in the movement and speed of electronic devices.

[0644] A "server" is a computer system that has the function of receiving and analyzing location information, acceleration data, and emotion data transmitted from electronic devices.

[0645] "Road surface condition" refers to the condition of the ground for travel that the server estimates based on location information and acceleration data.

[0646] A "passable road" is a route that the server has analyzed and determined to be safe to travel.

[0647] "Emotional data" is data collected to recognize a user's emotional state.

[0648] An "emotion engine" is software or a system for analyzing emotion data and inferring a user's emotional state.

[0649] "Information presentation method" refers to the means or format by which the server presents analysis results and information to the user.

[0650] "Visual information" refers to an information display format that allows the user to visually understand the analysis results and passable roads.

[0651] "Real-time" means that data is collected, analyzed, and information is presented without delay.

[0652] This invention relates to a system for supporting safe travel for users, and in particular to a program for providing real-time passable routes for autonomous vehicles. The system includes a server that collects and analyzes user location information, acceleration data, and emotion data.

[0653] Data collection

[0654] When a user activates an electronic device (for example, the console of an autonomous vehicle), sensors within the vehicle are activated. Specifically, the following sensors are installed:

[0655] GPS sensor: Collects vehicle location information (latitude, longitude, altitude).

[0656] Acceleration sensor: Acquires vehicle acceleration data to understand road surface conditions.

[0657] Cameras and microphones: Capture video and audio from inside and outside the vehicle and collect user emotional data.

[0658] Data transmission

[0659] The collected location, acceleration, and emotion data is transmitted to a server by the on-board computer in the autonomous vehicle. The transmitted data includes:

[0660] Location information: latitude, longitude, altitude

[0661] Acceleration data: Vehicle speed changes and vibration state

[0662] Emotion data: Emotional state obtained from the user's facial expressions and voice

[0663] Data analysis

[0664] The server analyzes the received data using the following steps:

[0665] 1. Analysis of location information and acceleration data: Based on location information and acceleration data, road surface conditions (flat ground, slopes, steps, etc.) are estimated.

[0666] 2. Emotion data analysis: Using the emotion engine, the user's emotional state (e.g., tension, relief) is analyzed from the collected emotion data.

[0667] 3. Integrated analysis: Integrate the estimated road surface conditions and the analyzed emotional state to identify passable paths.

[0668] Information presentation

[0669] The server generates the analysis results as visual information. This information is displayed in real time on the vehicle's display. The way the information is presented changes dynamically depending on the user's emotional state. Specifically, the following display formats are possible:

[0670] 1. Detailed guidance: If the user is nervous, detailed routes and precautions will be displayed.

[0671] 2. Simple guidance: If the user is comfortable, simple route information is displayed.

[0672] Specific examples

[0673] For example, when a large-scale event is being held in a city, certain roads may be unavailable. The vehicle's "Safety Navigation AI" uses GPS sensors, acceleration sensors, cameras, and microphones to collect and transmit data in real time. The server analyzes the received data, identifies the optimal route, and displays detailed or simple directions according to the user's emotional state.

[0674] Prompt statement

[0675] Data collection prompt statements

[0676] gps_data = get_gps_data()

[0677] acceleration_data = get_acceleration_data()

[0678] emotion_data = get_emotion_data()

[0679] Data transmission and route acquisition prompts

[0680] response = send_all_data(gps_data, acceleration_data, emotion_data)

[0681] route_data = get_route(response)

[0682] Root display prompt statement

[0683] display_route(route_data)

[0684] As described above, analyzing the user's location information and emotional data and providing appropriate route guidance can improve the safety and efficiency of self-driving vehicles.

[0685] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0686] Step 1:

[0687] Data collection

[0688] When a user starts up an electronic device, the device collects data using a GPS sensor, accelerometer, camera, and microphone. This data includes the current location (latitude, longitude, and altitude), the vehicle's acceleration, and emotion data from the user's facial expressions and voice. The inputs are location information from the GPS sensor, movement data from the accelerometer, and video and audio data from the camera and microphone. The output is the collected location information, acceleration data, and emotion data.

[0689] Step 2:

[0690] Data transmission

[0691] The device transmits the collected location information, acceleration data, and emotion data to the server via the on-board computer. This data transmission process uses a specific communication protocol (e.g., HTTP or MQTT). The input is the location information, acceleration data, and emotion data collected in step 1. The output is these data transmitted to the server.

[0692] Step 3:

[0693] Location and acceleration data analysis

[0694] The server analyzes road surface conditions based on the location information and acceleration data received. This involves estimating road surface conditions, such as flat ground, slopes, and steps, by comparing them with past and current data in a database. The input is location information and acceleration data. The output is the estimated road surface conditions.

[0695] Step 4:

[0696] Emotional Data Analysis

[0697] The server uses an emotion engine to analyze the received emotion data, thereby identifying the user's current emotional state (e.g., nervous, relieved). The input is the emotion data sent in step 2. The output is the analyzed user's emotional state.

[0698] Step 5:

[0699] Integrated analysis

[0700] The server integrates the estimated road surface conditions and the analyzed emotional state to identify passable roads in real time. This process also takes into account past traffic data in the database. The input is the estimated road surface conditions and the analyzed emotional state of the user. The output is the analysis result of passable roads.

[0701] Step 6:

[0702] Information presentation generation

[0703] The server generates the analysis results of passable roads as visual information. The presentation method is dynamically changed to provide detailed or simple guidance depending on the user's emotional state. The input is the analysis results of passable roads obtained in step 5 and the user's emotional state. The output is visual information presentation.

[0704] Step 7:

[0705] Presentation of information

[0706] The terminal displays the visual information received from the server on the vehicle's display in real time, allowing the user to check the proposed route for safe and efficient travel. The input is the visual information sent from the server. The output is the information displayed on the terminal's display.

[0707] The above processing steps enable the user to travel safely and comfortably.

[0708] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0709] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0710] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0711] [Third embodiment]

[0712] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0713] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0714] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0715] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0716] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0717] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0718] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0719] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0720] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0721] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0722] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0723] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0724] The present invention is a system that uses electronic devices, a server, and a network for communication through them, and in particular, a system that collects and analyzes location information and acceleration data to provide passable routes in real time in the event of an emergency.

[0725] Data collection

[0726] First, when a user starts walking with an electronic device, the user launches the "Footprint Navigation" app. The device uses its GPS and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is sent to the server at regular intervals (e.g., every second).

[0727] Data analysis

[0728] The server receives the location information and acceleration data sent from the device. The server uses this data to estimate road surface conditions (e.g., flat ground, slopes, steps, etc.). The estimated road surface conditions and location information are stored in a database, and passable routes are analyzed based on this information. The server uses generative AI to analyze the latest data and identify routes that pedestrians can safely travel.

[0729] Information presentation

[0730] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the routes that other users have previously traveled. This footprint information is displayed in real time on the screen of the electronic device. This allows users to continue traveling safely while checking which routes are "passable now."

[0731] Specific examples

[0732] Example 1: Use at an event

[0733] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. User A launches "Footprint Navigation" and, as he walks from the event venue to the station, his device collects GPS and acceleration data. These data are sent to the server. The server also receives data from other users (e.g., User B and User C) who have just passed by, analyzes this data using the generation AI, and identifies a safe route. The device displays the results of this analysis on a map, allowing User A to safely reach the station.

[0734] Example 2: Evacuation during a disaster

[0735] After an earthquake occurs, User D launches the "Footprint Navigation" app and begins evacuation. The device collects GPS and acceleration data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the generation AI analyzes evacuation routes. The analysis results are integrated with data from other evacuees (Users E and F). The device displays this on a map, and User D can safely evacuate by following the visualized "footprints."

[0736] As described above, the present invention is a system for providing pedestrian-passable routes in real time during emergencies or traffic restrictions, and can support users in moving around safely.

[0737] The processing flow will be explained below.

[0738] Step 1:

[0739] The user launches the "Footprint Navigation" app. The app initializes the GPS sensor and acceleration sensor, preparing to collect location and acceleration data.

[0740] Step 2:

[0741] The device begins collecting location information (latitude, longitude, altitude) and acceleration data. This data is collected at regular intervals (e.g., every second).

[0742] Step 3:

[0743] The device stores collected location and acceleration data in a temporary buffer, which is configured to hold the most recent data.

[0744] Step 4:

[0745] The terminal transmits the data held in the buffer to the server via the network at regular intervals.

[0746] Step 5:

[0747] The server stores the received location and acceleration data in a database in real time, and the stored data is tagged with the time.

[0748] Step 6:

[0749] The server analyzes the acceleration data and estimates the road surface conditions (flat ground, slopes, steps, stairs, etc.) using accumulated past data and machine learning models.

[0750] Step 7:

[0751] The server integrates estimated road surface conditions and location information to analyze passable routes. Data collected from multiple users is used in the analysis, and the generating AI identifies safe routes.

[0752] Step 8:

[0753] The server generates the analysis results as visual information (footprints or paths on a map), which shows the latest safe routes.

[0754] Step 9:

[0755] The server transmits the generated visual information to the terminal, and the transmission is set to be performed in real time.

[0756] Step 10:

[0757] The device displays the received visual information on a map, allowing the user to check which routes are currently passable.

[0758] Step 11:

[0759] The user moves safely by following the displayed "footprints." Each time the user moves, the device again collects data and sends it to the server to obtain the latest information.

[0760] These steps enable the system to provide safe and passable routes in real time during emergencies and traffic restrictions.

[0761] Example 1

[0762] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0763] In recent years, there has been a demand for systems that provide pedestrians with real-time guidance on appropriate routes to ensure safe travel during large-scale urban events and natural disasters. However, existing systems often do not fully utilize the latest sensor data, making it difficult to quickly and accurately provide information on road surface conditions and passable routes. Furthermore, they are unable to effectively utilize other users' travel data, making it difficult to provide realistic travel information. There is an urgent need to resolve these issues.

[0764] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0765] In this invention, the server includes a means for estimating road surface conditions based on received location information and acceleration data, a means for integrating the estimated road surface conditions and location information to analyze "passable roads" and store the results in a database, and a means for generating the analysis results as visual information using a generative AI model, thereby enabling the provision of accurate passable routes based on the latest data.

[0766] A "mobile terminal" is an electronic device carried by a user, and is a device equipped with a GPS function and an acceleration sensor.

[0767] "Location information" refers to geographical data such as latitude, longitude, and altitude collected by mobile devices.

[0768] "Acceleration data" refers to data relating to the speed and direction of movement collected by a mobile terminal.

[0769] A "server" is a remote computer system capable of receiving, analyzing, storing, and transmitting data sent from multiple mobile terminals.

[0770] "Road surface conditions" refers to information indicating the difficulty and characteristics of travel (e.g., flat ground, slopes, steps, stairs, etc.) that the server estimates based on location information and acceleration data.

[0771] "Generative AI models" refer to algorithms and software that use artificial intelligence technology to analyze data and generate optimal routes and visual information.

[0772] A "passable road" refers to a route that the server has analyzed and determined to be safe for the user to travel.

[0773] "User" refers to a person who uses a mobile device to check passable routes in emergencies or special situations through the "Footprint Navigation" app and attempts to travel safely.

[0774] A "database" is an information management system that allows a server to store location information, acceleration data, analysis results, etc.

[0775] "Visual information" is information generated by the server that displays passable roads, road surface conditions, etc. as maps or graphics.

[0776] "Real-time" refers to the state in which data collection, analysis, and display occur almost simultaneously, meaning that users can use their current location information without delay.

[0777] The present invention is a system that utilizes mobile terminals, a server, and a network for communication therethrough, and has been developed to provide passable roads in real time, particularly in emergencies and events. Specific embodiments of the program for this system are described below.

[0778] Data collection

[0779] A user holds a mobile device such as a smartphone and launches the "Footprint Navigation" application. The device uses a GPS module and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. The collected data is sent to a server at regular intervals (for example, every second). This allows the user's current location and movement status to be tracked in real time.

[0780] Hardware used:

[0781] Smartphone GPS module

[0782] Smartphone accelerometer

[0783] Software used:

[0784] Footprint navigation application

[0785] Data analysis

[0786] The server receives location information and acceleration data sent from the device. The received data is analyzed using a generative AI model to estimate road surface conditions (e.g., flat ground, slopes, steps, etc.). In addition, the estimated road surface conditions are integrated with the location information, and safe route information is stored in a database.

[0787] Hardware used:

[0788] Server with high-performance data processing capabilities

[0789] Software used:

[0790] Database management system (e.g. MySQL)

[0791] Generative AI models (e.g., TensorFlow)

[0792] Information presentation

[0793] The server uses the generative AI model to generate the analysis results as visual information. This information is displayed as "passable routes" on a map that includes the user's current location. The displayed information is updated in real time, allowing users to travel safely while checking the latest passable routes.

[0794] Hardware used:

[0795] Smartphone displays

[0796] Software used:

[0797] User Interface (UI) for Footprint Navigation Application

[0798] Specific examples

[0799] Example 1: Use at an event

[0800] When a large-scale event is held in a particular city and some roads are closed to traffic, a user can launch "Footprint Navigation" and walk from the event venue to the station. The device collects GPS and acceleration data and sends it to a server. The server then integrates the data of other users and analyzes it with a generative AI model to identify a safe route. The analysis results are sent to the user's device and displayed on a map, allowing the user to reach the station safely.

[0801] Example 2: Evacuation during a disaster

[0802] When a user activates "Footprint Navigation" after an earthquake and begins evacuation, the device collects GPS and acceleration data in real time and sends it to the server. The server then analyzes this data using a generative AI model to predict road surface conditions. The analysis results also incorporate data from other evacuees, and the user's device displays this on a map. The user can then safely evacuate by following the visualized "footprints."

[0803] Prompt Sentence Examples

[0804] Below are some examples of prompt sentences:

[0805] Prompts for generating scenarios for people

[0806] "You are participating in a large-scale event in a certain city. After the event has ended, traffic restrictions have been put in place on the roads from the venue to the station, so you need to find the safest route. You will use the Footprint Navigation app to navigate by referring to the routes that other users have just taken. Please describe, step by step, how you will proceed."

[0807] Prompts for generating evacuation scenarios in the event of a disaster

[0808] "You're in a big city and experiencing an earthquake, and you need to evacuate immediately. Use the Footprint Navigation app for step-by-step instructions on how to find the safest route based on up-to-date data from other evacuees."

[0809] By introducing detailed processes to support users' safe movement in this way, this system becomes an effective means of providing pedestrian-safe routes in real time during emergencies or traffic restrictions.

[0810] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0811] Step 1:

[0812] The user launches the app. The user launches the "Footprint Navigation" app using their smartphone. This prepares the app to start collecting location and acceleration data.

[0813] Specific behavior:

[0814] The user taps the "Footprint Navigation" icon on the smartphone's home screen to launch the app.

[0815] Input: Touch operation on smartphone

[0816] Output: App launch

[0817] Step 2:

[0818] The device collects location and acceleration data. When the app is launched, the device activates the GPS module and acceleration sensor to collect latitude, longitude, altitude, and acceleration data every second.

[0819] Specific behavior:

[0820] The device obtains latitude, longitude, and altitude data from the GPS every second, and current acceleration data from the acceleration sensor.

[0821] Input: Data from the GPS module and accelerometer

[0822] Output: A set of position and acceleration data updated every second

[0823] Step 3:

[0824] The device sends the collected data to the server. At regular intervals (e.g., every second), the collected location information and acceleration data are packetized and sent to the server.

[0825] Specific behavior:

[0826] The device packages the data into packets and sends them over the internet to a server.

[0827] Input: A set of location and acceleration data

[0828] Output: Data packets sent to the server

[0829] Step 4:

[0830] The server receives and analyzes the data. Based on the received location information and acceleration data, the server uses a generative AI model to estimate road surface conditions. The estimated road surface conditions and location information are integrated to generate passable route information and store it in a database.

[0831] Specific behavior:

[0832] The server stores the received data in a database and analyzes it using a generative AI model, which combines GPS information and acceleration data to predict road surface conditions (flat ground, slopes, steps, stairs, etc.).

[0833] Input: User location and acceleration data

[0834] Output: Estimated road surface conditions and passable route information

[0835] Step 5:

[0836] The server generates passable route information. The server integrates the latest data with existing data to identify safe routes. This information is generated in a visual format and provided to the user.

[0837] Specific behavior:

[0838] The server uses the generative AI model to generate the analysis results as visual information and draw them as map layers.

[0839] Input: Estimated road surface conditions and location information

[0840] Output: Visually displayable route information

[0841] Step 6:

[0842] The device receives and displays the route information sent from the server. The user's mobile device displays the analysis results on a map and updates them in real time. This allows the user to check the latest passable routes and travel safely.

[0843] Specific behavior:

[0844] The device receives the route information sent from the server and displays it on the map screen within the app. Users can travel safely while checking their route on the map in real time.

[0845] Input: Route information sent from the server

[0846] Output: Real-time updated map display and route information

[0847] (Application example 1)

[0848] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0849] The present invention relates to a system that provides passable routes in real time in emergencies, and aims to improve safety in emergencies by identifying and providing routes that autonomous vehicles can safely travel. Conventional systems have had difficulty in understanding and analyzing situations in real time, so there has been a demand for rapid and accurate responses in emergencies.

[0850] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0851] In this invention, the server includes a means for integrating the estimated road surface conditions and location information to analyze "passable roads" and store the results in a database, a means for generating visual information about the analysis results, and a means for an electronic device mounted on the autonomous vehicle to present a safe driving route based on the analysis results. This makes it possible to provide routes that autonomous vehicles can travel safely and efficiently even in emergencies.

[0852] An "electronic device" is a device that has the ability to collect location and acceleration data and transmit it to a server.

[0853] "Location information" refers to latitude, longitude, and altitude data obtained by a GPS device.

[0854] "Acceleration data" refers to data relating to acceleration acquired by a sensor built into an electronic device.

[0855] The "server" is a computer system that receives location information and acceleration data, estimates road surface conditions, and analyzes safe routes.

[0856] "Road surface condition" refers to the physical condition of the road estimated by the server based on the received position information and acceleration data.

[0857] A "passable road" is a road route that the server has analyzed and determined to be safe to travel.

[0858] The "database" is a recording medium for integrating and storing estimated road surface conditions and position information.

[0859] "Visual information" refers to visual representation data based on the analysis results that is generated by the server for presentation to the user.

[0860] An "autonomous vehicle" is a vehicle that can drive autonomously and present safe driving routes based on data from a server.

[0861] A "safe driving route" is the optimal route for an autonomous vehicle to travel based on information about passable roads.

[0862] This invention relates to a system that utilizes electronic devices, a server, and a network for communication therethrough. This system provides safe routes for autonomous vehicles in the event of an emergency in real time.

[0863] First, a user gets into an autonomous vehicle and starts up their electronic device. The electronic device is equipped with a GPS function and an acceleration sensor, which collects location information (latitude, longitude, altitude) and acceleration data. This data is sent to a server at regular intervals (for example, every second). The electronic device used can be a typical smartphone, tablet, or the vehicle's infotainment system.

[0864] The server receives the location information and acceleration data sent from the device. The server requires high-performance computing capabilities, so it is recommended to use dedicated server equipment or cloud computing services (e.g., AWS or Google Cloud). The server uses the received data to estimate the road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It is effective to use a generative AI model for this estimation.

[0865] The server combines the estimated road surface conditions and location information to analyze passable routes. The results of this analysis are stored in a database, which can be a standard SQL database or a NoSQL database (e.g., MySQL or MongoDB). The server then re-analyzes safe driving routes based on the latest data, enabling rapid response in emergencies.

[0866] The server then generates visual information based on the analysis results and sends it to the autonomous vehicle's onboard electronics, such as the infotainment system or a smartphone, allowing the user to visually check the safe driving route in real time. Map display services such as Google Maps API can be used to generate the visual information.

[0867] Examples of specific prompts include:

[0868] text

[0869] Analyze a safe route for an autonomous vehicle based on the following data:

[0870] Current location: Latitude 35.6895, Longitude 139.6917, Altitude 15m

[0871] Acceleration data: x=0.02, y=0.03, z=9.8

[0872] Other sensor data (e.g. road conditions, obstacles, weather, etc.)

[0873] Please return the analysis results as a safe route.

[0874] Specific use cases include evacuation during disasters and traffic restrictions during large-scale events. For example, after an earthquake, the system can identify safe evacuation routes for vehicles and display them on the infotainment system, effectively supporting evacuation.

[0875] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0876] Step 1:

[0877] The user activates the electronic device installed in the vehicle and starts an application. The input is the activation of the application by the user's operation. The electronic device uses the GPS function and acceleration sensor to collect the vehicle's location information (latitude, longitude, altitude) and acceleration data. The output is the collected location information and acceleration data.

[0878] Step 2:

[0879] The electronic device sends the location information and acceleration data it collects to a server. The input is the collected location information and acceleration data, and the output is the data sent to the server. The data is collected and sent at regular intervals (e.g., every second).

[0880] Step 3:

[0881] The server estimates road surface conditions using the location information and acceleration data it receives. The input is the received location information and acceleration data, and the output is the estimated road surface conditions. A generative AI model on the server analyzes the data and estimates the type of road surface (flat ground, slope, steps, stairs, etc.).

[0882] Step 4:

[0883] The server integrates the estimated road surface conditions and location information, analyzes "passable roads," and stores the results in a database. The input is the estimated road surface conditions and location information, and the output is the analyzed "passable road" data. Analysis technology based on a generative AI model is used.

[0884] Step 5:

[0885] The server generates visual information based on the analysis results. The input is the analyzed "passable roads" and the latest road surface conditions and location information, and the output is information that can be displayed visually. To generate the visual information, map display services such as Google Maps API are used.

[0886] Step 6:

[0887] The server generates visual information and sends it to the electronics of the autonomous vehicle. The input is the visual information, and the output is the data sent to the electronics. This provides a safe driving route that is updated in real time.

[0888] Step 7:

[0889] The visual information received by the electronic devices of an autonomous vehicle is displayed on the infotainment system display. The input is visual information sent from the server, and the output is a safe driving route displayed on the display. The user can travel safely based on this information.

[0890] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0891] The present invention is a system that uses electronic devices, servers, and a network for communication therethrough, and collects and analyzes location information and acceleration data to provide passable routes in real time, especially in emergencies.Furthermore, the present invention is a system that dynamically changes the way information is presented based on the user's emotional state by combining an emotion engine that recognizes the user's emotions.

[0892] Data collection

[0893] First, when a user starts walking with an electronic device, the user launches the "Footprint Navigation" app. The device uses its GPS and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is sent to the server at regular intervals (e.g., every second).

[0894] Emotional Data Collection

[0895] At the same time, the device uses sensors such as a camera and microphone to recognize the user's emotions. The device uses the data collected from these sensors to obtain emotional data from the user's facial expressions and voice. The emotional data is also sent to the server along with location information and acceleration data.

[0896] Data analysis

[0897] The server receives location information and acceleration data transmitted from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It then analyzes the received emotion data using an emotion engine to estimate the user's emotional state (e.g., tension, relief, fear, etc.). In this way, the server integrates the estimated road surface conditions and emotional state to analyze passable routes.

[0898] Information presentation

[0899] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the paths previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of presenting information is optimized according to the user's emotional state. For example, more detailed directions are provided to a nervous user, and simple directions are provided to a relaxed user.

[0900] Specific examples

[0901] Example 1: Use at an event

[0902] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. When User A launches "Footprint Navigation" and walks from the event venue to the station, the device collects GPS data, acceleration data, and emotional data and sends it to the server. The server analyzes this data and uses a generative AI to identify a safe route. The analysis results are displayed on a map as visual information that corresponds to User A's state of mind. For example, if User A is feeling nervous, more detailed footprint information will be provided.

[0903] Example 2: Evacuation during a disaster

[0904] After an earthquake occurs, User D activates the "Footprint Navigation" app and begins evacuation. The device collects GPS data, acceleration data, and emotion data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the emotion engine analyzes User D's emotional state. The analysis results are integrated with data from other evacuees (User E and User F), and the generation AI identifies a safe evacuation route. The device displays this on a map, and User D can evacuate safely by following the visualized "footprints." If the emotional state is tense, an escort alarm and detailed instructions are provided.

[0905] As described above, the present invention can support the user's safe travels by providing safe and passable routes in real time during emergencies or traffic restrictions, and by optimizing the information presentation method based on the user's emotional state.

[0906] The processing flow will be explained below.

[0907] Step 1:

[0908] The user launches the "Footprint Navigation" app. The app initializes the GPS sensor, acceleration sensor, and emotion recognition sensors such as the camera and microphone, and prepares to collect location information, acceleration data, and emotion data.

[0909] Step 2:

[0910] The device begins collecting location information (latitude, longitude, altitude) and acceleration data. This data is collected at regular intervals (e.g., every second).

[0911] Step 3:

[0912] The device uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the emotional data, which is then stored in a buffer in real time.

[0913] Step 4:

[0914] The location information, acceleration data, and emotion data collected by the terminal are transmitted to a server via a network at regular intervals.

[0915] Step 5:

[0916] The server receives location information and acceleration data and stores it in a database in real time. The location information also includes time information.

[0917] Step 6:

[0918] The server analyzes the acceleration data and estimates the road surface conditions (e.g., flat ground, slopes, steps, etc.) using accumulated past data and machine learning models.

[0919] Step 7:

[0920] The server uses an emotion engine to analyze the received emotion data and estimate the user's emotional state (e.g., tension, relief, fear, etc.).

[0921] Step 8:

[0922] The server integrates the estimated road surface conditions, location information, and emotional state to analyze passable paths, which are then identified as safe routes by the generation AI.

[0923] Step 9:

[0924] The server generates the analysis results as visual information (footprints or routes on a map), showing the latest safe routes and responding to the user's emotional state.

[0925] Step 10:

[0926] The server generates visual information and sends it to the terminal, with the transmission set to occur in real time.

[0927] Step 11:

[0928] The device displays the received visual information on a map, allowing the user to check which routes are currently passable.

[0929] Step 12:

[0930] The user follows the displayed "footprints" to move safely. During the movement, the device continues to collect data and transmits it to the server for the latest information. If the user's emotional state changes, the display method will also dynamically change accordingly.

[0931] These steps enable the system to provide safe and passable routes in real time during emergencies or traffic restrictions, while optimizing the way information is presented based on the user's emotional state.

[0932] Example 2

[0933] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0934] Conventional systems have had difficulty providing safe and passable routes in real time during emergencies or traffic restrictions. Furthermore, they lacked the ability to dynamically change the way information is presented depending on the user's emotional state, making it impossible to provide optimal route guidance suited to the situation. As a result, there was a problem in that the system did not adequately support users' safe travels.

[0935] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for estimating road surface conditions based on received position information and acceleration data, a means for integrating the estimated road surface conditions and position information to analyze "passable roads" and store the results in a database, a means for generating visual information based on the analysis results, a means for analyzing emotion data to estimate the user's emotional state, and a means for dynamically changing the information presentation method based on the estimated emotional state. This makes it possible to provide safe and passable roads in real time during emergencies or traffic restrictions, and to present optimal information according to the user's emotional state.

[0936] "Location information" is data indicating a geographical location, including latitude, longitude, and altitude.

[0937] "Acceleration data" is data that indicates changes in the speed of an object's movement, measured by an acceleration sensor in an electronic device.

[0938] A "server" is a centralized device that receives data from clients via a network, analyzes it, and stores it.

[0939] "Road surface condition" is data indicating the condition of the ground surface, such as flat ground, slopes, steps, and stairs.

[0940] "Passable roads" is data indicating routes that are estimated to be safe for the user to travel.

[0941] A "database" is a data storage system for systematically managing and storing information.

[0942] "Emotion data" is data that indicates the emotional state of the user analyzed from facial expressions, voice, and the like.

[0943] An "emotion engine" is a software or hardware system that analyzes emotion data to infer a user's emotional state.

[0944] "Visual information" refers to information such as maps and route guidance that is visually provided to the user.

[0945] "Electronic devices" refer to devices such as smartphones and tablets that users carry with them to process information.

[0946] "Real-time" is a term that refers to instant processing of data or information that reflects its current state.

[0947] A "sensor" is a device that detects environmental or operational conditions and transmits that data to electronic devices.

[0948] This invention relates to a system that utilizes electronic devices, a server, and a network for communication via them. The system has the function of collecting and analyzing location information and acceleration data to provide passable routes in real time, particularly in emergencies. It also has the function of dynamically changing the way information is presented based on the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[0949] Data collection

[0950] First, the user installs and launches the "Footprint Navigation" application on an electronic device such as a smartphone or tablet. When the user launches the application, the device uses its GPS function and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is then sent to a server at regular intervals (e.g., every second).

[0951] Emotional Data Collection

[0952] At the same time, the device uses sensors such as a camera and microphone to recognize the user's emotions. The device uses the data collected from these sensors to obtain emotional data from the user's facial expressions and voice. The emotional data is also sent to the server along with location information and acceleration data.

[0953] Data analysis

[0954] The server receives location information and acceleration data transmitted from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It then analyzes the received emotion data using an emotion engine to estimate the user's emotional state (e.g., tension, relief, fear, etc.). In this way, the server integrates the estimated road surface conditions and emotional state to analyze passable routes.

[0955] Information presentation

[0956] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the paths previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of presenting information is optimized according to the user's emotional state. For example, more detailed directions are provided to a nervous user, and simple directions are provided to a relaxed user.

[0957] Specific examples

[0958] Example 1: Use at an event

[0959] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. When User A launches "Footprint Navigation" and walks from the event venue to the station, the device collects GPS data, acceleration data, and emotional data and sends it to the server. The server analyzes this data and uses a generative AI to identify a safe route. The analysis results are displayed on a map as visual information that corresponds to User A's state of mind. For example, if User A is feeling nervous, more detailed footprint information will be provided.

[0960] Example 2: Evacuation during a disaster

[0961] After an earthquake occurs, User D activates the "Footprint Navigation" app and begins evacuation. The device collects GPS data, acceleration data, and emotion data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the emotion engine analyzes User D's emotional state. The analysis results are integrated with data from other evacuees (User E and User F), and the generation AI identifies a safe evacuation route. The device displays this on a map, and User D can evacuate safely by following the visualized "footprints." If the emotional state is tense, an escort alarm and detailed instructions are provided.

[0962] Prompt Sentence Examples

[0963] "We have launched a project to identify safe routes available in emergencies using a footprint navigation app. Please explain the detailed steps, the software and hardware used, and the specific data processing methods."

[0964] The above is an embodiment of the present invention.

[0965] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0966] Step 1:

[0967] The user installs the "Footprint Navigation" app on their electronic device and launches it. When the app launches, the main screen appears and the user is prompted to grant access to location services, the camera, and the microphone. As input, the app is launched by the user's operation. As output, the app's main screen appears and the use of sensors is permitted.

[0968] Step 2:

[0969] The user starts walking. The device begins to obtain the user's location information (latitude, longitude, and altitude) using its GPS function. At the same time, it uses the acceleration sensor to obtain acceleration data related to the user's movements. The camera and microphone are used to collect emotion data from the user's facial expressions and voice. Data is collected from the GPS sensor, acceleration sensor, camera, and microphone as input. Location information, acceleration data, and emotion data are obtained as output.

[0970] Step 3:

[0971] The device transmits the collected location information, acceleration data, and emotion data to the server at regular intervals (e.g., every second). Data transmission is performed using a secure communication protocol (e.g., HTTPS). The collected data exists as input. These data are transmitted to the server as output.

[0972] Step 4:

[0973] The server receives the location information and acceleration data sent from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate the road surface condition (flat ground, slope, step, stairs, etc.). The received data exists as input. The analysis results and road surface condition are stored in the database as output.

[0974] Step 5:

[0975] The server analyzes the received emotion data using an emotion engine to estimate the user's emotional state (tension, relief, fear, etc.). The estimated road surface condition and emotional state are integrated to analyze a passable road. The analyzed road surface condition and emotional state are input. The analysis result of a passable road is obtained as output.

[0976] Step 6:

[0977] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the routes previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of information presentation is optimized according to the user's emotional state. The input is the analysis result of passable routes. The output is visual information, and the method of information presentation is optimized based on the user's emotional state.

[0978] Step 7:

[0979] The user's electronic device receives the visual information previously generated by the server and displays it on its screen. The user uses this information to find a safe route and receives real-time updated guidance. As input, there is the visual information sent from the server. As output, there is the visual information displayed on the user's electronic device.

[0980] The above are the specific processing steps of the system according to the present invention.

[0981] (Application example 2)

[0982] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0983] Currently, autonomous vehicles are required to improve safety and efficiency, but there is a lack of systems that provide safe and passable routes in real time, especially in emergencies. Furthermore, technology that recognizes the emotional state of users while driving and provides appropriate information accordingly is immature. As a result, current systems do not adequately provide flexible and safe travel support based on the user's emotional state. Furthermore, existing technologies are limited in their ability to collect and analyze large amounts of data in real time, making it difficult to provide safe routes, especially during large-scale events and disasters.

[0984] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0985] In this invention, the server includes means for receiving position information and acceleration data and estimating road surface conditions, means for analyzing "passable roads" in real time based on the position information, acceleration data, and emotion data, means for generating visual information based on the analysis results, and means for analyzing the user's emotional state based on the emotion data and dynamically changing the information presentation method. This allows the server to provide the optimal route in real time according to the user's emotional state, both in emergencies and under normal circumstances, enabling safe and efficient travel.

[0986] An "electronic device" is a device that can be carried by a user and has the capability to collect location information and acceleration data.

[0987] "Location information" refers to latitude, longitude, and altitude data used by electronic devices to indicate their current location.

[0988] "Acceleration data" is data used to measure changes in the movement and speed of electronic devices.

[0989] A "server" is a computer system that has the function of receiving and analyzing location information, acceleration data, and emotion data transmitted from electronic devices.

[0990] "Road surface condition" refers to the condition of the ground for travel that the server estimates based on location information and acceleration data.

[0991] A "passable road" is a route that the server has analyzed and determined to be safe to travel.

[0992] "Emotional data" is data collected to recognize a user's emotional state.

[0993] An "emotion engine" is software or a system for analyzing emotion data and inferring a user's emotional state.

[0994] "Information presentation method" refers to the means or format by which the server presents analysis results and information to the user.

[0995] "Visual information" refers to an information display format that allows the user to visually understand the analysis results and passable roads.

[0996] "Real-time" means that data is collected, analyzed, and information is presented without delay.

[0997] This invention relates to a system for supporting safe travel for users, and in particular to a program for providing real-time passable routes for autonomous vehicles. The system includes a server that collects and analyzes user location information, acceleration data, and emotion data.

[0998] Data collection

[0999] When a user activates an electronic device (for example, the console of an autonomous vehicle), sensors within the vehicle are activated. Specifically, the following sensors are installed:

[1000] GPS sensor: Collects vehicle location information (latitude, longitude, altitude).

[1001] Acceleration sensor: Acquires vehicle acceleration data to understand road surface conditions.

[1002] Cameras and microphones: Capture video and audio from inside and outside the vehicle and collect user emotional data.

[1003] Data transmission

[1004] The collected location, acceleration, and emotion data is transmitted to a server by the on-board computer in the autonomous vehicle. The transmitted data includes:

[1005] Location information: latitude, longitude, altitude

[1006] Acceleration data: Vehicle speed changes and vibration state

[1007] Emotion data: Emotional state obtained from the user's facial expressions and voice

[1008] Data analysis

[1009] The server analyzes the received data using the following steps:

[1010] 1. Analysis of location information and acceleration data: Based on location information and acceleration data, road surface conditions (flat ground, slopes, steps, etc.) are estimated.

[1011] 2. Emotion data analysis: Using the emotion engine, the user's emotional state (e.g., tension, relief) is analyzed from the collected emotion data.

[1012] 3. Integrated analysis: Integrate the estimated road surface conditions and the analyzed emotional state to identify passable paths.

[1013] Information presentation

[1014] The server generates the analysis results as visual information. This information is displayed in real time on the vehicle's display. The way the information is presented changes dynamically depending on the user's emotional state. Specifically, the following display formats are possible:

[1015] 1. Detailed guidance: If the user is nervous, detailed routes and precautions will be displayed.

[1016] 2. Simple guidance: If the user is comfortable, simple route information is displayed.

[1017] Specific examples

[1018] For example, when a large-scale event is being held in a city, certain roads may be unavailable. The vehicle's "Safety Navigation AI" uses GPS sensors, acceleration sensors, cameras, and microphones to collect and transmit data in real time. The server analyzes the received data, identifies the optimal route, and displays detailed or simple directions according to the user's emotional state.

[1019] Prompt statement

[1020] Data collection prompt statements

[1021] gps_data = get_gps_data()

[1022] acceleration_data = get_acceleration_data()

[1023] emotion_data = get_emotion_data()

[1024] Data transmission and route acquisition prompts

[1025] response = send_all_data(gps_data, acceleration_data, emotion_data)

[1026] route_data = get_route(response)

[1027] Root display prompt statement

[1028] display_route(route_data)

[1029] As described above, analyzing the user's location information and emotional data and providing appropriate route guidance can improve the safety and efficiency of self-driving vehicles.

[1030] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1031] Step 1:

[1032] Data collection

[1033] When a user starts up an electronic device, the device collects data using a GPS sensor, accelerometer, camera, and microphone. This data includes the current location (latitude, longitude, and altitude), the vehicle's acceleration, and emotion data from the user's facial expressions and voice. The inputs are location information from the GPS sensor, movement data from the accelerometer, and video and audio data from the camera and microphone. The output is the collected location information, acceleration data, and emotion data.

[1034] Step 2:

[1035] Data transmission

[1036] The device transmits the collected location information, acceleration data, and emotion data to the server via the on-board computer. This data transmission process uses a specific communication protocol (e.g., HTTP or MQTT). The input is the location information, acceleration data, and emotion data collected in step 1. The output is these data transmitted to the server.

[1037] Step 3:

[1038] Location and acceleration data analysis

[1039] The server analyzes road surface conditions based on the location information and acceleration data received. This involves estimating road surface conditions, such as flat ground, slopes, and steps, by comparing them with past and current data in a database. The input is location information and acceleration data. The output is the estimated road surface conditions.

[1040] Step 4:

[1041] Emotional Data Analysis

[1042] The server uses an emotion engine to analyze the received emotion data, thereby identifying the user's current emotional state (e.g., nervous, relieved). The input is the emotion data sent in step 2. The output is the analyzed user's emotional state.

[1043] Step 5:

[1044] Integrated analysis

[1045] The server integrates the estimated road surface conditions and the analyzed emotional state to identify passable roads in real time. This process also takes into account past traffic data in the database. The input is the estimated road surface conditions and the analyzed emotional state of the user. The output is the analysis result of passable roads.

[1046] Step 6:

[1047] Information presentation generation

[1048] The server generates the analysis results of passable roads as visual information. The presentation method is dynamically changed to provide detailed or simple guidance depending on the user's emotional state. The input is the analysis results of passable roads obtained in step 5 and the user's emotional state. The output is visual information presentation.

[1049] Step 7:

[1050] Presentation of information

[1051] The terminal displays the visual information received from the server on the vehicle's display in real time, allowing the user to check the proposed route for safe and efficient travel. The input is the visual information sent from the server. The output is the information displayed on the terminal's display.

[1052] The above processing steps enable the user to travel safely and comfortably.

[1053] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1054] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1055] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1056] [Fourth embodiment]

[1057] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1058] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1059] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1060] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1061] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1062] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1063] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1064] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1065] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1066] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1067] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1068] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1069] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1070] The present invention is a system that uses electronic devices, a server, and a network for communication through them, and in particular, a system that collects and analyzes location information and acceleration data to provide passable routes in real time in the event of an emergency.

[1071] Data collection

[1072] First, when a user starts walking with an electronic device, the user launches the "Footprint Navigation" app. The device uses its GPS and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is sent to the server at regular intervals (e.g., every second).

[1073] Data analysis

[1074] The server receives the location information and acceleration data sent from the device. The server uses this data to estimate road surface conditions (e.g., flat ground, slopes, steps, etc.). The estimated road surface conditions and location information are stored in a database, and passable routes are analyzed based on this information. The server uses generative AI to analyze the latest data and identify routes that pedestrians can safely travel.

[1075] Information presentation

[1076] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the routes that other users have previously traveled. This footprint information is displayed in real time on the screen of the electronic device. This allows users to continue traveling safely while checking which routes are "passable now."

[1077] Specific examples

[1078] Example 1: Use at an event

[1079] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. User A launches "Footprint Navigation" and, as he walks from the event venue to the station, his device collects GPS and acceleration data. These data are sent to the server. The server also receives data from other users (e.g., User B and User C) who have just passed by, analyzes this data using the generation AI, and identifies a safe route. The device displays the results of this analysis on a map, allowing User A to safely reach the station.

[1080] Example 2: Evacuation during a disaster

[1081] After an earthquake occurs, User D launches the "Footprint Navigation" app and begins evacuation. The device collects GPS and acceleration data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the generation AI analyzes evacuation routes. The analysis results are integrated with data from other evacuees (Users E and F). The device displays this on a map, and User D can safely evacuate by following the visualized "footprints."

[1082] As described above, the present invention is a system for providing pedestrian-passable routes in real time during emergencies or traffic restrictions, and can support users in moving around safely.

[1083] The processing flow will be explained below.

[1084] Step 1:

[1085] The user launches the "Footprint Navigation" app. The app initializes the GPS sensor and acceleration sensor, preparing to collect location and acceleration data.

[1086] Step 2:

[1087] The device begins collecting location information (latitude, longitude, altitude) and acceleration data. This data is collected at regular intervals (e.g., every second).

[1088] Step 3:

[1089] The device stores collected location and acceleration data in a temporary buffer, which is configured to hold the most recent data.

[1090] Step 4:

[1091] The terminal transmits the data held in the buffer to the server via the network at regular intervals.

[1092] Step 5:

[1093] The server stores the received location and acceleration data in a database in real time, and the stored data is tagged with the time.

[1094] Step 6:

[1095] The server analyzes the acceleration data and estimates the road surface conditions (flat ground, slopes, steps, stairs, etc.) using accumulated past data and machine learning models.

[1096] Step 7:

[1097] The server integrates estimated road surface conditions and location information to analyze passable routes. Data collected from multiple users is used in the analysis, and the generating AI identifies safe routes.

[1098] Step 8:

[1099] The server generates the analysis results as visual information (footprints or paths on a map), which shows the latest safe routes.

[1100] Step 9:

[1101] The server transmits the generated visual information to the terminal, and the transmission is set to be performed in real time.

[1102] Step 10:

[1103] The device displays the received visual information on a map, allowing the user to check which routes are currently passable.

[1104] Step 11:

[1105] The user moves safely by following the displayed "footprints." Each time the user moves, the device again collects data and sends it to the server to obtain the latest information.

[1106] These steps enable the system to provide safe and passable routes in real time during emergencies and traffic restrictions.

[1107] Example 1

[1108] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1109] In recent years, there has been a demand for systems that provide pedestrians with real-time guidance on appropriate routes to ensure safe travel during large-scale urban events and natural disasters. However, existing systems often do not fully utilize the latest sensor data, making it difficult to quickly and accurately provide information on road surface conditions and passable routes. Furthermore, they are unable to effectively utilize other users' travel data, making it difficult to provide realistic travel information. There is an urgent need to resolve these issues.

[1110] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1111] In this invention, the server includes a means for estimating road surface conditions based on received location information and acceleration data, a means for integrating the estimated road surface conditions and location information to analyze "passable roads" and store the results in a database, and a means for generating the analysis results as visual information using a generative AI model, thereby enabling the provision of accurate passable routes based on the latest data.

[1112] A "mobile terminal" is an electronic device carried by a user, and is a device equipped with a GPS function and an acceleration sensor.

[1113] "Location information" refers to geographical data such as latitude, longitude, and altitude collected by mobile devices.

[1114] "Acceleration data" refers to data relating to the speed and direction of movement collected by a mobile terminal.

[1115] A "server" is a remote computer system capable of receiving, analyzing, storing, and transmitting data sent from multiple mobile terminals.

[1116] "Road surface conditions" refers to information indicating the difficulty and characteristics of travel (e.g., flat ground, slopes, steps, stairs, etc.) that the server estimates based on location information and acceleration data.

[1117] "Generative AI models" refer to algorithms and software that use artificial intelligence technology to analyze data and generate optimal routes and visual information.

[1118] A "passable road" refers to a route that the server has analyzed and determined to be safe for the user to travel.

[1119] "User" refers to a person who uses a mobile device to check passable routes in emergencies or special situations through the "Footprint Navigation" app and attempts to travel safely.

[1120] A "database" is an information management system that allows a server to store location information, acceleration data, analysis results, etc.

[1121] "Visual information" is information generated by the server that displays passable roads, road surface conditions, etc. as maps or graphics.

[1122] "Real-time" refers to the state in which data collection, analysis, and display occur almost simultaneously, meaning that users can use their current location information without delay.

[1123] The present invention is a system that utilizes mobile terminals, a server, and a network for communication therethrough, and has been developed to provide passable roads in real time, particularly in emergencies and events. Specific embodiments of the program for this system are described below.

[1124] Data collection

[1125] A user holds a mobile device such as a smartphone and launches the "Footprint Navigation" application. The device uses a GPS module and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. The collected data is sent to a server at regular intervals (for example, every second). This allows the user's current location and movement status to be tracked in real time.

[1126] Hardware used:

[1127] Smartphone GPS module

[1128] Smartphone accelerometer

[1129] Software used:

[1130] Footprint navigation application

[1131] Data analysis

[1132] The server receives location information and acceleration data sent from the device. The received data is analyzed using a generative AI model to estimate road surface conditions (e.g., flat ground, slopes, steps, etc.). In addition, the estimated road surface conditions are integrated with the location information, and safe route information is stored in a database.

[1133] Hardware used:

[1134] Server with high-performance data processing capabilities

[1135] Software used:

[1136] Database management system (e.g. MySQL)

[1137] Generative AI models (e.g., TensorFlow)

[1138] Information presentation

[1139] The server uses the generative AI model to generate the analysis results as visual information. This information is displayed as "passable routes" on a map that includes the user's current location. The displayed information is updated in real time, allowing users to travel safely while checking the latest passable routes.

[1140] Hardware used:

[1141] Smartphone displays

[1142] Software used:

[1143] User Interface (UI) for Footprint Navigation Application

[1144] Specific examples

[1145] Example 1: Use at an event

[1146] When a large-scale event is held in a particular city and some roads are closed to traffic, a user can launch "Footprint Navigation" and walk from the event venue to the station. The device collects GPS and acceleration data and sends it to a server. The server then integrates the data of other users and analyzes it with a generative AI model to identify a safe route. The analysis results are sent to the user's device and displayed on a map, allowing the user to reach the station safely.

[1147] Example 2: Evacuation during a disaster

[1148] When a user activates "Footprint Navigation" after an earthquake and begins evacuation, the device collects GPS and acceleration data in real time and sends it to the server. The server then analyzes this data using a generative AI model to predict road surface conditions. The analysis results also incorporate data from other evacuees, and the user's device displays this on a map. The user can then safely evacuate by following the visualized "footprints."

[1149] Prompt Sentence Examples

[1150] Below are some examples of prompt sentences:

[1151] Prompts for generating scenarios for people

[1152] "You are participating in a large-scale event in a certain city. After the event has ended, traffic restrictions have been put in place on the roads from the venue to the station, so you need to find the safest route. You will use the Footprint Navigation app to navigate by referring to the routes that other users have just taken. Please describe, step by step, how you will proceed."

[1153] Prompts for generating evacuation scenarios in the event of a disaster

[1154] "You're in a big city and experiencing an earthquake, and you need to evacuate immediately. Use the Footprint Navigation app for step-by-step instructions on how to find the safest route based on up-to-date data from other evacuees."

[1155] By introducing detailed processes to support users' safe movement in this way, this system becomes an effective means of providing pedestrian-safe routes in real time during emergencies or traffic restrictions.

[1156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1157] Step 1:

[1158] The user launches the app. The user launches the "Footprint Navigation" app using their smartphone. This prepares the app to start collecting location and acceleration data.

[1159] Specific behavior:

[1160] The user taps the "Footprint Navigation" icon on the smartphone's home screen to launch the app.

[1161] Input: Touch operation on smartphone

[1162] Output: App launch

[1163] Step 2:

[1164] The device collects location and acceleration data. When the app is launched, the device activates the GPS module and acceleration sensor to collect latitude, longitude, altitude, and acceleration data every second.

[1165] Specific behavior:

[1166] The device obtains latitude, longitude, and altitude data from the GPS every second, and current acceleration data from the acceleration sensor.

[1167] Input: Data from the GPS module and accelerometer

[1168] Output: A set of position and acceleration data updated every second

[1169] Step 3:

[1170] The device sends the collected data to the server. At regular intervals (e.g., every second), the collected location information and acceleration data are packetized and sent to the server.

[1171] Specific behavior:

[1172] The device packages the data into packets and sends them over the internet to a server.

[1173] Input: A set of location and acceleration data

[1174] Output: Data packets sent to the server

[1175] Step 4:

[1176] The server receives and analyzes the data. Based on the received location information and acceleration data, the server uses a generative AI model to estimate road surface conditions. The estimated road surface conditions and location information are integrated to generate passable route information and store it in a database.

[1177] Specific behavior:

[1178] The server stores the received data in a database and analyzes it using a generative AI model, which combines GPS information and acceleration data to predict road surface conditions (flat ground, slopes, steps, stairs, etc.).

[1179] Input: User location and acceleration data

[1180] Output: Estimated road surface conditions and passable route information

[1181] Step 5:

[1182] The server generates passable route information. The server integrates the latest data with existing data to identify safe routes. This information is generated in a visual format and provided to the user.

[1183] Specific behavior:

[1184] The server uses the generative AI model to generate the analysis results as visual information and draw them as map layers.

[1185] Input: Estimated road surface conditions and location information

[1186] Output: Visually displayable route information

[1187] Step 6:

[1188] The device receives and displays the route information sent from the server. The user's mobile device displays the analysis results on a map and updates them in real time. This allows the user to check the latest passable routes and travel safely.

[1189] Specific behavior:

[1190] The device receives the route information sent from the server and displays it on the map screen within the app. Users can travel safely while checking their route on the map in real time.

[1191] Input: Route information sent from the server

[1192] Output: Real-time updated map display and route information

[1193] (Application example 1)

[1194] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1195] The present invention relates to a system that provides passable routes in real time in emergencies, and aims to improve safety in emergencies by identifying and providing routes that autonomous vehicles can safely travel. Conventional systems have had difficulty in understanding and analyzing situations in real time, so there has been a demand for rapid and accurate responses in emergencies.

[1196] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1197] In this invention, the server includes a means for integrating the estimated road surface conditions and location information to analyze "passable roads" and store the results in a database, a means for generating visual information about the analysis results, and a means for an electronic device mounted on the autonomous vehicle to present a safe driving route based on the analysis results. This makes it possible to provide routes that autonomous vehicles can travel safely and efficiently even in emergencies.

[1198] An "electronic device" is a device that has the ability to collect location and acceleration data and transmit it to a server.

[1199] "Location information" refers to latitude, longitude, and altitude data obtained by a GPS device.

[1200] "Acceleration data" refers to data relating to acceleration acquired by a sensor built into an electronic device.

[1201] The "server" is a computer system that receives location information and acceleration data, estimates road surface conditions, and analyzes safe routes.

[1202] "Road surface condition" refers to the physical condition of the road estimated by the server based on the received position information and acceleration data.

[1203] A "passable road" is a road route that the server has analyzed and determined to be safe to travel.

[1204] The "database" is a recording medium for integrating and storing estimated road surface conditions and position information.

[1205] "Visual information" refers to visual representation data based on the analysis results that is generated by the server for presentation to the user.

[1206] An "autonomous vehicle" is a vehicle that can drive autonomously and present safe driving routes based on data from a server.

[1207] A "safe driving route" is the optimal route for an autonomous vehicle to travel based on information about passable roads.

[1208] This invention relates to a system that utilizes electronic devices, a server, and a network for communication therethrough. This system provides safe routes for autonomous vehicles in the event of an emergency in real time.

[1209] First, a user gets into an autonomous vehicle and starts up their electronic device. The electronic device is equipped with a GPS function and an acceleration sensor, which collects location information (latitude, longitude, altitude) and acceleration data. This data is sent to a server at regular intervals (for example, every second). The electronic device used can be a typical smartphone, tablet, or the vehicle's infotainment system.

[1210] The server receives the location information and acceleration data sent from the device. The server requires high-performance computing capabilities, so it is recommended to use dedicated server equipment or cloud computing services (e.g., AWS or Google Cloud). The server uses the received data to estimate the road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It is effective to use a generative AI model for this estimation.

[1211] The server combines the estimated road surface conditions and location information to analyze passable routes. The results of this analysis are stored in a database, which can be a standard SQL database or a NoSQL database (e.g., MySQL or MongoDB). The server then re-analyzes safe driving routes based on the latest data, enabling rapid response in emergencies.

[1212] The server then generates visual information based on the analysis results and sends it to the autonomous vehicle's onboard electronics, such as the infotainment system or a smartphone, allowing the user to visually check the safe driving route in real time. Map display services such as Google Maps API can be used to generate the visual information.

[1213] Examples of specific prompts include:

[1214] text

[1215] Analyze a safe route for an autonomous vehicle based on the following data:

[1216] Current location: Latitude 35.6895, Longitude 139.6917, Altitude 15m

[1217] Acceleration data: x=0.02, y=0.03, z=9.8

[1218] Other sensor data (e.g. road conditions, obstacles, weather, etc.)

[1219] Please return the analysis results as a safe route.

[1220] Specific use cases include evacuation during disasters and traffic restrictions during large-scale events. For example, after an earthquake, the system can identify safe evacuation routes for vehicles and display them on the infotainment system, effectively supporting evacuation.

[1221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1222] Step 1:

[1223] The user activates the electronic device installed in the vehicle and starts an application. The input is the activation of the application by the user's operation. The electronic device uses the GPS function and acceleration sensor to collect the vehicle's location information (latitude, longitude, altitude) and acceleration data. The output is the collected location information and acceleration data.

[1224] Step 2:

[1225] The electronic device sends the location information and acceleration data it collects to a server. The input is the collected location information and acceleration data, and the output is the data sent to the server. The data is collected and sent at regular intervals (e.g., every second).

[1226] Step 3:

[1227] The server estimates road surface conditions using the location information and acceleration data it receives. The input is the received location information and acceleration data, and the output is the estimated road surface conditions. A generative AI model on the server analyzes the data and estimates the type of road surface (flat ground, slope, steps, stairs, etc.).

[1228] Step 4:

[1229] The server integrates the estimated road surface conditions and location information, analyzes "passable roads," and stores the results in a database. The input is the estimated road surface conditions and location information, and the output is the analyzed "passable road" data. Analysis technology based on a generative AI model is used.

[1230] Step 5:

[1231] The server generates visual information based on the analysis results. The input is the analyzed "passable roads" and the latest road surface conditions and location information, and the output is information that can be displayed visually. To generate the visual information, map display services such as Google Maps API are used.

[1232] Step 6:

[1233] The server generates visual information and sends it to the electronics of the autonomous vehicle. The input is the visual information, and the output is the data sent to the electronics. This provides a safe driving route that is updated in real time.

[1234] Step 7:

[1235] The visual information received by the electronic devices of an autonomous vehicle is displayed on the infotainment system display. The input is visual information sent from the server, and the output is a safe driving route displayed on the display. The user can travel safely based on this information.

[1236] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1237] The present invention is a system that uses electronic devices, servers, and a network for communication therethrough, and collects and analyzes location information and acceleration data to provide passable routes in real time, especially in emergencies.Furthermore, the present invention is a system that dynamically changes the way information is presented based on the user's emotional state by combining an emotion engine that recognizes the user's emotions.

[1238] Data collection

[1239] First, when a user starts walking with an electronic device, the user launches the "Footprint Navigation" app. The device uses its GPS and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is sent to the server at regular intervals (e.g., every second).

[1240] Emotional Data Collection

[1241] At the same time, the device uses sensors such as a camera and microphone to recognize the user's emotions. The device uses the data collected from these sensors to obtain emotional data from the user's facial expressions and voice. The emotional data is also sent to the server along with location information and acceleration data.

[1242] Data analysis

[1243] The server receives location information and acceleration data transmitted from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It then analyzes the received emotion data using an emotion engine to estimate the user's emotional state (e.g., tension, relief, fear, etc.). In this way, the server integrates the estimated road surface conditions and emotional state to analyze passable routes.

[1244] Information presentation

[1245] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the paths previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of presenting information is optimized according to the user's emotional state. For example, more detailed directions are provided to a nervous user, and simple directions are provided to a relaxed user.

[1246] Specific examples

[1247] Example 1: Use at an event

[1248] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. When User A launches "Footprint Navigation" and walks from the event venue to the station, the device collects GPS data, acceleration data, and emotional data and sends it to the server. The server analyzes this data and uses a generative AI to identify a safe route. The analysis results are displayed on a map as visual information that corresponds to User A's state of mind. For example, if User A is feeling nervous, more detailed footprint information will be provided.

[1249] Example 2: Evacuation during a disaster

[1250] After an earthquake occurs, User D activates the "Footprint Navigation" app and begins evacuation. The device collects GPS data, acceleration data, and emotion data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the emotion engine analyzes User D's emotional state. The analysis results are integrated with data from other evacuees (User E and User F), and the generation AI identifies a safe evacuation route. The device displays this on a map, and User D can evacuate safely by following the visualized "footprints." If the emotional state is tense, an escort alarm and detailed instructions are provided.

[1251] As described above, the present invention can support the user's safe travels by providing safe and passable routes in real time during emergencies or traffic restrictions, and by optimizing the information presentation method based on the user's emotional state.

[1252] The processing flow will be explained below.

[1253] Step 1:

[1254] The user launches the "Footprint Navigation" app. The app initializes the GPS sensor, acceleration sensor, and emotion recognition sensors such as the camera and microphone, and prepares to collect location information, acceleration data, and emotion data.

[1255] Step 2:

[1256] The device begins collecting location information (latitude, longitude, altitude) and acceleration data. This data is collected at regular intervals (e.g., every second).

[1257] Step 3:

[1258] The device uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the emotional data, which is then stored in a buffer in real time.

[1259] Step 4:

[1260] The location information, acceleration data, and emotion data collected by the terminal are transmitted to a server via a network at regular intervals.

[1261] Step 5:

[1262] The server receives location information and acceleration data and stores it in a database in real time. The location information also includes time information.

[1263] Step 6:

[1264] The server analyzes the acceleration data and estimates the road surface conditions (e.g., flat ground, slopes, steps, etc.) using accumulated past data and machine learning models.

[1265] Step 7:

[1266] The server uses an emotion engine to analyze the received emotion data and estimate the user's emotional state (e.g., tension, relief, fear, etc.).

[1267] Step 8:

[1268] The server integrates the estimated road surface conditions, location information, and emotional state to analyze passable paths, which are then identified as safe routes by the generation AI.

[1269] Step 9:

[1270] The server generates the analysis results as visual information (footprints or routes on a map), showing the latest safe routes and responding to the user's emotional state.

[1271] Step 10:

[1272] The server generates visual information and sends it to the terminal, with the transmission set to occur in real time.

[1273] Step 11:

[1274] The device displays the received visual information on a map, allowing the user to check which routes are currently passable.

[1275] Step 12:

[1276] The user follows the displayed "footprints" to move safely. During the movement, the device continues to collect data and transmits it to the server for the latest information. If the user's emotional state changes, the display method will also dynamically change accordingly.

[1277] These steps enable the system to provide safe and passable routes in real time during emergencies or traffic restrictions, while optimizing the way information is presented based on the user's emotional state.

[1278] Example 2

[1279] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1280] Conventional systems have had difficulty providing safe and passable routes in real time during emergencies or traffic restrictions. Furthermore, they lacked the ability to dynamically change the way information is presented depending on the user's emotional state, making it impossible to provide optimal route guidance suited to the situation. As a result, there was a problem in that the system did not adequately support users' safe travels.

[1281] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for estimating road surface conditions based on received position information and acceleration data, a means for integrating the estimated road surface conditions and position information to analyze "passable roads" and store the results in a database, a means for generating visual information based on the analysis results, a means for analyzing emotion data to estimate the user's emotional state, and a means for dynamically changing the information presentation method based on the estimated emotional state. This makes it possible to provide safe and passable roads in real time during emergencies or traffic restrictions, and to present optimal information according to the user's emotional state.

[1282] "Location information" is data indicating a geographical location, including latitude, longitude, and altitude.

[1283] "Acceleration data" is data that indicates changes in the speed of an object's movement, measured by an acceleration sensor in an electronic device.

[1284] A "server" is a centralized device that receives data from clients via a network, analyzes it, and stores it.

[1285] "Road surface condition" is data indicating the condition of the ground surface, such as flat ground, slopes, steps, and stairs.

[1286] "Passable roads" is data indicating routes that are estimated to be safe for the user to travel.

[1287] A "database" is a data storage system for systematically managing and storing information.

[1288] "Emotion data" is data that indicates the emotional state of the user analyzed from facial expressions, voice, and the like.

[1289] An "emotion engine" is a software or hardware system that analyzes emotion data to infer a user's emotional state.

[1290] "Visual information" refers to information such as maps and route guidance that is visually provided to the user.

[1291] "Electronic devices" refer to devices such as smartphones and tablets that users carry with them to process information.

[1292] "Real-time" is a term that refers to instant processing of data or information that reflects its current state.

[1293] A "sensor" is a device that detects environmental or operational conditions and transmits that data to electronic devices.

[1294] This invention relates to a system that utilizes electronic devices, a server, and a network for communication via them. The system has the function of collecting and analyzing location information and acceleration data to provide passable routes in real time, particularly in emergencies. It also has the function of dynamically changing the way information is presented based on the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[1295] Data collection

[1296] First, the user installs and launches the "Footprint Navigation" application on an electronic device such as a smartphone or tablet. When the user launches the application, the device uses its GPS function and acceleration sensor to collect location information (latitude, longitude, altitude) and acceleration data. This data is then sent to a server at regular intervals (e.g., every second).

[1297] Emotional Data Collection

[1298] At the same time, the device uses sensors such as a camera and microphone to recognize the user's emotions. The device uses the data collected from these sensors to obtain emotional data from the user's facial expressions and voice. The emotional data is also sent to the server along with location information and acceleration data.

[1299] Data analysis

[1300] The server receives location information and acceleration data transmitted from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate road surface conditions (e.g., flat ground, slopes, steps, stairs, etc.). It then analyzes the received emotion data using an emotion engine to estimate the user's emotional state (e.g., tension, relief, fear, etc.). In this way, the server integrates the estimated road surface conditions and emotional state to analyze passable routes.

[1301] Information presentation

[1302] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the paths previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of presenting information is optimized according to the user's emotional state. For example, more detailed directions are provided to a nervous user, and simple directions are provided to a relaxed user.

[1303] Specific examples

[1304] Example 1: Use at an event

[1305] Suppose a large-scale event is being held in a particular city. Traffic on some roads is restricted due to the event. When User A launches "Footprint Navigation" and walks from the event venue to the station, the device collects GPS data, acceleration data, and emotional data and sends it to the server. The server analyzes this data and uses a generative AI to identify a safe route. The analysis results are displayed on a map as visual information that corresponds to User A's state of mind. For example, if User A is feeling nervous, more detailed footprint information will be provided.

[1306] Example 2: Evacuation during a disaster

[1307] After an earthquake occurs, User D activates the "Footprint Navigation" app and begins evacuation. The device collects GPS data, acceleration data, and emotion data in real time and sends it to the server. The server estimates road surface conditions based on this data, and the emotion engine analyzes User D's emotional state. The analysis results are integrated with data from other evacuees (User E and User F), and the generation AI identifies a safe evacuation route. The device displays this on a map, and User D can evacuate safely by following the visualized "footprints." If the emotional state is tense, an escort alarm and detailed instructions are provided.

[1308] Prompt Sentence Examples

[1309] "We have launched a project to identify safe routes available in emergencies using a footprint navigation app. Please explain the detailed steps, the software and hardware used, and the specific data processing methods."

[1310] The above is an embodiment of the present invention.

[1311] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1312] Step 1:

[1313] The user installs the "Footprint Navigation" app on their electronic device and launches it. When the app launches, the main screen appears and the user is prompted to grant access to location services, the camera, and the microphone. As input, the app is launched by the user's operation. As output, the app's main screen appears and the use of sensors is permitted.

[1314] Step 2:

[1315] The user starts walking. The device begins to obtain the user's location information (latitude, longitude, and altitude) using its GPS function. At the same time, it uses the acceleration sensor to obtain acceleration data related to the user's movements. The camera and microphone are used to collect emotion data from the user's facial expressions and voice. Data is collected from the GPS sensor, acceleration sensor, camera, and microphone as input. Location information, acceleration data, and emotion data are obtained as output.

[1316] Step 3:

[1317] The device transmits the collected location information, acceleration data, and emotion data to the server at regular intervals (e.g., every second). Data transmission is performed using a secure communication protocol (e.g., HTTPS). The collected data exists as input. These data are transmitted to the server as output.

[1318] Step 4:

[1319] The server receives the location information and acceleration data sent from the device in real time and stores them in a database. The server analyzes the acceleration data to estimate the road surface condition (flat ground, slope, step, stairs, etc.). The received data exists as input. The analysis results and road surface condition are stored in the database as output.

[1320] Step 5:

[1321] The server analyzes the received emotion data using an emotion engine to estimate the user's emotional state (tension, relief, fear, etc.). The estimated road surface condition and emotional state are integrated to analyze a passable road. The analyzed road surface condition and emotional state are input. The analysis result of a passable road is obtained as output.

[1322] Step 6:

[1323] The server generates the analysis results as visual information. For example, it creates "footprint information" based on the routes previously traveled by other users. This footprint information is displayed in real time on the screen of the electronic device. Furthermore, the method of information presentation is optimized according to the user's emotional state. The input is the analysis result of passable routes. The output is visual information, and the method of information presentation is optimized based on the user's emotional state.

[1324] Step 7:

[1325] The user's electronic device receives the visual information previously generated by the server and displays it on its screen. The user uses this information to find a safe route and receives real-time updated guidance. As input, there is the visual information sent from the server. As output, there is the visual information displayed on the user's electronic device.

[1326] The above are the specific processing steps of the system according to the present invention.

[1327] (Application example 2)

[1328] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1329] Currently, autonomous vehicles are required to improve safety and efficiency, but there is a lack of systems that provide safe and passable routes in real time, especially in emergencies. Furthermore, technology that recognizes the emotional state of users while driving and provides appropriate information accordingly is immature. As a result, current systems do not adequately provide flexible and safe travel support based on the user's emotional state. Furthermore, existing technologies are limited in their ability to collect and analyze large amounts of data in real time, making it difficult to provide safe routes, especially during large-scale events and disasters.

[1330] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1331] In this invention, the server includes means for receiving position information and acceleration data and estimating road surface conditions, means for analyzing "passable roads" in real time based on the position information, acceleration data, and emotion data, means for generating visual information based on the analysis results, and means for analyzing the user's emotional state based on the emotion data and dynamically changing the information presentation method. This allows the server to provide the optimal route in real time according to the user's emotional state, both in emergencies and under normal circumstances, enabling safe and efficient travel.

[1332] An "electronic device" is a device that can be carried by a user and has the capability to collect location information and acceleration data.

[1333] "Location information" refers to latitude, longitude, and altitude data used by electronic devices to indicate their current location.

[1334] "Acceleration data" is data used to measure changes in the movement and speed of electronic devices.

[1335] A "server" is a computer system that has the function of receiving and analyzing location information, acceleration data, and emotion data transmitted from electronic devices.

[1336] "Road surface condition" refers to the condition of the ground for travel that the server estimates based on location information and acceleration data.

[1337] A "passable road" is a route that the server has analyzed and determined to be safe to travel.

[1338] "Emotional data" is data collected to recognize a user's emotional state.

[1339] An "emotion engine" is software or a system for analyzing emotion data and inferring a user's emotional state.

[1340] "Information presentation method" refers to the means or format by which the server presents analysis results and information to the user.

[1341] "Visual information" refers to an information display format that allows the user to visually understand the analysis results and passable roads.

[1342] "Real-time" means that data is collected, analyzed, and information is presented without delay.

[1343] This invention relates to a system for supporting safe travel for users, and in particular to a program for providing real-time passable routes for autonomous vehicles. The system includes a server that collects and analyzes user location information, acceleration data, and emotion data.

[1344] Data collection

[1345] When a user activates an electronic device (for example, the console of an autonomous vehicle), sensors within the vehicle are activated. Specifically, the following sensors are installed:

[1346] GPS sensor: Collects vehicle location information (latitude, longitude, altitude).

[1347] Acceleration sensor: Acquires vehicle acceleration data to understand road surface conditions.

[1348] Cameras and microphones: Capture video and audio from inside and outside the vehicle and collect user emotional data.

[1349] Data transmission

[1350] The collected location, acceleration, and emotion data is transmitted to a server by the on-board computer in the autonomous vehicle. The transmitted data includes:

[1351] Location information: latitude, longitude, altitude

[1352] Acceleration data: Vehicle speed changes and vibration state

[1353] Emotion data: Emotional state obtained from the user's facial expressions and voice

[1354] Data analysis

[1355] The server analyzes the received data using the following steps:

[1356] 1. Analysis of location information and acceleration data: Based on location information and acceleration data, road surface conditions (flat ground, slopes, steps, etc.) are estimated.

[1357] 2. Emotion data analysis: Using the emotion engine, the user's emotional state (e.g., tension, relief) is analyzed from the collected emotion data.

[1358] 3. Integrated analysis: Integrate the estimated road surface conditions and the analyzed emotional state to identify passable paths.

[1359] Information presentation

[1360] The server generates the analysis results as visual information. This information is displayed in real time on the vehicle's display. The way the information is presented changes dynamically depending on the user's emotional state. Specifically, the following display formats are possible:

[1361] 1. Detailed guidance: If the user is nervous, detailed routes and precautions will be displayed.

[1362] 2. Simple guidance: If the user is comfortable, simple route information is displayed.

[1363] Specific examples

[1364] For example, when a large-scale event is being held in a city, certain roads may be unavailable. The vehicle's "Safety Navigation AI" uses GPS sensors, acceleration sensors, cameras, and microphones to collect and transmit data in real time. The server analyzes the received data, identifies the optimal route, and displays detailed or simple directions according to the user's emotional state.

[1365] Prompt statement

[1366] Data collection prompt statements

[1367] gps_data = get_gps_data()

[1368] acceleration_data = get_acceleration_data()

[1369] emotion_data = get_emotion_data()

[1370] Data transmission and route acquisition prompts

[1371] response = send_all_data(gps_data, acceleration_data, emotion_data)

[1372] route_data = get_route(response)

[1373] Root display prompt statement

[1374] display_route(route_data)

[1375] As described above, analyzing the user's location information and emotional data and providing appropriate route guidance can improve the safety and efficiency of self-driving vehicles.

[1376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1377] Step 1:

[1378] Data collection

[1379] When a user starts up an electronic device, the device collects data using a GPS sensor, accelerometer, camera, and microphone. This data includes the current location (latitude, longitude, and altitude), the vehicle's acceleration, and emotion data from the user's facial expressions and voice. The inputs are location information from the GPS sensor, movement data from the accelerometer, and video and audio data from the camera and microphone. The output is the collected location information, acceleration data, and emotion data.

[1380] Step 2:

[1381] Data transmission

[1382] The device transmits the collected location information, acceleration data, and emotion data to the server via the on-board computer. This data transmission process uses a specific communication protocol (e.g., HTTP or MQTT). The input is the location information, acceleration data, and emotion data collected in step 1. The output is these data transmitted to the server.

[1383] Step 3:

[1384] Location and acceleration data analysis

[1385] The server analyzes road surface conditions based on the location information and acceleration data received. This involves estimating road surface conditions, such as flat ground, slopes, and steps, by comparing them with past and current data in a database. The input is location information and acceleration data. The output is the estimated road surface conditions.

[1386] Step 4:

[1387] Emotional Data Analysis

[1388] The server uses an emotion engine to analyze the received emotion data, thereby identifying the user's current emotional state (e.g., nervous, relieved). The input is the emotion data sent in step 2. The output is the analyzed user's emotional state.

[1389] Step 5:

[1390] Integrated analysis

[1391] The server integrates the estimated road surface conditions and the analyzed emotional state to identify passable roads in real time. This process also takes into account past traffic data in the database. The input is the estimated road surface conditions and the analyzed emotional state of the user. The output is the analysis result of passable roads.

[1392] Step 6:

[1393] Information presentation generation

[1394] The server generates the analysis results of passable roads as visual information. The presentation method is dynamically changed to provide detailed or simple guidance depending on the user's emotional state. The input is the analysis results of passable roads obtained in step 5 and the user's emotional state. The output is visual information presentation.

[1395] Step 7:

[1396] Presentation of information

[1397] The terminal displays the visual information received from the server on the vehicle's display in real time, allowing the user to check the proposed route for safe and efficient travel. The input is the visual information sent from the server. The output is the information displayed on the terminal's display.

[1398] The above processing steps enable the user to travel safely and comfortably.

[1399] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1400] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1401] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1402] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1403] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1404] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1405] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1406] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1407] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1408] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1409] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1410] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1411] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1412] 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.

[1413] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1414] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1415] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1416] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1417] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1418] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1419] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1420] The following is further disclosed regarding the above embodiment.

[1421] (Claim 1)

[1422] A means for collecting location information of an electronic device;

[1423] means for acquiring acceleration data;

[1424] means for transmitting the position information and acceleration data to a server;

[1425] a means for estimating road surface conditions based on the received position information and acceleration data;

[1426] a means for the server to analyze "passable roads" by integrating the estimated road surface conditions and location information and store the results in a database;

[1427] means for generating analysis results as visual information in the server;

[1428] The electronic device includes means for visually presenting the analysis results to a user.

[1429] (Claim 2)

[1430] 10. The system of claim 1, wherein the generated visual information provides "roadable road" information based on roads previously traveled by other users.

[1431] (Claim 3)

[1432] 10. The system of claim 1, wherein the information about traversable roads is updated in real time.

[1433] "Example 1"

[1434] (Claim 1)

[1435] means for launching an application installed on a mobile terminal held by a user;

[1436] a means for collecting location information in the mobile terminal;

[1437] means for acquiring acceleration data in the mobile terminal;

[1438] means for transmitting the position information and acceleration data to a server;

[1439] a means for estimating road surface conditions based on the received position information and acceleration data;

[1440] a means for the server to analyze "passable roads" by integrating the estimated road surface conditions and location information and store the results in a database;

[1441] A means for generating analysis results as visual information using a generative AI model by the server;

[1442] and means for the mobile terminal to present analysis results to a user in real time.

[1443] (Claim 2)

[1444] 10. The system of claim 1, wherein the generated visual information provides "roadable road" information based on roads previously traveled by other users.

[1445] (Claim 3)

[1446] 10. The system of claim 1, wherein the information about traversable roads is updated in real time using a generative AI model.

[1447] "Application Example 1"

[1448] (Claim 1)

[1449] A means for collecting location information of an electronic device;

[1450] means for acquiring acceleration data;

[1451] means for transmitting the position information and acceleration data to a server;

[1452] a means for estimating road surface conditions based on the received position information and acceleration data;

[1453] a means for the server to analyze "passable roads" by integrating the estimated road surface conditions and location information and store the results in a database;

[1454] means for generating analysis results as visual information in the server;

[1455] and a means for an electronic device installed in the autonomous vehicle to present a safe driving route based on the analysis results.

[1456] (Claim 2)

[1457] 10. The system of claim 1, wherein the generated visual information provides "passable road" information based on roads previously traveled by other users.

[1458] (Claim 3)

[1459] 10. The system of claim 1, wherein the information on traversable roads is updated in real time and a safe driving route is displayed on a display within the autonomous vehicle.

[1460] "Example 2: Combining Emotion Engines"

[1461] (Claim 1)

[1462] A means for collecting location information of an electronic device;

[1463] means for acquiring acceleration data;

[1464] means for transmitting the position information and acceleration data to a server;

[1465] a means for estimating road surface conditions based on the received position information and acceleration data;

[1466] a means for the server to analyze "passable roads" by integrating the estimated road surface conditions and location information and store the results in a database;

[1467] means for generating analysis results as visual information in the server;

[1468] A means for the electronic device to collect user emotion data using sensors such as a camera and a microphone;

[1469] means for transmitting the emotion data to a server;

[1470] a means for analyzing emotion data and estimating an emotional state of a user,

[1471] a means for dynamically changing an information presentation method based on the estimated emotional state, in the server;

[1472] The electronic device includes means for visually presenting the analysis results to a user.

[1473] (Claim 2)

[1474] 10. The system of claim 1, wherein the generated visual information provides "roadable road" information based on roads previously traveled by other users.

[1475] (Claim 3)

[1476] 10. The system of claim 1, wherein the information about traversable roads is updated in real time.

[1477] "Application example 2 when combining emotion engines"

[1478] (Claim 1)

[1479] A means for collecting location information of an electronic device;

[1480] means for acquiring acceleration data;

[1481] means for transmitting the position information and acceleration data to a server;

[1482] a means for estimating road surface conditions based on the received position information and acceleration data;

[1483] a means for the server to analyze "passable roads" by integrating the estimated road surface conditions and location information and store the results in a database;

[1484] means for generating analysis results as visual information in the server;

[1485] a means for visually presenting the analysis results to a user in the electronic device;

[1486] a means for recognizing and collecting emotion data;

[1487] means for transmitting the emotion data to a server;

[1488] means for analyzing the emotional state of the user based on the received emotional data;

[1489] and means for dynamically changing the information presentation method based on the analyzed emotional state.

[1490] (Claim 2)

[1491] 10. The system of claim 1, wherein the generated visual information provides "roadable road" information based on roads previously traveled by other users.

[1492] (Claim 3)

[1493] 10. The system of claim 1, wherein the information about traversable roads is updated in real time. [Explanation of symbols]

[1494] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting location information of an electronic device; means for acquiring acceleration data; means for transmitting the position information and acceleration data to a server; a means for estimating road surface conditions based on the received position information and acceleration data; a means for the server to analyze "passable roads" by integrating the estimated road surface conditions and location information and store the results in a database; means for generating analysis results as visual information in the server; The electronic device includes means for visually presenting the analysis results to a user.

2. 10. The system of claim 1, wherein the generated visual information provides "roads available" information based on roads previously traveled by other users.

3. 10. The system of claim 1, wherein the information about available roads is updated in real time.

Images