system

Abstract

A system is provided.SOLUTION: A system comprising: means for uploading a photograph or a moving image taken by a user via the Internet; means for acquiring position information of a current location using a GPS module; means for transmitting the taken photograph or moving image and the position information to a server; AI means for analyzing the received photograph or moving image and position information in the server; means for updating map information based on information analyzed by the AI means; and means for displaying the updated map information on a user device.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] It is difficult to grasp the status of roads, trails, ports, and houses in real time, making it difficult to obtain accurate information when a rapid and appropriate response is required during disasters and emergencies. Furthermore, conventional map services and information provision methods have issues with information delays and accuracy, making it difficult for users to obtain the information they need in a timely manner to make appropriate preparations and select routes. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a means for uploading photos and videos taken by a user via the Internet, a means for obtaining location information of the current location using a GPS module, a means for transmitting the taken photos and videos and the location information to a server, an AI means for analyzing the photos, videos and the location information received by the server, a means for updating map data based on the information analyzed by the AI ​​means, and a means for displaying the updated map data on a user terminal.

[0006] Specifically, when photos and videos taken by users are sent to the server, AI analyzes them to determine the condition of roads, hiking trails, ports, and houses, and map data is automatically updated based on the analysis results. This allows users to obtain the latest information in real time and quickly make appropriate preparations and select routes. Furthermore, the server can generate and send alert messages to users based on the analysis results, allowing users to take prompt action.

[0007] A "user" is an entity that uses the system to take photos and videos and upload that information.

[0008] "Photos and videos" are still images or videos taken by the user using a terminal, and are data showing the conditions of roads, hiking trails, ports, houses, etc.

[0009] The "Internet" refers to a digital communications network that can send and receive information worldwide, used for uploading and downloading.

[0010] A "GPS module" is a device for identifying location information on Earth and is used to obtain the latitude and longitude of a user's current location.

[0011] "Current location information" is data on the geographical latitude and longitude of the user's actual location, obtained using a GPS module.

[0012] A "server" is a central computer system that receives data over the Internet, analyzes it, and sends the results to user terminals.

[0013] "AI means" is an artificial intelligence technology that analyzes received photo and video data and determines the condition of roads, hiking trails, ports, and houses.

[0014] "Analyzed information" refers to assessments and conclusions drawn from photo and video data processed by AI means.

[0015] "Map data" refers to digital data showing geographical information such as roads, hiking trails, ports, and houses, which can be viewed by users on client terminals.

[0016] A "user terminal" refers to a communication device used by a user, such as a computer, smartphone, or tablet, and is a device that displays map data and notifications sent from the server.

[0017] An "alert message" is a warning or suggestion message that the server generates based on the analysis results and sends to the user.

[0018] "Passability" is a rating that indicates whether a road or trail is in a condition that allows safe passage.

[0019] "Required equipment" refers to the tools and equipment needed to traverse roads or trails in specific conditions.

[0020] A "request" is a form of data that a user terminal or server uses to request information or operations from other systems or devices.

[0021] A "data packet" is a small unit of digital information sent and received over the Internet.

[0022] "Analysis Results" means conclusions or findings derived from photos or videos processed by AI means. [Brief explanation of the drawings]

[0023] [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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] System Overview

[0045] This invention is a system that grasps the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflects this information in map data, allowing users to check the latest status and take prompt and appropriate action.

[0046] Data Collection Stage

[0047] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[0048] The device uses the GPS module to obtain the current location information (latitude and longitude) of the location where the photo or video was taken.

[0049] The device sends the captured photos and videos, as well as its current location information, to a server via the Internet.

[0050] Data reception and analysis stage

[0051] The server receives the data packets sent from the device and stores the photos, videos, and location information in a database.

[0052] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, such as fallen trees, snow, and collapsed roads.

[0053] AI analysis methods determine passability, required equipment, and congestion status. For example, if a trail is covered in snow, the results will be "Impassable" and "Required Equipment: Snow Gear."

[0054] Data linkage and display stage

[0055] The server integrates the analysis results returned by the AI ​​analysis means with the location information and updates the map data in real time.

[0056] The updated map data is color-coded and symbolized based on the condition; for example, impassable areas are displayed in red.

[0057] User Support Stage

[0058] The server generates an alert message based on the analysis results and sends it to the user. For example, if a road is collapsed, a message will be sent saying, "This road is currently impassable. Please choose an alternative route."

[0059] The device receives updates and alert messages from the server and notifies the user, who can then take appropriate action.

[0060] Specific examples

[0061] Examples of hiking trails

[0062] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[0063] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[0064] The server receives the data and analyzes the photos using AI analysis methods, which recognizes the trail as covered in snow and generates a result that it is "impassable."

[0065] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0066] The device displays the latest map data and notifies User A, "This hiking trail is currently impassable. Please bring snow gear."

[0067] Example of a disaster

[0068] User B takes a photo of the road after the earthquake and uploads it through the app.

[0069] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[0070] The server receives the data and analyzes the photos using AI analysis methods. The AI ​​recognizes that the road has collapsed and generates a result that says "impassable."

[0071] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0072] The device displays the latest map data and notifies User B, "This road is currently impassable. Please choose another route."

[0073] This allows users to obtain the latest information in real time and take prompt action, improving the accuracy of map data and user safety.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[0077] Step 2:

[0078] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[0079] Step 3:

[0080] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[0081] Step 4:

[0082] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[0083] Step 5:

[0084] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements within the image. Specifically, the AI ​​scans the image and recognizes conditions such as fallen trees, snow, collapsed roads, and flooding.

[0085] Step 6:

[0086] AI analysis methods determine passability, required equipment, and congestion status. For example, if a trail is covered in snow, the results will be "Impassable" and "Required Equipment: Snow Gear."

[0087] Step 7:

[0088] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[0089] Step 8:

[0090] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[0091] Step 9:

[0092] The device displays the received map data and notifies the user of the current situation in real time, for example by displaying warning messages and icons on the screen.

[0093] Step 10:

[0094] The server generates and sends an alert message to the user based on the analysis results. In the event of an emergency, the alert message is sent to the user as a push notification.

[0095] Step 11:

[0096] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action. For example, the message might read, "This road is currently collapsed and impassable. Please choose an alternative route."

[0097] Example 1

[0098] 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."

[0099] In modern society, it is extremely important to quickly grasp natural disasters and sudden changes in road conditions and take appropriate measures. However, conventional systems have difficulty collecting information and updating map data in real time, which means that users have to spend a great deal of time and effort obtaining the latest information. In addition, determining whether a road is passable and what equipment is needed is also manual, so a highly accurate and rapid response is required.

[0100] 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.

[0101] In this invention, the server includes means for transferring image data captured by a user via a network, means for acquiring geographic information of the current location using a position detection device, means for transmitting the captured image data and the geographic information to a data processing device, machine learning means in the data processing device for analyzing the received image data and geographic information, means for updating map data based on the information analyzed by the machine learning means, and means for displaying the updated map data on a user terminal. This makes it possible to grasp road conditions and mountain trail conditions in real time and quickly and accurately determine passability and necessary equipment.

[0102] A "user" is an entity that uses the system to capture image data and transmits related information to the server.

[0103] "Image data" refers to digital data of photographs and videos taken by the user.

[0104] "Network" refers generally to a communications network, including the Internet, used to transmit and receive data.

[0105] A "location detection device" is a hardware and software device, including a GPS module, that detects the geographic information (latitude and longitude) of a current location.

[0106] "Geographic information" refers to data on the latitude and longitude of a specific location obtained by a location detection device.

[0107] A "data processing device" is a computer system, including a server, for processing and analyzing received data.

[0108] "Machine learning means" refers to means, including an AI analysis module, that analyzes received image data and geographic information and generates a specific judgment result.

[0109] "Map data" refers to digital maps that are updated in real time and provide users with location information and road and trail conditions.

[0110] A "user terminal" is a device such as a smartphone or computer owned by a user, which displays data and messages sent from the server.

[0111] A "warning message" is a message that is generated based on the analysis results and provides specific attention or information to the user.

[0112] System Overview

[0113] This invention is a system that grasps the condition of roads, hiking trails, etc. in real time based on image data taken by the user and reflects this information in map data. This system allows the user to check the latest conditions and take prompt and appropriate action.

[0114] Hardware and software used

[0115] In implementing this system, the following hardware and software are used:

[0116] Smartphone or dedicated device (user device)

[0117] GPS module (position detection device)

[0118] Internet communication function (network)

[0119] Server (data processing device)

[0120] AI analysis module (machine learning method)

[0121] Map display software (map data and display means)

[0122] Data collection and transmission

[0123] The user uses a smartphone or dedicated device to take photos and videos (image data) of the road or hiking trail of interest. For example, a photo of a fallen tree may be taken while hiking. The device uses its built-in GPS module to obtain the latitude and longitude of the location where the photo was taken. For example, the device obtains information such as "latitude 35.625, longitude 139.243."

[0124] Next, the device sends the acquired image data and location information (geographical information) to a server via the Internet. This data transmission uses the HTTPS protocol to ensure data security.

[0125] Receiving and analyzing data

[0126] The server receives data packets sent from the device. The received photos, videos, and location information are first stored in a database. The server then inputs the stored photos and videos into an AI analysis module.

[0127] The AI ​​analysis module analyzes elements in the image (e.g., fallen trees, snow, collapsed roads, etc.) to determine passability, required equipment, and congestion. For example, if a hiking trail is covered in snow, the analysis results will be "impassable" and "Required equipment: snow gear."

[0128] Data integration and map updates

[0129] The server integrates the analysis results returned by the AI ​​analysis tool with the location information. For example, if the location at latitude 35.625, longitude 139.243 is determined to be impassable, the map data is updated to show that location in red. This ensures that the map data viewed by the user is always up to date.

[0130] User Notification and Assistance

[0131] The server generates an alert message based on the analysis results. For example, it generates a message saying, "This hiking trail is currently impassable. Please bring snow gear." This alert message is then sent to the user's device via the network. The device receives this message and immediately notifies the user, who can then take appropriate action.

[0132] Specific examples

[0133] Examples of hiking trails

[0134] User A takes a photo on the hiking trail on Mount Takao and uploads it through the app. The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server. The server receives the data and analyzes the photo using AI analysis. The AI ​​recognizes that the hiking trail is covered in snow and generates a result that says "impassable." The server updates the map data based on the analysis results and sets the target area to be displayed in red. The device displays the latest map data and notifies User A, "This hiking trail is currently impassable. Please bring snow gear."

[0135] Example of a disaster

[0136] User B takes a photo of the road after the earthquake and uploads it through the app. The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server. The server receives the data and analyzes the photo using AI analysis. The AI ​​recognizes that the road has collapsed and generates a result that says "impassable." The server updates the map data based on the analysis results and sets the affected area to be displayed in red. The device displays the latest map data and notifies User B, "This road is currently impassable. Please select another route."

[0137] Prompt Sentence Examples

[0138] "Analyze the condition of the hiking trail in this photo and determine the necessary equipment and passability."

[0139] "Please analyze the photo of this road and let me know the current traffic situation."

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1: Data collection

[0142] The user uses a smartphone or dedicated device to take photos and videos of the road or hiking trail of interest. The input data is the captured image data, and location information (latitude and longitude) is obtained using a GPS module. This location information becomes additional input data. Specifically, the user operates a camera app to take a photo. The device receives the GPS signal and adds the obtained location information to the image data.

[0143] Step 2: Send data

[0144] The terminal assembles the acquired image data and location information into packets and sends them to a server via the Internet. The input data is a packet containing the image data and location information, and the output data is the packet sent to the server. Specifically, the terminal uses an internal communication module to send data to the server securely and quickly using the HTTPS protocol.

[0145] Step 3: Receiving data

[0146] The server receives data packets sent from the device. The input data is image data and location information in packet format, and the output data is the storage of the received data in a repository for analysis. Specifically, the server receives data via a specific API endpoint and stores the received image data and location information in a database.

[0147] Step 4: Data analysis

[0148] The server inputs the stored image data and location information into the AI ​​analysis module. The input data is the image data and location information, and the output data is the analysis results. Specifically, the AI ​​analysis module analyzes elements in the image (e.g., fallen trees, snow, collapsed roads, etc.) and determines whether the path is passable, what equipment is required, and how crowded the path is. For example, the AI ​​recognizes that a hiking trail is covered in snow and generates the results "impassable" and "Required equipment: snow gear."

[0149] Step 5: Data Integration

[0150] The server integrates the analysis results and location information returned from the AI ​​analysis means. The input data are the analysis results and location information, and the output data is updated map data. Specifically, the server reflects the analysis results in the map data, making it possible to display the relevant points in a new state. For example, it can be set to display impassable points in red.

[0151] Step 6: Data Distribution

[0152] The server generates an alert message based on the analysis results and sends it to the user's terminal. The input data is the analysis results, and the output data is the generated alert message and its delivery to the user's terminal. Specifically, the server generates an appropriate warning message from the analysis results and sends it to the user's terminal via the network.

[0153] Step 7: User Notification

[0154] The terminal notifies the user of the alert message received from the server. The input data is the alert message, and the output data is the notification to the user. Specifically, the terminal displays the received message as a pop-up on the screen to immediately alert the user. The user can then take appropriate action based on this.

[0155] (Application example 1)

[0156] 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."

[0157] Conventional self-driving vehicles have had difficulty grasping road conditions in real time and immediately sharing appropriate information with other vehicles. As a result, they have been unable to respond quickly to emergencies such as road obstacles and accidents, which has led to problems such as reduced driving safety and efficiency.

[0158] 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.

[0159] In this invention, the server includes means for uploading photos and videos taken by the user via the Internet, means for acquiring current location information using a GPS module, means for transmitting the taken photos and videos and the location information to the server, artificial intelligence means for analyzing the received photos, videos and location information in the server, means for updating map data in real time based on the information analyzed by the artificial intelligence means and sending alerts to other autonomous vehicles, and means for displaying the updated map data on the user terminal, thereby enabling road obstacles and emergency situations to be quickly detected and information to be shared with other autonomous vehicles in real time.

[0160] A "User" is a person or organization that uses the System to take photos and videos and upload them via the Internet.

[0161] A "GPS module" is a device for obtaining geographical location information, and is used to obtain data such as latitude and longitude.

[0162] A "server" is a computer system that receives and processes data, stores photos, videos, and location information, and analyzes them using artificial intelligence.

[0163] "Artificial intelligence means" refers to technology that analyzes received photos and videos to determine road conditions, passability, and necessary equipment.

[0164] "Map data" refers to electronic data containing geographical information, and is used to visually display road conditions, passability, and the like.

[0165] "Updating means" refers to a device or system that has the function of changing map data in real time based on information analyzed by the artificial intelligence means.

[0166] "Means for sending alerts" is a function for sending emergency information or important notifications to other autonomous vehicles or users.

[0167] A "user terminal" is a device used by a user to receive and display information, such as a smartphone or tablet.

[0168] This system uses the camera and GPS module of the autonomous vehicle to grasp the current road conditions in real time and provides information to other autonomous vehicles and users based on the analysis results. The entire system consists of a terminal, a server, an artificial intelligence means, a map data update means, an alert sending means, and a user terminal.

[0169] program

[0170] The system's programming is structured as follows:

[0171] 1. An autonomous vehicle equipped with a camera and GPS module uses the camera to continuously take photos and videos of the road.

[0172] 2. The GPS module obtains the location information (latitude and longitude) of the current location where the photo or video was taken.

[0173] 3. The device sends the captured photos and videos, as well as location information, to a server via the Internet.

[0174] 4. The server stores the received photos, videos, and location information in a database.

[0175] 5. The server inputs the stored photos and videos into the artificial intelligence means, which analyzes elements within the images, including machine learning models to detect fallen trees, snow, and road collapses, for example.

[0176] 6. Based on the information analyzed by the artificial intelligence means, the map data update means updates the map data in real time. The updated map data is visually displayed, and impassable areas are marked in red.

[0177] 7. The alert sending means sends the analysis results to other autonomous vehicles to alert them to road obstacles or emergency situations.

[0178] 8. The user device receives the updated map data and alert messages from the server and displays or notifies the user.

[0179] Hardware and Software

[0180] The hardware used is a camera and GPS module installed in the autonomous vehicle, as well as a terminal that can connect to the Internet. The server is equipped with a database and artificial intelligence means, which are implemented using machine learning libraries such as Python and TensorFlow.

[0181] Specific examples

[0182] For example, while autonomous vehicle A is driving down a road, it takes pictures of a fallen tree on the road with its camera. These pictures, along with location information obtained by the GPS module, are sent to a server via the device. The server stores the received data and analyzes it using artificial intelligence means. When a fallen tree is detected, map data is updated in real time, and the affected area is highlighted in red. Furthermore, an alert is sent to other autonomous vehicles, allowing them to automatically change their routes to avoid the affected road. This improves the safety of autonomous vehicles overall.

[0183] Prompt Sentence Examples

[0184] "Please design a system that will analyze the current road conditions captured by my self-driving vehicle in real time and provide traffic updates. It will also use camera images and GPS data, perform AI analysis, and provide alerts to notify other vehicles."

[0185] The above is the mode for carrying out the invention in this system.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] Taking photos and videos with the camera

[0189] Cameras in self-driving vehicles capture road conditions in real time.

[0190] Input: Road image

[0191] Output: Photos and videos taken

[0192] What it does: The camera periodically captures images and saves frames at regular intervals.

[0193] Step 2:

[0194] Obtaining location information using the GPS module

[0195] The GPS module installed in the autonomous vehicle acquires the latitude and longitude of its current location.

[0196] Input: Position data from satellites

[0197] Output: Current location latitude and longitude

[0198] How it works: The GPS module periodically communicates with satellites to update location information.

[0199] Step 3:

[0200] Sending data

[0201] The device sends the photos and videos taken and the acquired location information to a server via the Internet.

[0202] Input: photos, videos, location information

[0203] Output: Data sent to the server

[0204] Specific operation: The terminal packetizes the data and sends a POST request to the server using the HTTP protocol.

[0205] Step 4:

[0206] Receiving and storing data on the server

[0207] The server stores the received photos, videos, and location information in a database.

[0208] Input: photos, videos, location information

[0209] Output: Data recorded in the database

[0210] Specific operation: The server parses the received data and stores it in the corresponding database fields.

[0211] Step 5:

[0212] Data analysis by means of artificial intelligence

[0213] The server's artificial intelligence means analyzes the received photos and videos to determine the condition of the road and whether it is passable.

[0214] Input: photos, videos, location information

[0215] Output: Analysis results such as "fallen tree detection" and "passage impassable"

[0216] Specific operation: The artificial intelligence means analyzes the image using a machine learning model, extracts features, and generates a judgment result.

[0217] Step 6:

[0218] Real-time map data updates

[0219] The server updates the map data in real time based on the analysis results.

[0220] Input: Analysis results, location information

[0221] Output: Updated map data

[0222] Specific operation: The server reads map data from the database, corrects the relevant parts based on the analysis results, and re-saves them.

[0223] Step 7:

[0224] Sending alerts

[0225] Based on the analysis results, the server sends alerts to other self-driving vehicles and users as needed.

[0226] Input: Analysis results, location information

[0227] Output: Alert message sent

[0228] Specific operation: The server generates an alert message and sends it to other autonomous vehicles via means such as push notifications.

[0229] Step 8:

[0230] Displaying information on the user's device

[0231] The user terminal receives the latest map data and alert message and displays or notifies the user.

[0232] Input: Updated map data, alert message

[0233] Output: Map data and alert messages displayed to the user

[0234] Specific operation: The user terminal receives the data from the server and displays it to the user through the GUI.

[0235] Through these processing steps, autonomous vehicles can monitor road conditions in real time and quickly share information with other vehicles, greatly improving road safety and efficiency.

[0236] 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.

[0237] System Overview

[0238] This invention is a system that grasps the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflects this in map data. This system also incorporates an emotion engine that recognizes the user's emotions, and generates customized alert messages according to the user's situation, thereby providing more effective information provision and support for countermeasures.

[0239] Data Collection Stage

[0240] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[0241] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[0242] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[0243] Data reception and analysis stage

[0244] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[0245] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[0246] Emotion Engine

[0247] The device also captures the user's facial expressions and voice when taking photos and videos.

[0248] The server sends this emotional data to the emotion engine, which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state.

[0249] The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[0250] Data linkage and display stage

[0251] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[0252] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[0253] User Support Stage

[0254] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[0255] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action.

[0256] Specific examples

[0257] Examples of hiking trails

[0258] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[0259] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[0260] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the mountain trail is covered in snow and generates a result of "impassable." The emotion engine recognizes the impatience in the user's facial expression and generates a message saying, "Please stay calm. Please bring snow gear."

[0261] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0262] The device displays the latest map data and notifies the user with customized alert messages.

[0263] Example of a disaster

[0264] User B takes a photo of the road after the earthquake and uploads it through the app.

[0265] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[0266] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying, "Please remain calm. Please choose another route."

[0267] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0268] The device displays the latest map data and notifies the user with customized alert messages.

[0269] In this way, the system not only improves the accuracy of map data but also provides customized messages based on the user's emotional state, supporting safe and effective response.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] Users use their smartphones or dedicated devices to take photos and videos of roads and hiking trails.

[0273] Step 2:

[0274] The device uses a camera module to simultaneously capture the user's facial expressions and voice, and this data is used by the emotion engine.

[0275] Step 3:

[0276] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[0277] Step 4:

[0278] The device sends the captured photos and videos, as well as current location information, and data on the user's facial expressions and voice, to a server via the Internet.

[0279] Step 5:

[0280] The server receives data packets sent from the device, analyzes the received data, and stores photos, videos, location information, facial expressions, and voice data in a database.

[0281] Step 6:

[0282] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[0283] Step 7:

[0284] The server inputs facial and voice data into the emotion engine, which then analyzes the user's emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as "anxiety" or "impatience."

[0285] Step 8:

[0286] AI tools determine whether a path is passable, what equipment is needed, and how crowded it is. At the same time, the emotion engine combines the analyzed emotional data and generates a customized alert message based on the analysis results. For example, if the trail is covered in snow and the user is feeling anxious, the message generated is, "Please stay calm. Please bring snow gear."

[0287] Step 9:

[0288] The server integrates the analysis results returned by the AI ​​analysis means and emotion engine with the location information, and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[0289] Step 10:

[0290] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[0291] Step 11:

[0292] The device displays the received map data and notifies the user of the current situation in real time, for example by displaying warning messages and icons on the screen.

[0293] Step 12:

[0294] The server generates and sends an alert message to the user based on the analysis results. In the event of an emergency, the alert message is sent to the user as a push notification.

[0295] Step 13:

[0296] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action. For example, the message might read, "This road is currently collapsed and impassable. Please choose an alternative route."

[0297] Example 2

[0298] 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."

[0299] Conventional map data update systems lack the functionality to reflect user-provided information in real time, and are insufficient in providing safety information based on the user's emotional state, making it difficult to take effective measures to ensure the user's safety. There is a growing need for a system that can instantly reflect the situation the user faces and alert them to the appropriate equipment and routes, especially during mountain climbing and disasters.

[0300] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0301] In this invention, the server includes artificial intelligence means for analyzing received photos, videos, and location information, means for including in the server an emotion engine for analyzing the user's emotional state based on the analysis, and means for updating map data based on the information analyzed by the artificial intelligence means and the emotion engine. This makes it possible to analyze data captured by the user in real time, generate customized alert messages according to the user's emotional state, and instantly update the map data.

[0302] A "user" is an entity that uses the system to take photos and videos and obtain location information.

[0303] A "terminal" is a smartphone or dedicated device used by a user, which takes photos and videos, obtains location information using a GPS module, and transmits the data to a server via the Internet.

[0304] A "GPS module" is a receiving device for identifying a location on Earth and is a device used to obtain latitude and longitude information.

[0305] A "server" is a device or system that receives photos, videos, and location information sent from a terminal, analyzes and stores this data, updates map data, and generates alert messages for users.

[0306] "Artificial intelligence means" is a general term for algorithms and technologies that analyze photographs and videos, recognize the elements and situations contained therein, and generate analytical results.

[0307] The "emotion engine" is a system that uses facial expression recognition technology and voice analysis technology to analyze the user's facial expressions and voice and recognize their emotional state.

[0308] "Map data" is data that represents geographical information, and is updated based on content including the status of roads, hiking trails, ports, houses, and the like.

[0309] An "alert message" is a notification message generated based on the analysis results and the user's emotional state, and encourages the user to take appropriate action.

[0310] System Overview

[0311] This system aims to grasp the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflect them in map data. It also incorporates an emotion engine that recognizes the user's emotions and generates customized alert messages according to the user's emotional state. This allows for more effective provision of information and support for countermeasures to users.

[0312] Data Collection Stage

[0313] The user uses a smartphone or dedicated device to take photos and videos of the road or hiking trail in question by pressing the camera button on the smartphone's camera application.

[0314] The device uses the GPS module to obtain location information (latitude and longitude) of the current location where the photo or video was taken. The obtained location information is tagged with the photo or video. Specifically, the GPS sensor in the device measures the current location, and the location information is added to the metadata based on this.

[0315] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[0316] Data reception and analysis stage

[0317] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database. The server listens for data on the receiving port, analyzes the data packets for photos, videos, and location information, and stores them in a database.

[0318] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the image. Specifically, the AI ​​module (e.g., TensorFlow, OpenCV) recognizes situations such as fallen trees, snow, collapsed roads, and flooding.

[0319] Emotion Engine

[0320] When taking photos or videos, the device also captures the user's facial expressions and voice. The device's camera and microphone are activated to collect the user's facial and voice data.

[0321] The server sends this emotional data to an emotion engine (e.g., Microsoft Azure Emotion API), which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state. The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[0322] Data linkage and display stage

[0323] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[0324] The server sends the updated map data to the device. When the user opens the application, the latest map information is displayed. The server generates the latest map data and sends it to the device in a data packet.

[0325] User Support Stage

[0326] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[0327] The device receives the alert message and displays a notification to the user. The user can check the notification and consider what action is necessary. The device displays the received alert message as a pop-up notification, prompting the user to take action.

[0328] Specific examples

[0329] Examples of hiking trails

[0330] User A takes a photo on the hiking trail on Mount Takao and uploads it through the app. The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server. The server receives the data and analyzes the photo and the user's emotional state using AI analysis means and an emotion engine. The AI ​​recognizes that the hiking trail is covered in snow and generates a result saying "impassable." The emotion engine recognizes the user's impatience from their facial expression and generates a message saying "Please stay calm. Please bring snow gear." The server updates the map data based on the analysis results and sets the target area to be displayed in red. The device displays the latest map data and notifies the user with a customized alert message.

[0331] Example of a disaster

[0332] User B takes a photo of the road after the earthquake and uploads it through the app. The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server. The server receives the data and analyzes the photo and the user's emotional state using AI analysis means and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying "Please remain calm. Please select an alternative route." The server updates the map data based on the analysis results and sets the affected area to be displayed in red. The device displays the latest map data and notifies the user with a customized alert message.

[0333] Prompt Sentence Examples

[0334] "Uploading the status of the hiking trails on Mount Takao. Current location is latitude 35.625, longitude 139.243. Snow is visible in the photo. I'm getting anxious."

[0335] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0336] System processing flow

[0337] Step 1:

[0338] Users take photos and videos using their smartphones or dedicated devices.

[0339] Input: Photos and videos taken by the user.

[0340] Output: Captured photo and video files.

[0341] Specific actions: The user opens the camera app on their smartphone, frames the subject, and presses the capture button.

[0342] Step 2:

[0343] The device uses the GPS module to obtain location information (latitude and longitude) and tag photos and videos.

[0344] Input: Photos and videos taken, location data from the GPS module.

[0345] Output: Location-tagged photo and video files.

[0346] How it works: The device's GPS sensor measures your current location and adds the acquired latitude and longitude as metadata to photo and video files.

[0347] Step 3:

[0348] The device sends photos, videos, and location information to a server via the Internet, and the user presses the "upload" button within the application.

[0349] Input: Location-tagged photo and video files.

[0350] Output: Data packets sent to the server.

[0351] Specific operation: The user presses the "upload" button in the application, and the device sends a data packet to the server via the Internet.

[0352] Step 4:

[0353] The server receives data packets sent from the device, analyzes the photos, videos, and location information, and stores them in a database.

[0354] Input: The data packet sent to the server.

[0355] Output: Photos, videos, and location information stored in a database.

[0356] Specific operation: The server listens for data on the receiving port, extracts the received data from the packets, and stores the photos, videos, and location information in a database.

[0357] Step 5:

[0358] The server inputs the stored photos and videos into an AI analysis module, which analyzes the elements within the images.

[0359] Input: Photos and videos stored in the database.

[0360] Output: Analysis results for each image.

[0361] Specific operation: The server inputs the image data into an AI analysis module (e.g., TensorFlow, OpenCV) and obtains the analysis results. The AI ​​recognizes situations such as fallen trees, snow, collapsed roads, and flooding.

[0362] Step 6:

[0363] The device also captures the user's facial expressions and voice when taking photos and videos.

[0364] Input: The user's facial expressions and voice at the time of capture.

[0365] Output: User's facial and voice data.

[0366] Specific operation: The device's camera and microphone are activated to collect the user's facial and voice data.

[0367] Step 7:

[0368] The server sends the user's facial expression and voice data to the emotion engine, which analyzes the user's emotional state.

[0369] Input: User facial and voice data.

[0370] Output: Parsed emotional state of the user.

[0371] Specific operation: The server inputs the received data into an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotional state.

[0372] Step 8:

[0373] The server integrates the analysis results of the AI ​​analysis means and emotion engine with the location information and updates the map data.

[0374] Input: Analysis results of AI analysis method and emotion engine, and location information.

[0375] Output: Updated map data.

[0376] Specific operation: The server integrates the analysis results and location information into a map database, color-coding the relevant area, and updating it.

[0377] Step 9:

[0378] The server sends the updated map data to the terminal, and the latest map information is displayed when the user opens the application.

[0379] Input: Updated map data.

[0380] Output: The latest map data sent to the device.

[0381] Specific operation: The server generates the latest map data and sends it to the device as a data packet.

[0382] Step 10:

[0383] The server generates a customized alert message based on the analysis results and sends it to the user.

[0384] Input: Analysis results of AI analysis method and emotion engine.

[0385] Output: The alert message sent to the user.

[0386] Specific operation: The server generates an alert message based on the analysis results and sends it to the terminal as a data packet.

[0387] Step 11:

[0388] The device receives the alert message and displays a notification to the user, who then checks the notification and considers what action to take.

[0389] Input: The alert message sent by the server.

[0390] Output: The alert message that will be displayed to the user.

[0391] Specific operation: The device displays the received alert message as a pop-up notification, prompting the user to take action.

[0392] (Application example 2)

[0393] 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."

[0394] Conventional road condition monitoring systems and hiking trail information systems have difficulty accurately grasping local conditions in real time. Furthermore, they are unable to provide information that takes into account the user's psychological state, making it difficult to support appropriate responses in emergencies or stressful situations. This presents a problem of a lack of support for users to make calm decisions on their own.

[0395] 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.

[0396] In this invention, the server includes means for uploading photos and videos taken by the user via the Internet, means for acquiring current location information using a GPS module, means for transmitting the taken photos and videos and the location information to the server, AI means for analyzing the received photos, videos, and location information, means including an emotion engine for capturing the user's facial expressions and voice and analyzing their emotional state, means for updating map data based on the information analyzed by the AI ​​means and the analysis results of the emotion engine, and means for displaying the updated map data and a customized alert message on the user terminal. This makes it possible to grasp the local situation in real time and provide an appropriate alert message based on the user's emotional state.

[0397] A "User" is any person or entity that utilizes the system to take photos and videos and upload the data over the Internet.

[0398] "Photos and videos" are still images and videos taken by users to record local conditions and upload them to the system.

[0399] The "Internet" is a global network for transmitting and receiving data between user terminals and servers.

[0400] A "GPS module" is a device that measures the current location of a user terminal and acquires latitude and longitude information.

[0401] A "server" is a centralized computer system that receives, processes, and analyzes data sent from user terminals over the Internet.

[0402] "AI means" is an artificial intelligence system that analyzes road conditions and hiking trail conditions from photos and videos, determines passability and necessary equipment, and generates customized alert messages based on the user's emotional state.

[0403] The "emotion engine" is a system that captures the user's facial expressions and voice and analyzes their emotional state.

[0404] "Map data" refers to geographical information data that is updated based on location information obtained from users and the results of analysis of photos and videos.

[0405] An "alert message" is a warning or advice message that is generated based on the analysis results and the user's emotional state.

[0406] A "user terminal" is an electronic device such as a smartphone or dedicated terminal that a user uses to access the system.

[0407] MODE FOR CARRYING OUT THE INVENTION

[0408] In order to implement the present invention, the following hardware and software are used.

[0409] Hardware

[0410] 1. Smartphone or dedicated device: Equipped with a camera function and a GPS module.

[0411] 2. Server: A centralized computer system with a powerful processor and large amount of storage.

[0412] software

[0413] 1. AI analysis module: An image analysis system that uses machine learning libraries such as TensorFlow and PyTorch.

[0414] 2. Emotion recognition engine: The EmotionRecognizer library combines facial expression recognition technology and voice analysis technology.

[0415] 3. Data communication libraries: Software for sending and receiving data over the Internet, such as the requests library.

[0416] System Flow

[0417] 1. Data Collection Stage

[0418] Users use their smartphones or dedicated devices to take photos and videos of local roads and hiking trails.

[0419] The device uses a GPS module to obtain location information and tag photos and videos.

[0420] These data are sent to a server over the Internet, where the user presses the "upload" button in the application.

[0421] 2. Data Reception and Analysis Stage

[0422] The server receives the data sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[0423] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[0424] 3. Emotion Engine

[0425] The device also captures the user's facial expressions and voice when taking photos and videos.

[0426] The server sends this emotional data to the emotion engine, which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state.

[0427] The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[0428] 4. Data linkage and display stage

[0429] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[0430] The server sends the updated map data to the user's device, and when the user opens the application, the latest map information is displayed.

[0431] 5. User Support Stage

[0432] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[0433] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action.

[0434] Specific examples

[0435] Examples of hiking trails

[0436] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[0437] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[0438] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the mountain trail is covered in snow and generates a result of "impassable." The emotion engine recognizes the impatience in the user's facial expression and generates a message saying, "Please stay calm. Please bring snow gear."

[0439] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0440] The device displays the latest map data and notifies the user with customized alert messages.

[0441] Example of a disaster

[0442] User B takes a photo of the road after the earthquake and uploads it through the app.

[0443] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[0444] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying, "Please remain calm. Please choose another route."

[0445] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0446] The device displays the latest map data and notifies the user with customized alert messages.

[0447] Prompt Sentence Examples

[0448] "The map should reflect the latest road conditions based on photos taken by the user. The alert message should also take into account the user's emotional state."

[0449] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0450] Step 1:

[0451] Users use their smartphones or dedicated devices to take photos and videos of local roads and hiking trails.

[0452] Input: Photos and videos taken

[0453] Output: None (Photos and videos are saved on the device)

[0454] Specific actions: The user launches the device's camera app and presses the capture button.

[0455] Step 2:

[0456] The device uses a GPS module to obtain location information of the current location.

[0457] Input: GPS module of the device

[0458] Output: Current location (latitude and longitude)

[0459] Specific operation: The device's GPS function will automatically activate and obtain the current latitude and longitude.

[0460] Step 3:

[0461] The user presses the "upload" button in the application to send the photos and videos they have taken and their location information to the server.

[0462] Input: Photos, videos, location information

[0463] Output: Send data to the server

[0464] What happens: The user presses the upload button in the application, and the data is sent over the Internet to the server.

[0465] Step 4:

[0466] The server stores the received photos, videos, and location information in a database for analysis.

[0467] Input: Photos, videos, and location information sent

[0468] Output: Data stored in the database

[0469] Specific operation: The server writes the received data to the database.

[0470] Step 5:

[0471] The server inputs the stored photos and videos into an AI analysis module, which analyzes the elements within the images.

[0472] Input: Photos and videos stored in the database

[0473] Output: Analyzed image data (e.g., fallen trees, snow, collapsed roads, etc.)

[0474] Specific operation: The AI ​​analysis module analyzes the image and outputs the recognized elements.

[0475] Step 6:

[0476] The device captures the user's facial expressions and voice along with photos and videos and sends them to the server.

[0477] Input: User's facial expressions, voice, photos and videos

[0478] Output: Send emotion data to the server

[0479] Specific operation: The device's camera and microphone capture the user's facial expressions and voice, and the data is sent to the server.

[0480] Step 7:

[0481] The server inputs the emotion data into an emotion engine to analyze the user's emotional state.

[0482] Input: Emotion data

[0483] Output: Analyzed emotional state (e.g., impatience, anxiety, tension, etc.)

[0484] Specific operation: The emotion engine analyzes facial and vocal data to determine the emotional state.

[0485] Step 8:

[0486] The server integrates the analysis results with location information and updates the map data in real time.

[0487] Input: Image analysis results, location information, emotional state

[0488] Output: Updated map data

[0489] Specific operation: The server integrates the analysis results and location information into the map database and updates the map.

[0490] Step 9:

[0491] The server generates a customized alert message based on the map data.

[0492] Input: Emotional state, analysis results

[0493] Output: Customized alert message

[0494] Specific behavior: The server takes into account the emotional state and analysis results to generate an appropriate alert message to send to the user.

[0495] Step 10:

[0496] The server sends updated map data and customized alert messages to the user terminal.

[0497] Input: Updated map data, alert message

[0498] Output: Sending data to the user's terminal

[0499] Specific operation: The server sends map data and an alert message to the user's device.

[0500] Step 11:

[0501] The device displays the latest map data and notifies the user with customized alert messages.

[0502] Input: Updated map data, alert message

[0503] Output: User notification

[0504] Specific operation: The device displays map data and presents an alert message to the user as a notification.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] [Second embodiment]

[0509] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0510] 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.

[0511] 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).

[0512] 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.

[0513] 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.

[0514] 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).

[0515] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0516] 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.

[0517] 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.

[0518] 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.

[0519] In the smart glasses 214, 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.

[0520] 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."

[0521] System Overview

[0522] This invention is a system that grasps the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflects this information in map data, allowing users to check the latest status and take prompt and appropriate action.

[0523] Data Collection Stage

[0524] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[0525] The device uses the GPS module to obtain the current location information (latitude and longitude) of the location where the photo or video was taken.

[0526] The device sends the captured photos and videos, as well as its current location information, to a server via the Internet.

[0527] Data reception and analysis stage

[0528] The server receives the data packets sent from the device and stores the photos, videos, and location information in a database.

[0529] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, such as fallen trees, snow, and collapsed roads.

[0530] AI analysis methods determine passability, required equipment, and congestion status. For example, if a trail is covered in snow, the results will be "Impassable" and "Required Equipment: Snow Gear."

[0531] Data linkage and display stage

[0532] The server integrates the analysis results returned by the AI ​​analysis means with the location information and updates the map data in real time.

[0533] The updated map data is color-coded and symbolized based on the condition; for example, impassable areas are displayed in red.

[0534] User Support Stage

[0535] The server generates an alert message based on the analysis results and sends it to the user. For example, if a road is collapsed, a message will be sent saying, "This road is currently impassable. Please choose an alternative route."

[0536] The device receives updates and alert messages from the server and notifies the user, who can then take appropriate action.

[0537] Specific examples

[0538] Examples of hiking trails

[0539] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[0540] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[0541] The server receives the data and analyzes the photos using AI analysis methods, which recognizes the trail as covered in snow and generates a result that it is "impassable."

[0542] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0543] The device displays the latest map data and notifies User A, "This hiking trail is currently impassable. Please bring snow gear."

[0544] Example of a disaster

[0545] User B takes a photo of the road after the earthquake and uploads it through the app.

[0546] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[0547] The server receives the data and analyzes the photos using AI analysis methods. The AI ​​recognizes that the road has collapsed and generates a result that says "impassable."

[0548] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0549] The device displays the latest map data and notifies User B, "This road is currently impassable. Please choose another route."

[0550] This allows users to obtain the latest information in real time and take prompt action, improving the accuracy of map data and user safety.

[0551] The processing flow will be explained below.

[0552] Step 1:

[0553] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[0554] Step 2:

[0555] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[0556] Step 3:

[0557] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[0558] Step 4:

[0559] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[0560] Step 5:

[0561] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements within the image. Specifically, the AI ​​scans the image and recognizes conditions such as fallen trees, snow, collapsed roads, and flooding.

[0562] Step 6:

[0563] AI analysis methods determine passability, required equipment, and congestion status. For example, if a trail is covered in snow, the results will be "Impassable" and "Required Equipment: Snow Gear."

[0564] Step 7:

[0565] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[0566] Step 8:

[0567] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[0568] Step 9:

[0569] The device displays the received map data and notifies the user of the current situation in real time, for example by displaying warning messages and icons on the screen.

[0570] Step 10:

[0571] The server generates and sends an alert message to the user based on the analysis results. In the event of an emergency, the alert message is sent to the user as a push notification.

[0572] Step 11:

[0573] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action. For example, the message might read, "This road is currently collapsed and impassable. Please choose an alternative route."

[0574] Example 1

[0575] 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."

[0576] In modern society, it is extremely important to quickly grasp natural disasters and sudden changes in road conditions and take appropriate measures. However, conventional systems have difficulty collecting information and updating map data in real time, which means that users have to spend a great deal of time and effort obtaining the latest information. In addition, determining whether a road is passable and what equipment is needed is also manual, so a highly accurate and rapid response is required.

[0577] 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.

[0578] In this invention, the server includes means for transferring image data captured by a user via a network, means for acquiring geographic information of the current location using a position detection device, means for transmitting the captured image data and the geographic information to a data processing device, machine learning means in the data processing device for analyzing the received image data and geographic information, means for updating map data based on the information analyzed by the machine learning means, and means for displaying the updated map data on a user terminal. This makes it possible to grasp road conditions and mountain trail conditions in real time and quickly and accurately determine passability and necessary equipment.

[0579] A "user" is an entity that uses the system to capture image data and transmits related information to the server.

[0580] "Image data" refers to digital data of photographs and videos taken by the user.

[0581] "Network" refers generally to a communications network, including the Internet, used to transmit and receive data.

[0582] A "location detection device" is a hardware and software device, including a GPS module, that detects the geographic information (latitude and longitude) of a current location.

[0583] "Geographic information" refers to data on the latitude and longitude of a specific location obtained by a location detection device.

[0584] A "data processing device" is a computer system, including a server, for processing and analyzing received data.

[0585] "Machine learning means" refers to means, including an AI analysis module, that analyzes received image data and geographic information and generates a specific judgment result.

[0586] "Map data" refers to digital maps that are updated in real time and provide users with location information and road and trail conditions.

[0587] A "user terminal" is a device such as a smartphone or computer owned by a user, which displays data and messages sent from the server.

[0588] A "warning message" is a message that is generated based on the analysis results and provides specific attention or information to the user.

[0589] System Overview

[0590] This invention is a system that grasps the condition of roads, hiking trails, etc. in real time based on image data taken by the user and reflects this information in map data. This system allows the user to check the latest conditions and take prompt and appropriate action.

[0591] Hardware and software used

[0592] In implementing this system, the following hardware and software are used:

[0593] Smartphone or dedicated device (user device)

[0594] GPS module (position detection device)

[0595] Internet communication function (network)

[0596] Server (data processing device)

[0597] AI analysis module (machine learning method)

[0598] Map display software (map data and display means)

[0599] Data collection and transmission

[0600] The user uses a smartphone or dedicated device to take photos and videos (image data) of the road or hiking trail of interest. For example, a photo of a fallen tree may be taken while hiking. The device uses its built-in GPS module to obtain the latitude and longitude of the location where the photo was taken. For example, the device obtains information such as "latitude 35.625, longitude 139.243."

[0601] Next, the device sends the acquired image data and location information (geographical information) to a server via the Internet. This data transmission uses the HTTPS protocol to ensure data security.

[0602] Receiving and analyzing data

[0603] The server receives data packets sent from the device. The received photos, videos, and location information are first stored in a database. The server then inputs the stored photos and videos into an AI analysis module.

[0604] The AI ​​analysis module analyzes elements in the image (e.g., fallen trees, snow, collapsed roads, etc.) to determine passability, required equipment, and congestion. For example, if a hiking trail is covered in snow, the analysis results will be "impassable" and "Required equipment: snow gear."

[0605] Data integration and map updates

[0606] The server integrates the analysis results returned by the AI ​​analysis tool with the location information. For example, if the location at latitude 35.625, longitude 139.243 is determined to be impassable, the map data is updated to show that location in red. This ensures that the map data viewed by the user is always up to date.

[0607] User Notification and Assistance

[0608] The server generates an alert message based on the analysis results. For example, it generates a message saying, "This hiking trail is currently impassable. Please bring snow gear." This alert message is then sent to the user's device via the network. The device receives this message and immediately notifies the user, who can then take appropriate action.

[0609] Specific examples

[0610] Examples of hiking trails

[0611] User A takes a photo on the hiking trail on Mount Takao and uploads it through the app. The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server. The server receives the data and analyzes the photo using AI analysis. The AI ​​recognizes that the hiking trail is covered in snow and generates a result that says "impassable." The server updates the map data based on the analysis results and sets the target area to be displayed in red. The device displays the latest map data and notifies User A, "This hiking trail is currently impassable. Please bring snow gear."

[0612] Example of a disaster

[0613] User B takes a photo of the road after the earthquake and uploads it through the app. The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server. The server receives the data and analyzes the photo using AI analysis. The AI ​​recognizes that the road has collapsed and generates a result that says "impassable." The server updates the map data based on the analysis results and sets the affected area to be displayed in red. The device displays the latest map data and notifies User B, "This road is currently impassable. Please select another route."

[0614] Prompt Sentence Examples

[0615] "Analyze the condition of the hiking trail in this photo and determine the necessary equipment and passability."

[0616] "Please analyze the photo of this road and let me know the current traffic situation."

[0617] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0618] Step 1: Data collection

[0619] The user uses a smartphone or dedicated device to take photos and videos of the road or hiking trail of interest. The input data is the captured image data, and location information (latitude and longitude) is obtained using a GPS module. This location information becomes additional input data. Specifically, the user operates a camera app to take a photo. The device receives the GPS signal and adds the obtained location information to the image data.

[0620] Step 2: Send data

[0621] The terminal assembles the acquired image data and location information into packets and sends them to a server via the Internet. The input data is a packet containing the image data and location information, and the output data is the packet sent to the server. Specifically, the terminal uses an internal communication module to send data to the server securely and quickly using the HTTPS protocol.

[0622] Step 3: Receiving data

[0623] The server receives data packets sent from the device. The input data is image data and location information in packet format, and the output data is the storage of the received data in a repository for analysis. Specifically, the server receives data via a specific API endpoint and stores the received image data and location information in a database.

[0624] Step 4: Data analysis

[0625] The server inputs the stored image data and location information into the AI ​​analysis module. The input data is the image data and location information, and the output data is the analysis results. Specifically, the AI ​​analysis module analyzes elements in the image (e.g., fallen trees, snow, collapsed roads, etc.) and determines whether the path is passable, what equipment is required, and how crowded the path is. For example, the AI ​​recognizes that a hiking trail is covered in snow and generates the results "impassable" and "Required equipment: snow gear."

[0626] Step 5: Data Integration

[0627] The server integrates the analysis results and location information returned from the AI ​​analysis means. The input data are the analysis results and location information, and the output data is updated map data. Specifically, the server reflects the analysis results in the map data, making it possible to display the relevant points in a new state. For example, it can be set to display impassable points in red.

[0628] Step 6: Data Distribution

[0629] The server generates an alert message based on the analysis results and sends it to the user's terminal. The input data is the analysis results, and the output data is the generated alert message and its delivery to the user's terminal. Specifically, the server generates an appropriate warning message from the analysis results and sends it to the user's terminal via the network.

[0630] Step 7: User Notification

[0631] The terminal notifies the user of the alert message received from the server. The input data is the alert message, and the output data is the notification to the user. Specifically, the terminal displays the received message as a pop-up on the screen to immediately alert the user. The user can then take appropriate action based on this.

[0632] (Application example 1)

[0633] 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."

[0634] Conventional self-driving vehicles have had difficulty grasping road conditions in real time and immediately sharing appropriate information with other vehicles. As a result, they have been unable to respond quickly to emergencies such as road obstacles and accidents, which has led to problems such as reduced driving safety and efficiency.

[0635] 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.

[0636] In this invention, the server includes means for uploading photos and videos taken by the user via the Internet, means for acquiring current location information using a GPS module, means for transmitting the taken photos and videos and the location information to the server, artificial intelligence means for analyzing the received photos, videos and location information in the server, means for updating map data in real time based on the information analyzed by the artificial intelligence means and sending alerts to other autonomous vehicles, and means for displaying the updated map data on the user terminal, thereby enabling road obstacles and emergency situations to be quickly detected and information to be shared with other autonomous vehicles in real time.

[0637] A "User" is a person or organization that uses the System to take photos and videos and upload them via the Internet.

[0638] A "GPS module" is a device for obtaining geographical location information, and is used to obtain data such as latitude and longitude.

[0639] A "server" is a computer system that receives and processes data, stores photos, videos, and location information, and analyzes them using artificial intelligence.

[0640] "Artificial intelligence means" refers to technology that analyzes received photos and videos to determine road conditions, passability, and necessary equipment.

[0641] "Map data" refers to electronic data containing geographical information, and is used to visually display road conditions, passability, and the like.

[0642] "Updating means" refers to a device or system that has the function of changing map data in real time based on information analyzed by the artificial intelligence means.

[0643] "Means for sending alerts" is a function for sending emergency information or important notifications to other autonomous vehicles or users.

[0644] A "user terminal" is a device used by a user to receive and display information, such as a smartphone or tablet.

[0645] This system uses the camera and GPS module of the autonomous vehicle to grasp the current road conditions in real time and provides information to other autonomous vehicles and users based on the analysis results. The entire system consists of a terminal, a server, an artificial intelligence means, a map data update means, an alert sending means, and a user terminal.

[0646] program

[0647] The system's programming is structured as follows:

[0648] 1. An autonomous vehicle equipped with a camera and GPS module uses the camera to continuously take photos and videos of the road.

[0649] 2. The GPS module obtains the location information (latitude and longitude) of the current location where the photo or video was taken.

[0650] 3. The device sends the captured photos and videos, as well as location information, to a server via the Internet.

[0651] 4. The server stores the received photos, videos, and location information in a database.

[0652] 5. The server inputs the stored photos and videos into the artificial intelligence means, which analyzes elements within the images, including machine learning models to detect fallen trees, snow, and road collapses, for example.

[0653] 6. Based on the information analyzed by the artificial intelligence means, the map data update means updates the map data in real time. The updated map data is visually displayed, and impassable areas are marked in red.

[0654] 7. The alert sending means sends the analysis results to other autonomous vehicles to alert them to road obstacles or emergency situations.

[0655] 8. The user device receives the updated map data and alert messages from the server and displays or notifies the user.

[0656] Hardware and Software

[0657] The hardware used is a camera and GPS module installed in the autonomous vehicle, as well as a terminal that can connect to the Internet. The server is equipped with a database and artificial intelligence means, which are implemented using machine learning libraries such as Python and TensorFlow.

[0658] Specific examples

[0659] For example, while autonomous vehicle A is driving down a road, it takes pictures of a fallen tree on the road with its camera. These pictures, along with location information obtained by the GPS module, are sent to a server via the device. The server stores the received data and analyzes it using artificial intelligence means. When a fallen tree is detected, map data is updated in real time, and the affected area is highlighted in red. Furthermore, an alert is sent to other autonomous vehicles, allowing them to automatically change their routes to avoid the affected road. This improves the safety of autonomous vehicles overall.

[0660] Prompt Sentence Examples

[0661] "Please design a system that will analyze the current road conditions captured by my self-driving vehicle in real time and provide traffic updates. It will also use camera images and GPS data, perform AI analysis, and provide alerts to notify other vehicles."

[0662] The above is the mode for carrying out the invention in this system.

[0663] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0664] Step 1:

[0665] Taking photos and videos with the camera

[0666] Cameras in self-driving vehicles capture road conditions in real time.

[0667] Input: Road image

[0668] Output: Photos and videos taken

[0669] What it does: The camera periodically captures images and saves frames at regular intervals.

[0670] Step 2:

[0671] Obtaining location information using the GPS module

[0672] The GPS module installed in the autonomous vehicle acquires the latitude and longitude of its current location.

[0673] Input: Position data from satellites

[0674] Output: Current location latitude and longitude

[0675] How it works: The GPS module periodically communicates with satellites to update location information.

[0676] Step 3:

[0677] Sending data

[0678] The device sends the photos and videos taken and the acquired location information to a server via the Internet.

[0679] Input: photos, videos, location information

[0680] Output: Data sent to the server

[0681] Specific operation: The terminal packetizes the data and sends a POST request to the server using the HTTP protocol.

[0682] Step 4:

[0683] Receiving and storing data on the server

[0684] The server stores the received photos, videos, and location information in a database.

[0685] Input: photos, videos, location information

[0686] Output: Data recorded in the database

[0687] Specific operation: The server parses the received data and stores it in the corresponding database fields.

[0688] Step 5:

[0689] Data analysis by means of artificial intelligence

[0690] The server's artificial intelligence means analyzes the received photos and videos to determine the condition of the road and whether it is passable.

[0691] Input: photos, videos, location information

[0692] Output: Analysis results such as "fallen tree detection" and "passage impassable"

[0693] Specific operation: The artificial intelligence means analyzes the image using a machine learning model, extracts features, and generates a judgment result.

[0694] Step 6:

[0695] Real-time map data updates

[0696] The server updates the map data in real time based on the analysis results.

[0697] Input: Analysis results, location information

[0698] Output: Updated map data

[0699] Specific operation: The server reads map data from the database, corrects the relevant parts based on the analysis results, and re-saves them.

[0700] Step 7:

[0701] Sending alerts

[0702] Based on the analysis results, the server sends alerts to other self-driving vehicles and users as needed.

[0703] Input: Analysis results, location information

[0704] Output: Alert message sent

[0705] Specific operation: The server generates an alert message and sends it to other autonomous vehicles via means such as push notifications.

[0706] Step 8:

[0707] Displaying information on the user's device

[0708] The user terminal receives the latest map data and alert message and displays or notifies the user.

[0709] Input: Updated map data, alert message

[0710] Output: Map data and alert messages displayed to the user

[0711] Specific operation: The user terminal receives the data from the server and displays it to the user through the GUI.

[0712] Through these processing steps, autonomous vehicles can monitor road conditions in real time and quickly share information with other vehicles, greatly improving road safety and efficiency.

[0713] 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.

[0714] System Overview

[0715] This invention is a system that grasps the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflects this in map data. This system also incorporates an emotion engine that recognizes the user's emotions, and generates customized alert messages according to the user's situation, thereby providing more effective information provision and support for countermeasures.

[0716] Data Collection Stage

[0717] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[0718] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[0719] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[0720] Data reception and analysis stage

[0721] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[0722] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[0723] Emotion Engine

[0724] The device also captures the user's facial expressions and voice when taking photos and videos.

[0725] The server sends this emotional data to the emotion engine, which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state.

[0726] The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[0727] Data linkage and display stage

[0728] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[0729] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[0730] User Support Stage

[0731] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[0732] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action.

[0733] Specific examples

[0734] Examples of hiking trails

[0735] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[0736] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[0737] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the mountain trail is covered in snow and generates a result of "impassable." The emotion engine recognizes the impatience in the user's facial expression and generates a message saying, "Please stay calm. Please bring snow gear."

[0738] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0739] The device displays the latest map data and notifies the user with customized alert messages.

[0740] Example of a disaster

[0741] User B takes a photo of the road after the earthquake and uploads it through the app.

[0742] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[0743] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying, "Please remain calm. Please choose another route."

[0744] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0745] The device displays the latest map data and notifies the user with customized alert messages.

[0746] In this way, the system not only improves the accuracy of map data but also provides customized messages based on the user's emotional state, supporting safe and effective response.

[0747] The processing flow will be explained below.

[0748] Step 1:

[0749] Users use their smartphones or dedicated devices to take photos and videos of roads and hiking trails.

[0750] Step 2:

[0751] The device uses a camera module to simultaneously capture the user's facial expressions and voice, and this data is used by the emotion engine.

[0752] Step 3:

[0753] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[0754] Step 4:

[0755] The device sends the captured photos and videos, as well as current location information, and data on the user's facial expressions and voice, to a server via the Internet.

[0756] Step 5:

[0757] The server receives data packets sent from the device, analyzes the received data, and stores photos, videos, location information, facial expressions, and voice data in a database.

[0758] Step 6:

[0759] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[0760] Step 7:

[0761] The server inputs facial and voice data into the emotion engine, which then analyzes the user's emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as "anxiety" or "impatience."

[0762] Step 8:

[0763] AI tools determine whether a path is passable, what equipment is needed, and how crowded it is. At the same time, the emotion engine combines the analyzed emotional data and generates a customized alert message based on the analysis results. For example, if the trail is covered in snow and the user is feeling anxious, the message generated is, "Please stay calm. Please bring snow gear."

[0764] Step 9:

[0765] The server integrates the analysis results returned by the AI ​​analysis means and emotion engine with the location information, and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[0766] Step 10:

[0767] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[0768] Step 11:

[0769] The device displays the received map data and notifies the user of the current situation in real time, for example by displaying warning messages and icons on the screen.

[0770] Step 12:

[0771] The server generates and sends an alert message to the user based on the analysis results. In the event of an emergency, the alert message is sent to the user as a push notification.

[0772] Step 13:

[0773] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action. For example, the message might read, "This road is currently collapsed and impassable. Please choose an alternative route."

[0774] Example 2

[0775] 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."

[0776] Conventional map data update systems lack the functionality to reflect user-provided information in real time, and are insufficient in providing safety information based on the user's emotional state, making it difficult to take effective measures to ensure the user's safety. There is a growing need for a system that can instantly reflect the situation the user faces and alert them to the appropriate equipment and routes, especially during mountain climbing and disasters.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0778] In this invention, the server includes artificial intelligence means for analyzing received photos, videos, and location information, means for including in the server an emotion engine for analyzing the user's emotional state based on the analysis, and means for updating map data based on the information analyzed by the artificial intelligence means and the emotion engine. This makes it possible to analyze data captured by the user in real time, generate customized alert messages according to the user's emotional state, and instantly update the map data.

[0779] A "user" is an entity that uses the system to take photos and videos and obtain location information.

[0780] A "terminal" is a smartphone or dedicated device used by a user, which takes photos and videos, obtains location information using a GPS module, and transmits the data to a server via the Internet.

[0781] A "GPS module" is a receiving device for identifying a location on Earth and is a device used to obtain latitude and longitude information.

[0782] A "server" is a device or system that receives photos, videos, and location information sent from a terminal, analyzes and stores this data, updates map data, and generates alert messages for users.

[0783] "Artificial intelligence means" is a general term for algorithms and technologies that analyze photographs and videos, recognize the elements and situations contained therein, and generate analytical results.

[0784] The "emotion engine" is a system that uses facial expression recognition technology and voice analysis technology to analyze the user's facial expressions and voice and recognize their emotional state.

[0785] "Map data" is data that represents geographical information, and is updated based on content including the status of roads, hiking trails, ports, houses, and the like.

[0786] An "alert message" is a notification message generated based on the analysis results and the user's emotional state, and encourages the user to take appropriate action.

[0787] System Overview

[0788] This system aims to grasp the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflect them in map data. It also incorporates an emotion engine that recognizes the user's emotions and generates customized alert messages according to the user's emotional state. This allows for more effective provision of information and support for countermeasures to users.

[0789] Data Collection Stage

[0790] The user uses a smartphone or dedicated device to take photos and videos of the road or hiking trail in question by pressing the camera button on the smartphone's camera application.

[0791] The device uses the GPS module to obtain location information (latitude and longitude) of the current location where the photo or video was taken. The obtained location information is tagged with the photo or video. Specifically, the GPS sensor in the device measures the current location, and the location information is added to the metadata based on this.

[0792] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[0793] Data reception and analysis stage

[0794] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database. The server listens for data on the receiving port, analyzes the data packets for photos, videos, and location information, and stores them in a database.

[0795] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the image. Specifically, the AI ​​module (e.g., TensorFlow, OpenCV) recognizes situations such as fallen trees, snow, collapsed roads, and flooding.

[0796] Emotion Engine

[0797] When taking photos or videos, the device also captures the user's facial expressions and voice. The device's camera and microphone are activated to collect the user's facial and voice data.

[0798] The server sends this emotional data to an emotion engine (e.g., Microsoft Azure Emotion API), which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state. The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[0799] Data linkage and display stage

[0800] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[0801] The server sends the updated map data to the device. When the user opens the application, the latest map information is displayed. The server generates the latest map data and sends it to the device in a data packet.

[0802] User Support Stage

[0803] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[0804] The device receives the alert message and displays a notification to the user. The user can check the notification and consider what action is necessary. The device displays the received alert message as a pop-up notification, prompting the user to take action.

[0805] Specific examples

[0806] Examples of hiking trails

[0807] User A takes a photo on the hiking trail on Mount Takao and uploads it through the app. The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server. The server receives the data and analyzes the photo and the user's emotional state using AI analysis means and an emotion engine. The AI ​​recognizes that the hiking trail is covered in snow and generates a result saying "impassable." The emotion engine recognizes the user's impatience from their facial expression and generates a message saying "Please stay calm. Please bring snow gear." The server updates the map data based on the analysis results and sets the target area to be displayed in red. The device displays the latest map data and notifies the user with a customized alert message.

[0808] Example of a disaster

[0809] User B takes a photo of the road after the earthquake and uploads it through the app. The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server. The server receives the data and analyzes the photo and the user's emotional state using AI analysis means and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying "Please remain calm. Please select an alternative route." The server updates the map data based on the analysis results and sets the affected area to be displayed in red. The device displays the latest map data and notifies the user with a customized alert message.

[0810] Prompt Sentence Examples

[0811] "Uploading the status of the hiking trails on Mount Takao. Current location is latitude 35.625, longitude 139.243. Snow is visible in the photo. I'm getting anxious."

[0812] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0813] System processing flow

[0814] Step 1:

[0815] Users take photos and videos using their smartphones or dedicated devices.

[0816] Input: Photos and videos taken by the user.

[0817] Output: Captured photo and video files.

[0818] Specific actions: The user opens the camera app on their smartphone, frames the subject, and presses the capture button.

[0819] Step 2:

[0820] The device uses the GPS module to obtain location information (latitude and longitude) and tag photos and videos.

[0821] Input: Photos and videos taken, location data from the GPS module.

[0822] Output: Location-tagged photo and video files.

[0823] How it works: The device's GPS sensor measures your current location and adds the acquired latitude and longitude as metadata to photo and video files.

[0824] Step 3:

[0825] The device sends photos, videos, and location information to a server via the Internet, and the user presses the "upload" button within the application.

[0826] Input: Location-tagged photo and video files.

[0827] Output: Data packets sent to the server.

[0828] Specific operation: The user presses the "upload" button in the application, and the device sends a data packet to the server via the Internet.

[0829] Step 4:

[0830] The server receives data packets sent from the device, analyzes the photos, videos, and location information, and stores them in a database.

[0831] Input: The data packet sent to the server.

[0832] Output: Photos, videos, and location information stored in a database.

[0833] Specific operation: The server listens for data on the receiving port, extracts the received data from the packets, and stores the photos, videos, and location information in a database.

[0834] Step 5:

[0835] The server inputs the stored photos and videos into an AI analysis module, which analyzes the elements within the images.

[0836] Input: Photos and videos stored in the database.

[0837] Output: Analysis results for each image.

[0838] Specific operation: The server inputs the image data into an AI analysis module (e.g., TensorFlow, OpenCV) and obtains the analysis results. The AI ​​recognizes situations such as fallen trees, snow, collapsed roads, and flooding.

[0839] Step 6:

[0840] The device also captures the user's facial expressions and voice when taking photos and videos.

[0841] Input: The user's facial expressions and voice at the time of capture.

[0842] Output: User's facial and voice data.

[0843] Specific operation: The device's camera and microphone are activated to collect the user's facial and voice data.

[0844] Step 7:

[0845] The server sends the user's facial expression and voice data to the emotion engine, which analyzes the user's emotional state.

[0846] Input: User facial and voice data.

[0847] Output: Parsed emotional state of the user.

[0848] Specific operation: The server inputs the received data into an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotional state.

[0849] Step 8:

[0850] The server integrates the analysis results of the AI ​​analysis means and emotion engine with the location information and updates the map data.

[0851] Input: Analysis results of AI analysis method and emotion engine, and location information.

[0852] Output: Updated map data.

[0853] Specific operation: The server integrates the analysis results and location information into a map database, color-coding the relevant area, and updating it.

[0854] Step 9:

[0855] The server sends the updated map data to the terminal, and the latest map information is displayed when the user opens the application.

[0856] Input: Updated map data.

[0857] Output: The latest map data sent to the device.

[0858] Specific operation: The server generates the latest map data and sends it to the device as a data packet.

[0859] Step 10:

[0860] The server generates a customized alert message based on the analysis results and sends it to the user.

[0861] Input: Analysis results of AI analysis method and emotion engine.

[0862] Output: The alert message sent to the user.

[0863] Specific operation: The server generates an alert message based on the analysis results and sends it to the terminal as a data packet.

[0864] Step 11:

[0865] The device receives the alert message and displays a notification to the user, who then checks the notification and considers what action to take.

[0866] Input: The alert message sent by the server.

[0867] Output: The alert message that will be displayed to the user.

[0868] Specific operation: The device displays the received alert message as a pop-up notification, prompting the user to take action.

[0869] (Application example 2)

[0870] 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."

[0871] Conventional road condition monitoring systems and hiking trail information systems have difficulty accurately grasping local conditions in real time. Furthermore, they are unable to provide information that takes into account the user's psychological state, making it difficult to support appropriate responses in emergencies or stressful situations. This presents a problem of a lack of support for users to make calm decisions on their own.

[0872] 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.

[0873] In this invention, the server includes means for uploading photos and videos taken by the user via the Internet, means for acquiring current location information using a GPS module, means for transmitting the taken photos and videos and the location information to the server, AI means for analyzing the received photos, videos, and location information, means including an emotion engine for capturing the user's facial expressions and voice and analyzing their emotional state, means for updating map data based on the information analyzed by the AI ​​means and the analysis results of the emotion engine, and means for displaying the updated map data and a customized alert message on the user terminal. This makes it possible to grasp the local situation in real time and provide an appropriate alert message based on the user's emotional state.

[0874] A "User" is any person or entity that utilizes the system to take photos and videos and upload the data over the Internet.

[0875] "Photos and videos" are still images and videos taken by users to record local conditions and upload them to the system.

[0876] The "Internet" is a global network for transmitting and receiving data between user terminals and servers.

[0877] A "GPS module" is a device that measures the current location of a user terminal and acquires latitude and longitude information.

[0878] A "server" is a centralized computer system that receives, processes, and analyzes data sent from user terminals over the Internet.

[0879] "AI means" is an artificial intelligence system that analyzes road conditions and hiking trail conditions from photos and videos, determines passability and necessary equipment, and generates customized alert messages based on the user's emotional state.

[0880] The "emotion engine" is a system that captures the user's facial expressions and voice and analyzes their emotional state.

[0881] "Map data" refers to geographical information data that is updated based on location information obtained from users and the results of analysis of photos and videos.

[0882] An "alert message" is a warning or advice message that is generated based on the analysis results and the user's emotional state.

[0883] A "user terminal" is an electronic device such as a smartphone or dedicated terminal that a user uses to access the system.

[0884] MODE FOR CARRYING OUT THE INVENTION

[0885] In order to implement the present invention, the following hardware and software are used.

[0886] Hardware

[0887] 1. Smartphone or dedicated device: Equipped with a camera function and a GPS module.

[0888] 2. Server: A centralized computer system with a powerful processor and large amount of storage.

[0889] software

[0890] 1. AI analysis module: An image analysis system that uses machine learning libraries such as TensorFlow and PyTorch.

[0891] 2. Emotion recognition engine: The EmotionRecognizer library combines facial expression recognition technology and voice analysis technology.

[0892] 3. Data communication libraries: Software for sending and receiving data over the Internet, such as the requests library.

[0893] System Flow

[0894] 1. Data Collection Stage

[0895] Users use their smartphones or dedicated devices to take photos and videos of local roads and hiking trails.

[0896] The device uses a GPS module to obtain location information and tag photos and videos.

[0897] These data are sent to a server over the Internet, where the user presses the "upload" button in the application.

[0898] 2. Data Reception and Analysis Stage

[0899] The server receives the data sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[0900] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[0901] 3. Emotion Engine

[0902] The device also captures the user's facial expressions and voice when taking photos and videos.

[0903] The server sends this emotional data to the emotion engine, which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state.

[0904] The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[0905] 4. Data linkage and display stage

[0906] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[0907] The server sends the updated map data to the user's device, and when the user opens the application, the latest map information is displayed.

[0908] 5. User Support Stage

[0909] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[0910] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action.

[0911] Specific examples

[0912] Examples of hiking trails

[0913] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[0914] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[0915] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the mountain trail is covered in snow and generates a result of "impassable." The emotion engine recognizes the impatience in the user's facial expression and generates a message saying, "Please stay calm. Please bring snow gear."

[0916] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0917] The device displays the latest map data and notifies the user with customized alert messages.

[0918] Example of a disaster

[0919] User B takes a photo of the road after the earthquake and uploads it through the app.

[0920] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[0921] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying, "Please remain calm. Please choose another route."

[0922] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[0923] The device displays the latest map data and notifies the user with customized alert messages.

[0924] Prompt Sentence Examples

[0925] "The map should reflect the latest road conditions based on photos taken by the user. The alert message should also take into account the user's emotional state."

[0926] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0927] Step 1:

[0928] Users use their smartphones or dedicated devices to take photos and videos of local roads and hiking trails.

[0929] Input: Photos and videos taken

[0930] Output: None (Photos and videos are saved on the device)

[0931] Specific actions: The user launches the device's camera app and presses the capture button.

[0932] Step 2:

[0933] The device uses a GPS module to obtain location information of the current location.

[0934] Input: GPS module of the device

[0935] Output: Current location (latitude and longitude)

[0936] Specific operation: The device's GPS function will automatically activate and obtain the current latitude and longitude.

[0937] Step 3:

[0938] The user presses the "upload" button in the application to send the photos and videos they have taken and their location information to the server.

[0939] Input: Photos, videos, location information

[0940] Output: Send data to the server

[0941] What happens: The user presses the upload button in the application, and the data is sent over the Internet to the server.

[0942] Step 4:

[0943] The server stores the received photos, videos, and location information in a database for analysis.

[0944] Input: Photos, videos, and location information sent

[0945] Output: Data stored in the database

[0946] Specific operation: The server writes the received data to the database.

[0947] Step 5:

[0948] The server inputs the stored photos and videos into an AI analysis module, which analyzes the elements within the images.

[0949] Input: Photos and videos stored in the database

[0950] Output: Analyzed image data (e.g., fallen trees, snow, collapsed roads, etc.)

[0951] Specific operation: The AI ​​analysis module analyzes the image and outputs the recognized elements.

[0952] Step 6:

[0953] The device captures the user's facial expressions and voice along with photos and videos and sends them to the server.

[0954] Input: User's facial expressions, voice, photos and videos

[0955] Output: Send emotion data to the server

[0956] Specific operation: The device's camera and microphone capture the user's facial expressions and voice, and the data is sent to the server.

[0957] Step 7:

[0958] The server inputs the emotion data into an emotion engine to analyze the user's emotional state.

[0959] Input: Emotion data

[0960] Output: Analyzed emotional state (e.g., impatience, anxiety, tension, etc.)

[0961] Specific operation: The emotion engine analyzes facial and vocal data to determine the emotional state.

[0962] Step 8:

[0963] The server integrates the analysis results with location information and updates the map data in real time.

[0964] Input: Image analysis results, location information, emotional state

[0965] Output: Updated map data

[0966] Specific operation: The server integrates the analysis results and location information into the map database and updates the map.

[0967] Step 9:

[0968] The server generates a customized alert message based on the map data.

[0969] Input: Emotional state, analysis results

[0970] Output: Customized alert message

[0971] Specific behavior: The server takes into account the emotional state and analysis results to generate an appropriate alert message to send to the user.

[0972] Step 10:

[0973] The server sends updated map data and customized alert messages to the user terminal.

[0974] Input: Updated map data, alert message

[0975] Output: Sending data to the user's terminal

[0976] Specific operation: The server sends map data and an alert message to the user's device.

[0977] Step 11:

[0978] The device displays the latest map data and notifies the user with customized alert messages.

[0979] Input: Updated map data, alert message

[0980] Output: User notification

[0981] Specific operation: The device displays map data and presents an alert message to the user as a notification.

[0982] 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.

[0983] 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.

[0984] 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.

[0985] [Third embodiment]

[0986] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0987] 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.

[0988] 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).

[0989] 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.

[0990] 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.

[0991] 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).

[0992] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0993] 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.

[0994] 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.

[0995] 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.

[0996] 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.

[0997] 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."

[0998] System Overview

[0999] This invention is a system that grasps the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflects this information in map data, allowing users to check the latest status and take prompt and appropriate action.

[1000] Data Collection Stage

[1001] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[1002] The device uses the GPS module to obtain the current location information (latitude and longitude) of the location where the photo or video was taken.

[1003] The device sends the captured photos and videos, as well as its current location information, to a server via the Internet.

[1004] Data reception and analysis stage

[1005] The server receives the data packets sent from the device and stores the photos, videos, and location information in a database.

[1006] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, such as fallen trees, snow, and collapsed roads.

[1007] AI analysis methods determine passability, required equipment, and congestion status. For example, if a trail is covered in snow, the results will be "Impassable" and "Required Equipment: Snow Gear."

[1008] Data linkage and display stage

[1009] The server integrates the analysis results returned by the AI ​​analysis means with the location information and updates the map data in real time.

[1010] The updated map data is color-coded and symbolized based on the condition; for example, impassable areas are displayed in red.

[1011] User Support Stage

[1012] The server generates an alert message based on the analysis results and sends it to the user. For example, if a road is collapsed, a message will be sent saying, "This road is currently impassable. Please choose an alternative route."

[1013] The device receives updates and alert messages from the server and notifies the user, who can then take appropriate action.

[1014] Specific examples

[1015] Examples of hiking trails

[1016] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[1017] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[1018] The server receives the data and analyzes the photos using AI analysis methods, which recognizes the trail as covered in snow and generates a result that it is "impassable."

[1019] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1020] The device displays the latest map data and notifies User A, "This hiking trail is currently impassable. Please bring snow gear."

[1021] Example of a disaster

[1022] User B takes a photo of the road after the earthquake and uploads it through the app.

[1023] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[1024] The server receives the data and analyzes the photos using AI analysis methods. The AI ​​recognizes that the road has collapsed and generates a result that says "impassable."

[1025] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1026] The device displays the latest map data and notifies User B, "This road is currently impassable. Please choose another route."

[1027] This allows users to obtain the latest information in real time and take prompt action, improving the accuracy of map data and user safety.

[1028] The processing flow will be explained below.

[1029] Step 1:

[1030] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[1031] Step 2:

[1032] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[1033] Step 3:

[1034] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[1035] Step 4:

[1036] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[1037] Step 5:

[1038] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements within the image. Specifically, the AI ​​scans the image and recognizes conditions such as fallen trees, snow, collapsed roads, and flooding.

[1039] Step 6:

[1040] AI analysis methods determine passability, required equipment, and congestion status. For example, if a trail is covered in snow, the results will be "Impassable" and "Required Equipment: Snow Gear."

[1041] Step 7:

[1042] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[1043] Step 8:

[1044] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[1045] Step 9:

[1046] The device displays the received map data and notifies the user of the current situation in real time, for example by displaying warning messages and icons on the screen.

[1047] Step 10:

[1048] The server generates and sends an alert message to the user based on the analysis results. In the event of an emergency, the alert message is sent to the user as a push notification.

[1049] Step 11:

[1050] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action. For example, the message might read, "This road is currently collapsed and impassable. Please choose an alternative route."

[1051] Example 1

[1052] 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."

[1053] In modern society, it is extremely important to quickly grasp natural disasters and sudden changes in road conditions and take appropriate measures. However, conventional systems have difficulty collecting information and updating map data in real time, which means that users have to spend a great deal of time and effort obtaining the latest information. In addition, determining whether a road is passable and what equipment is needed is also manual, so a highly accurate and rapid response is required.

[1054] 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.

[1055] In this invention, the server includes means for transferring image data captured by a user via a network, means for acquiring geographic information of the current location using a position detection device, means for transmitting the captured image data and the geographic information to a data processing device, machine learning means in the data processing device for analyzing the received image data and geographic information, means for updating map data based on the information analyzed by the machine learning means, and means for displaying the updated map data on a user terminal. This makes it possible to grasp road conditions and mountain trail conditions in real time and quickly and accurately determine passability and necessary equipment.

[1056] A "user" is an entity that uses the system to capture image data and transmits related information to the server.

[1057] "Image data" refers to digital data of photographs and videos taken by the user.

[1058] "Network" refers generally to a communications network, including the Internet, used to transmit and receive data.

[1059] A "location detection device" is a hardware and software device, including a GPS module, that detects the geographic information (latitude and longitude) of a current location.

[1060] "Geographic information" refers to data on the latitude and longitude of a specific location obtained by a location detection device.

[1061] A "data processing device" is a computer system, including a server, for processing and analyzing received data.

[1062] "Machine learning means" refers to means, including an AI analysis module, that analyzes received image data and geographic information and generates a specific judgment result.

[1063] "Map Data" refers to digital maps that are updated in real time and provide users with location information and road and trail conditions.

[1064] A "user terminal" is a device such as a smartphone or computer owned by a user, which displays data and messages sent from the server.

[1065] A "warning message" is a message that is generated based on the analysis results and provides specific attention or information to the user.

[1066] System Overview

[1067] This invention is a system that grasps the condition of roads, hiking trails, etc. in real time based on image data taken by the user and reflects this information in map data. This system allows the user to check the latest conditions and take prompt and appropriate action.

[1068] Hardware and software used

[1069] In implementing this system, the following hardware and software are used:

[1070] Smartphone or dedicated device (user device)

[1071] GPS module (position detection device)

[1072] Internet communication function (network)

[1073] Server (data processing device)

[1074] AI analysis module (machine learning method)

[1075] Map display software (map data and display means)

[1076] Data collection and transmission

[1077] The user uses a smartphone or dedicated device to take photos and videos (image data) of the road or hiking trail of interest. For example, a photo of a fallen tree may be taken while hiking. The device uses its built-in GPS module to obtain the latitude and longitude of the location where the photo was taken. For example, the device obtains information such as "latitude 35.625, longitude 139.243."

[1078] Next, the device sends the acquired image data and location information (geographical information) to a server via the Internet. This data transmission uses the HTTPS protocol to ensure data security.

[1079] Receiving and analyzing data

[1080] The server receives data packets sent from the device. The received photos, videos, and location information are first stored in a database. The server then inputs the stored photos and videos into an AI analysis module.

[1081] The AI ​​analysis module analyzes elements in the image (e.g., fallen trees, snow, collapsed roads, etc.) to determine passability, required equipment, and congestion. For example, if a hiking trail is covered in snow, the analysis results will be "impassable" and "Required equipment: snow gear."

[1082] Data integration and map updates

[1083] The server integrates the analysis results returned by the AI ​​analysis tool with the location information. For example, if the location at latitude 35.625, longitude 139.243 is determined to be impassable, the map data is updated to show that location in red. This ensures that the map data viewed by the user is always up to date.

[1084] User Notification and Assistance

[1085] The server generates an alert message based on the analysis results. For example, it generates a message saying, "This hiking trail is currently impassable. Please bring snow gear." This alert message is then sent to the user's device via the network. The device receives this message and immediately notifies the user, who can then take appropriate action.

[1086] Specific examples

[1087] Examples of hiking trails

[1088] User A takes a photo on the hiking trail on Mount Takao and uploads it through the app. The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server. The server receives the data and analyzes the photo using AI analysis. The AI ​​recognizes that the hiking trail is covered in snow and generates a result that says "impassable." The server updates the map data based on the analysis results and sets the target area to be displayed in red. The device displays the latest map data and notifies User A, "This hiking trail is currently impassable. Please bring snow gear."

[1089] Example of a disaster

[1090] User B takes a photo of the road after the earthquake and uploads it through the app. The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server. The server receives the data and analyzes the photo using AI analysis. The AI ​​recognizes that the road has collapsed and generates a result that says "impassable." The server updates the map data based on the analysis results and sets the affected area to be displayed in red. The device displays the latest map data and notifies User B, "This road is currently impassable. Please select another route."

[1091] Prompt Sentence Examples

[1092] "Analyze the condition of the hiking trail in this photo and determine the necessary equipment and passability."

[1093] "Please analyze the photo of this road and let me know the current traffic situation."

[1094] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1095] Step 1: Data collection

[1096] The user uses a smartphone or dedicated device to take photos and videos of the road or hiking trail of interest. The input data is the captured image data, and location information (latitude and longitude) is obtained using a GPS module. This location information becomes additional input data. Specifically, the user operates a camera app to take a photo. The device receives the GPS signal and adds the obtained location information to the image data.

[1097] Step 2: Send data

[1098] The terminal assembles the acquired image data and location information into packets and sends them to a server via the Internet. The input data is a packet containing the image data and location information, and the output data is the packet sent to the server. Specifically, the terminal uses an internal communication module to send data to the server securely and quickly using the HTTPS protocol.

[1099] Step 3: Receiving data

[1100] The server receives data packets sent from the device. The input data is image data and location information in packet format, and the output data is the storage of the received data in a repository for analysis. Specifically, the server receives data via a specific API endpoint and stores the received image data and location information in a database.

[1101] Step 4: Data analysis

[1102] The server inputs the stored image data and location information into the AI ​​analysis module. The input data is the image data and location information, and the output data is the analysis results. Specifically, the AI ​​analysis module analyzes elements in the image (e.g., fallen trees, snow, collapsed roads, etc.) and determines whether the path is passable, what equipment is required, and how crowded the path is. For example, the AI ​​recognizes that a hiking trail is covered in snow and generates the results "impassable" and "Required equipment: snow gear."

[1103] Step 5: Data Integration

[1104] The server integrates the analysis results and location information returned from the AI ​​analysis means. The input data are the analysis results and location information, and the output data is updated map data. Specifically, the server reflects the analysis results in the map data, making it possible to display the relevant points in a new state. For example, it can be set to display impassable points in red.

[1105] Step 6: Data Distribution

[1106] The server generates an alert message based on the analysis results and sends it to the user's terminal. The input data is the analysis results, and the output data is the generated alert message and its delivery to the user's terminal. Specifically, the server generates an appropriate warning message from the analysis results and sends it to the user's terminal via the network.

[1107] Step 7: User Notification

[1108] The terminal notifies the user of the alert message received from the server. The input data is the alert message, and the output data is the notification to the user. Specifically, the terminal displays the received message as a pop-up on the screen to immediately alert the user. The user can then take appropriate action based on this.

[1109] (Application example 1)

[1110] 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."

[1111] Conventional self-driving vehicles have had difficulty grasping road conditions in real time and immediately sharing appropriate information with other vehicles. As a result, they have been unable to respond quickly to emergencies such as road obstacles and accidents, which has led to problems such as reduced driving safety and efficiency.

[1112] 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.

[1113] In this invention, the server includes means for uploading photos and videos taken by the user via the Internet, means for acquiring current location information using a GPS module, means for transmitting the taken photos and videos and the location information to the server, artificial intelligence means for analyzing the received photos, videos and location information in the server, means for updating map data in real time based on the information analyzed by the artificial intelligence means and sending alerts to other autonomous vehicles, and means for displaying the updated map data on the user terminal, thereby enabling road obstacles and emergency situations to be quickly detected and information to be shared with other autonomous vehicles in real time.

[1114] A "User" is a person or organization that uses the System to take photos and videos and upload them via the Internet.

[1115] A "GPS module" is a device for obtaining geographical location information, and is used to obtain data such as latitude and longitude.

[1116] A "server" is a computer system that receives and processes data, stores photos, videos, and location information, and analyzes them using artificial intelligence.

[1117] "Artificial intelligence means" refers to technology that analyzes received photos and videos to determine road conditions, passability, and necessary equipment.

[1118] "Map data" refers to electronic data containing geographical information, and is used to visually display road conditions, passability, and the like.

[1119] "Updating means" refers to a device or system that has the function of updating map data in real time based on information analyzed by the artificial intelligence means.

[1120] "Means for sending alerts" is a function for sending emergency information or important notifications to other autonomous vehicles or users.

[1121] A "user terminal" is a device used by a user to receive and display information, such as a smartphone or tablet.

[1122] This system uses the camera and GPS module of the autonomous vehicle to grasp the current road conditions in real time and provides information to other autonomous vehicles and users based on the analysis results. The entire system consists of a terminal, a server, an artificial intelligence means, a map data update means, an alert sending means, and a user terminal.

[1123] program

[1124] The system's programming is structured as follows:

[1125] 1. An autonomous vehicle equipped with a camera and GPS module uses the camera to continuously take photos and videos of the road.

[1126] 2. The GPS module obtains the location information (latitude and longitude) of the current location where the photo or video was taken.

[1127] 3. The device sends the captured photos and videos, as well as location information, to a server via the Internet.

[1128] 4. The server stores the received photos, videos, and location information in a database.

[1129] 5. The server inputs the stored photos and videos into the artificial intelligence means, which analyzes elements within the images, including machine learning models to detect fallen trees, snow, and road collapses, for example.

[1130] 6. Based on the information analyzed by the artificial intelligence means, the map data update means updates the map data in real time. The updated map data is visually displayed, and impassable areas are indicated in red.

[1131] 7. The alert sending means sends the analysis results to other autonomous vehicles to alert them to road obstacles or emergency situations.

[1132] 8. The user device receives the updated map data and alert messages from the server and displays or notifies the user.

[1133] Hardware and Software

[1134] The hardware used is a camera and GPS module mounted on the autonomous vehicle, as well as a terminal with internet connectivity. The server is equipped with a database and artificial intelligence means, which are implemented using machine learning libraries such as Python and TensorFlow.

[1135] Specific examples

[1136] For example, while autonomous vehicle A is driving down a road, it takes pictures of a fallen tree on the road with its camera. These pictures, along with location information obtained by the GPS module, are sent to a server via the device. The server stores the received data and analyzes it using artificial intelligence means. When a fallen tree is detected, map data is updated in real time, and the affected area is highlighted in red. Furthermore, an alert is sent to other autonomous vehicles, allowing them to automatically change their routes to avoid the affected road. This improves the safety of autonomous vehicles overall.

[1137] Prompt Sentence Examples

[1138] "Please design a system that will analyze the current road conditions captured by my self-driving vehicle in real time and provide traffic updates. It will also use camera images and GPS data, perform AI analysis, and provide alerts to notify other vehicles."

[1139] The above is the mode for carrying out the invention in this system.

[1140] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1141] Step 1:

[1142] Taking photos and videos with the camera

[1143] Cameras in self-driving vehicles capture road conditions in real time.

[1144] Input: Road image

[1145] Output: Photos and videos taken

[1146] What it does: The camera periodically captures images and saves frames at regular intervals.

[1147] Step 2:

[1148] Obtaining location information using the GPS module

[1149] The GPS module installed in the autonomous vehicle acquires the latitude and longitude of its current location.

[1150] Input: Position data from satellites

[1151] Output: Current location latitude and longitude

[1152] How it works: The GPS module periodically communicates with satellites to update location information.

[1153] Step 3:

[1154] Sending data

[1155] The device sends the photos and videos taken and the acquired location information to a server via the Internet.

[1156] Input: photos, videos, location information

[1157] Output: Data sent to the server

[1158] Specific operation: The terminal packetizes the data and sends a POST request to the server using the HTTP protocol.

[1159] Step 4:

[1160] Receiving and storing data on the server

[1161] The server stores the received photos, videos, and location information in a database.

[1162] Input: photos, videos, location information

[1163] Output: Data recorded in the database

[1164] Specific operation: The server parses the received data and stores it in the corresponding database fields.

[1165] Step 5:

[1166] Data analysis by means of artificial intelligence

[1167] The server's artificial intelligence means analyzes the received photos and videos to determine the condition of the road and whether it is passable.

[1168] Input: photos, videos, location information

[1169] Output: Analysis results such as "fallen tree detection" and "passage impassable"

[1170] Specific operation: The artificial intelligence means analyzes the image using a machine learning model, extracts features, and generates a judgment result.

[1171] Step 6:

[1172] Real-time map data updates

[1173] The server updates the map data in real time based on the analysis results.

[1174] Input: Analysis results, location information

[1175] Output: Updated map data

[1176] Specific operation: The server reads map data from the database, corrects the relevant parts based on the analysis results, and re-saves them.

[1177] Step 7:

[1178] Sending alerts

[1179] Based on the analysis results, the server sends alerts to other self-driving vehicles and users as needed.

[1180] Input: Analysis results, location information

[1181] Output: Alert message sent

[1182] Specific operation: The server generates an alert message and sends it to other autonomous vehicles via means such as push notifications.

[1183] Step 8:

[1184] Displaying information on the user's device

[1185] The user terminal receives the latest map data and alert message and displays or notifies the user.

[1186] Input: Updated map data, alert message

[1187] Output: Map data and alert messages displayed to the user

[1188] Specific operation: The user terminal receives the data from the server and displays it to the user through the GUI.

[1189] Through these processing steps, autonomous vehicles can monitor road conditions in real time and quickly share information with other vehicles, greatly improving road safety and efficiency.

[1190] 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.

[1191] System Overview

[1192] This invention is a system that grasps the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflects this in map data. This system also incorporates an emotion engine that recognizes the user's emotions, and generates customized alert messages according to the user's situation, thereby providing more effective information provision and support for countermeasures.

[1193] Data Collection Stage

[1194] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[1195] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[1196] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[1197] Data reception and analysis stage

[1198] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[1199] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[1200] Emotion Engine

[1201] The device also captures the user's facial expressions and voice when taking photos and videos.

[1202] The server sends this emotional data to the emotion engine, which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state.

[1203] The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[1204] Data linkage and display stage

[1205] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[1206] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[1207] User Support Stage

[1208] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[1209] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action.

[1210] Specific examples

[1211] Examples of hiking trails

[1212] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[1213] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[1214] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the mountain trail is covered in snow and generates a result of "impassable." The emotion engine recognizes the impatience in the user's facial expression and generates a message saying, "Please stay calm. Please bring snow gear."

[1215] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1216] The device displays the latest map data and notifies the user with customized alert messages.

[1217] Example of a disaster

[1218] User B takes a photo of the road after the earthquake and uploads it through the app.

[1219] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[1220] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying, "Please remain calm. Please choose another route."

[1221] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1222] The device displays the latest map data and notifies the user with customized alert messages.

[1223] In this way, the system not only improves the accuracy of map data but also provides customized messages based on the user's emotional state, supporting safe and effective responses.

[1224] The processing flow will be explained below.

[1225] Step 1:

[1226] Users use their smartphones or dedicated devices to take photos and videos of roads and hiking trails.

[1227] Step 2:

[1228] The device uses a camera module to simultaneously capture the user's facial expressions and voice, and this data is used by the emotion engine.

[1229] Step 3:

[1230] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[1231] Step 4:

[1232] The device sends the captured photos and videos, as well as current location information, and data on the user's facial expressions and voice, to a server via the Internet.

[1233] Step 5:

[1234] The server receives data packets sent from the device, analyzes the received data, and stores photos, videos, location information, facial expressions, and voice data in a database.

[1235] Step 6:

[1236] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[1237] Step 7:

[1238] The server inputs facial and voice data into the emotion engine, which then analyzes the user's emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as "anxiety" or "impatience."

[1239] Step 8:

[1240] AI tools determine whether a path is passable, what equipment is needed, and how crowded it is. At the same time, the emotion engine combines the analyzed emotional data and generates a customized alert message based on the analysis results. For example, if the trail is covered in snow and the user is feeling anxious, the message generated is, "Please stay calm. Please bring snow gear."

[1241] Step 9:

[1242] The server integrates the analysis results returned by the AI ​​analysis means and emotion engine with the location information, and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[1243] Step 10:

[1244] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[1245] Step 11:

[1246] The device displays the received map data and notifies the user of the current situation in real time, for example by displaying warning messages and icons on the screen.

[1247] Step 12:

[1248] The server generates and sends an alert message to the user based on the analysis results. In the event of an emergency, the alert message is sent to the user as a push notification.

[1249] Step 13:

[1250] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action. For example, the message might read, "This road is currently collapsed and impassable. Please choose an alternative route."

[1251] Example 2

[1252] 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."

[1253] Conventional map data update systems lack the functionality to reflect user-provided information in real time, and are insufficient in providing safety information based on the user's emotional state, making it difficult to take effective measures to ensure the user's safety. There is a growing need for a system that can instantly reflect the situation the user faces and alert them to the appropriate equipment and routes, especially during mountain climbing and disasters.

[1254] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1255] In this invention, the server includes artificial intelligence means for analyzing received photos, videos, and location information, means for including in the server an emotion engine for analyzing the user's emotional state based on the analysis, and means for updating map data based on the information analyzed by the artificial intelligence means and the emotion engine. This makes it possible to analyze data captured by the user in real time, generate customized alert messages according to the user's emotional state, and instantly update the map data.

[1256] A "user" is an entity that uses the system to take photos and videos and obtain location information.

[1257] A "terminal" is a smartphone or dedicated device used by a user, which takes photos and videos, obtains location information using a GPS module, and transmits the data to a server via the Internet.

[1258] A "GPS module" is a receiving device for identifying a location on Earth and is a device used to obtain latitude and longitude information.

[1259] A "server" is a device or system that receives photos, videos, and location information sent from a terminal, analyzes and stores this data, updates map data, and generates alert messages for users.

[1260] "Artificial intelligence means" is a general term for algorithms and technologies that analyze photographs and videos, recognize the elements and situations contained therein, and generate analytical results.

[1261] The "emotion engine" is a system that uses facial expression recognition technology and voice analysis technology to analyze a user's facial expressions and voice and recognize their emotional state.

[1262] "Map data" refers to data that represents geographical information, and is updated based on content including the status of roads, hiking trails, ports, houses, and the like.

[1263] An "alert message" is a notification message generated based on the analysis results and the user's emotional state, and encourages the user to take appropriate action.

[1264] System Overview

[1265] This system aims to grasp the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflect them in map data. It also incorporates an emotion engine that recognizes the user's emotions and generates customized alert messages according to the user's emotional state. This allows for more effective provision of information and support for countermeasures to users.

[1266] Data Collection Stage

[1267] The user uses a smartphone or dedicated device to take photos and videos of the road or hiking trail in question by pressing the camera button on the smartphone's camera application.

[1268] The device uses the GPS module to obtain location information (latitude and longitude) of the current location where the photo or video was taken. The obtained location information is tagged with the photo or video. Specifically, the GPS sensor in the device measures the current location, and the location information is added to the metadata based on this.

[1269] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[1270] Data reception and analysis stage

[1271] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database. The server listens for data on the receiving port, analyzes the data packets for photos, videos, and location information, and stores them in a database.

[1272] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the image. Specifically, the AI ​​module (e.g., TensorFlow, OpenCV) recognizes situations such as fallen trees, snow, collapsed roads, and flooding.

[1273] Emotion Engine

[1274] When taking photos or videos, the device also captures the user's facial expressions and voice. The device's camera and microphone are activated to collect the user's facial and voice data.

[1275] The server sends this emotional data to an emotion engine (e.g., Microsoft Azure Emotion API), which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state. The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[1276] Data linkage and display stage

[1277] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[1278] The server sends the updated map data to the device. When the user opens the application, the latest map information is displayed. The server generates the latest map data and sends it to the device in a data packet.

[1279] User Support Stage

[1280] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[1281] The device receives the alert message and displays a notification to the user. The user can check the notification and consider what action is necessary. The device displays the received alert message as a pop-up notification, prompting the user to take action.

[1282] Specific examples

[1283] Examples of hiking trails

[1284] User A takes a photo on the hiking trail on Mount Takao and uploads it through the app. The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server. The server receives the data and analyzes the photo and the user's emotional state using AI analysis means and an emotion engine. The AI ​​recognizes that the hiking trail is covered in snow and generates a result saying "impassable." The emotion engine recognizes the user's impatience from their facial expression and generates a message saying "Please stay calm. Please bring snow gear." The server updates the map data based on the analysis results and sets the target area to be displayed in red. The device displays the latest map data and notifies the user with a customized alert message.

[1285] Example of a disaster

[1286] User B takes a photo of the road after the earthquake and uploads it through the app. The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server. The server receives the data and analyzes the photo and the user's emotional state using AI analysis means and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying "Please remain calm. Please select an alternative route." The server updates the map data based on the analysis results and sets the affected area to be displayed in red. The device displays the latest map data and notifies the user with a customized alert message.

[1287] Prompt Sentence Examples

[1288] "Uploading the status of the hiking trails on Mount Takao. Current location is latitude 35.625, longitude 139.243. Snow is visible in the photo. I'm getting anxious."

[1289] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1290] System processing flow

[1291] Step 1:

[1292] Users take photos and videos using their smartphones or dedicated devices.

[1293] Input: Photos and videos taken by the user.

[1294] Output: Captured photo and video files.

[1295] Specific actions: The user opens the camera app on their smartphone, frames the subject, and presses the capture button.

[1296] Step 2:

[1297] The device uses the GPS module to obtain location information (latitude and longitude) and tag photos and videos.

[1298] Input: Photos and videos taken, location data from the GPS module.

[1299] Output: Location-tagged photo and video files.

[1300] How it works: The device's GPS sensor measures your current location and adds the acquired latitude and longitude as metadata to photo and video files.

[1301] Step 3:

[1302] The device sends photos, videos, and location information to a server via the Internet, and the user presses the "upload" button within the application.

[1303] Input: Location-tagged photo and video files.

[1304] Output: Data packets sent to the server.

[1305] Specific operation: The user presses the "upload" button in the application, and the device sends a data packet to the server via the Internet.

[1306] Step 4:

[1307] The server receives data packets sent from the device, analyzes the photos, videos, and location information, and stores them in a database.

[1308] Input: The data packet sent to the server.

[1309] Output: Photos, videos, and location information stored in a database.

[1310] Specific operation: The server listens for data on the receiving port, extracts the received data from the packets, and stores the photos, videos, and location information in a database.

[1311] Step 5:

[1312] The server inputs the stored photos and videos into an AI analysis module, which analyzes the elements within the images.

[1313] Input: Photos and videos stored in the database.

[1314] Output: Analysis results for each image.

[1315] Specific operation: The server inputs the image data into an AI analysis module (e.g., TensorFlow, OpenCV) and obtains the analysis results. The AI ​​recognizes situations such as fallen trees, snow, collapsed roads, and flooding.

[1316] Step 6:

[1317] The device also captures the user's facial expressions and voice when taking photos and videos.

[1318] Input: The user's facial expressions and voice at the time of capture.

[1319] Output: User's facial and voice data.

[1320] Specific operation: The device's camera and microphone are activated to collect the user's facial and voice data.

[1321] Step 7:

[1322] The server sends the user's facial expression and voice data to the emotion engine, which analyzes the user's emotional state.

[1323] Input: User facial and voice data.

[1324] Output: Parsed emotional state of the user.

[1325] Specific operation: The server inputs the received data into an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotional state.

[1326] Step 8:

[1327] The server integrates the analysis results of the AI ​​analysis means and emotion engine with the location information and updates the map data.

[1328] Input: Analysis results of AI analysis method and emotion engine, and location information.

[1329] Output: Updated map data.

[1330] Specific operation: The server integrates the analysis results and location information into a map database, color-coding the relevant area, and updating it.

[1331] Step 9:

[1332] The server sends the updated map data to the terminal, and the latest map information is displayed when the user opens the application.

[1333] Input: Updated map data.

[1334] Output: The latest map data sent to the device.

[1335] Specific operation: The server generates the latest map data and sends it to the device as a data packet.

[1336] Step 10:

[1337] The server generates a customized alert message based on the analysis results and sends it to the user.

[1338] Input: Analysis results of AI analysis method and emotion engine.

[1339] Output: The alert message sent to the user.

[1340] Specific operation: The server generates an alert message based on the analysis results and sends it to the terminal as a data packet.

[1341] Step 11:

[1342] The device receives the alert message and displays a notification to the user, who then checks the notification and considers what action to take.

[1343] Input: The alert message sent by the server.

[1344] Output: The alert message that will be displayed to the user.

[1345] Specific operation: The device displays the received alert message as a pop-up notification, prompting the user to take action.

[1346] (Application example 2)

[1347] 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."

[1348] Conventional road condition monitoring systems and hiking trail information systems have difficulty accurately grasping local conditions in real time. Furthermore, they are unable to provide information that takes into account the user's psychological state, making it difficult to support appropriate responses in emergencies or stressful situations. This presents a problem of a lack of support for users to make calm decisions on their own.

[1349] 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.

[1350] In this invention, the server includes means for uploading photos and videos taken by the user via the Internet, means for acquiring current location information using a GPS module, means for transmitting the taken photos and videos and the location information to the server, AI means for analyzing the received photos, videos, and location information, means including an emotion engine for capturing the user's facial expressions and voice and analyzing their emotional state, means for updating map data based on the information analyzed by the AI ​​means and the analysis results of the emotion engine, and means for displaying the updated map data and a customized alert message on the user terminal. This makes it possible to grasp the local situation in real time and provide an appropriate alert message based on the user's emotional state.

[1351] A "User" is any person or entity that utilizes the system to take photos and videos and upload the data over the Internet.

[1352] "Photos and videos" are still images and videos taken by users to record local conditions and upload them to the system.

[1353] The "Internet" is a global network for transmitting and receiving data between user terminals and servers.

[1354] A "GPS module" is a device that measures the current location of a user terminal and acquires latitude and longitude information.

[1355] A "server" is a centralized computer system that receives, processes, and analyzes data sent from user terminals over the Internet.

[1356] "AI means" is an artificial intelligence system that analyzes road conditions and hiking trail conditions from photos and videos, determines passability and necessary equipment, and generates customized alert messages based on the user's emotional state.

[1357] The "emotion engine" is a system that captures the user's facial expressions and voice and analyzes their emotional state.

[1358] "Map data" refers to geographical information data that is updated based on location information obtained from users and the results of analysis of photos and videos.

[1359] An "alert message" is a warning or advice message that is generated based on the analysis results and the user's emotional state.

[1360] A "user terminal" is an electronic device such as a smartphone or dedicated terminal that a user uses to access the system.

[1361] MODE FOR CARRYING OUT THE INVENTION

[1362] In order to implement the present invention, the following hardware and software are used.

[1363] Hardware

[1364] 1. Smartphone or dedicated device: Equipped with a camera function and a GPS module.

[1365] 2. Server: A centralized computer system with a powerful processor and large amount of storage.

[1366] software

[1367] 1. AI analysis module: An image analysis system that uses machine learning libraries such as TensorFlow and PyTorch.

[1368] 2. Emotion recognition engine: The EmotionRecognizer library combines facial expression recognition technology and voice analysis technology.

[1369] 3. Data communication libraries: Software for sending and receiving data over the Internet, such as the requests library.

[1370] System Flow

[1371] 1. Data Collection Stage

[1372] Users use their smartphones or dedicated devices to take photos and videos of local roads and hiking trails.

[1373] The device uses a GPS module to obtain location information and tag photos and videos.

[1374] These data are sent to a server over the Internet, where the user presses the "upload" button in the application.

[1375] 2. Data Reception and Analysis Stage

[1376] The server receives the data sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[1377] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[1378] 3. Emotion Engine

[1379] The device also captures the user's facial expressions and voice when taking photos and videos.

[1380] The server sends this emotional data to the emotion engine, which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state.

[1381] The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[1382] 4. Data linkage and display stage

[1383] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[1384] The server sends the updated map data to the user's device, and when the user opens the application, the latest map information is displayed.

[1385] 5. User Support Stage

[1386] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[1387] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action.

[1388] Specific examples

[1389] Examples of hiking trails

[1390] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[1391] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[1392] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the mountain trail is covered in snow and generates a result of "impassable." The emotion engine recognizes the impatience in the user's facial expression and generates a message saying, "Please stay calm. Please bring snow gear."

[1393] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1394] The device displays the latest map data and notifies the user with customized alert messages.

[1395] Example of a disaster

[1396] User B takes a photo of the road after the earthquake and uploads it through the app.

[1397] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[1398] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying, "Please remain calm. Please choose another route."

[1399] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1400] The device displays the latest map data and notifies the user with customized alert messages.

[1401] Prompt Sentence Examples

[1402] "The map should reflect the latest road conditions based on photos taken by the user. The alert message should also take into account the user's emotional state."

[1403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1404] Step 1:

[1405] Users use their smartphones or dedicated devices to take photos and videos of local roads and hiking trails.

[1406] Input: Photos and videos taken

[1407] Output: None (Photos and videos are saved on the device)

[1408] Specific actions: The user launches the device's camera app and presses the capture button.

[1409] Step 2:

[1410] The device uses a GPS module to obtain location information of the current location.

[1411] Input: GPS module of the device

[1412] Output: Current location (latitude and longitude)

[1413] Specific operation: The device's GPS function will automatically activate and obtain the current latitude and longitude.

[1414] Step 3:

[1415] The user presses the "upload" button in the application to send the photos and videos they have taken and their location information to the server.

[1416] Input: Photos, videos, location information

[1417] Output: Send data to the server

[1418] What happens: The user presses the upload button in the application, and the data is sent over the Internet to the server.

[1419] Step 4:

[1420] The server stores the received photos, videos, and location information in a database for analysis.

[1421] Input: Photos, videos, and location information sent

[1422] Output: Data stored in the database

[1423] Specific operation: The server writes the received data to the database.

[1424] Step 5:

[1425] The server inputs the stored photos and videos into an AI analysis module, which analyzes the elements within the images.

[1426] Input: Photos and videos stored in the database

[1427] Output: Analyzed image data (e.g., fallen trees, snow, collapsed roads, etc.)

[1428] Specific operation: The AI ​​analysis module analyzes the image and outputs the recognized elements.

[1429] Step 6:

[1430] The device captures the user's facial expressions and voice along with photos and videos and sends them to the server.

[1431] Input: User's facial expressions, voice, photos and videos

[1432] Output: Send emotion data to the server

[1433] Specific operation: The device's camera and microphone capture the user's facial expressions and voice, and the data is sent to the server.

[1434] Step 7:

[1435] The server inputs the emotion data into an emotion engine to analyze the user's emotional state.

[1436] Input: Emotion data

[1437] Output: Analyzed emotional state (e.g., impatience, anxiety, tension, etc.)

[1438] Specific operation: The emotion engine analyzes facial and vocal data to determine the emotional state.

[1439] Step 8:

[1440] The server integrates the analysis results with location information and updates the map data in real time.

[1441] Input: Image analysis results, location information, emotional state

[1442] Output: Updated map data

[1443] Specific operation: The server integrates the analysis results and location information into the map database and updates the map.

[1444] Step 9:

[1445] The server generates a customized alert message based on the map data.

[1446] Input: Emotional state, analysis results

[1447] Output: Customized alert message

[1448] Specific behavior: The server takes into account the emotional state and analysis results to generate an appropriate alert message to send to the user.

[1449] Step 10:

[1450] The server sends updated map data and customized alert messages to the user terminal.

[1451] Input: Updated map data, alert message

[1452] Output: Sending data to the user's terminal

[1453] Specific operation: The server sends map data and an alert message to the user's device.

[1454] Step 11:

[1455] The device displays the latest map data and notifies the user with customized alert messages.

[1456] Input: Updated map data, alert message

[1457] Output: User notification

[1458] Specific operation: The device displays map data and presents an alert message to the user as a notification.

[1459] 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.

[1460] 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.

[1461] 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.

[1462] [Fourth embodiment]

[1463] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1464] 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.

[1465] 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).

[1466] 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.

[1467] 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.

[1468] 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).

[1469] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] 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."

[1476] System Overview

[1477] This invention is a system that grasps the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflects this information in map data, allowing users to check the latest status and take prompt and appropriate action.

[1478] Data Collection Stage

[1479] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[1480] The device uses the GPS module to obtain the current location information (latitude and longitude) of the location where the photo or video was taken.

[1481] The device sends the captured photos and videos, as well as its current location information, to a server via the Internet.

[1482] Data reception and analysis stage

[1483] The server receives the data packets sent from the device and stores the photos, videos, and location information in a database.

[1484] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, such as fallen trees, snow, and collapsed roads.

[1485] AI analysis methods determine passability, required equipment, and congestion status. For example, if a trail is covered in snow, the results will be "Impassable" and "Required Equipment: Snow Gear."

[1486] Data linkage and display stage

[1487] The server integrates the analysis results returned by the AI ​​analysis means with the location information and updates the map data in real time.

[1488] The updated map data is color-coded and symbolized based on the condition; for example, impassable areas are displayed in red.

[1489] User Support Stage

[1490] The server generates an alert message based on the analysis results and sends it to the user. For example, if a road is collapsed, a message will be sent saying, "This road is currently impassable. Please choose an alternative route."

[1491] The device receives updates and alert messages from the server and notifies the user, who can then take appropriate action.

[1492] Specific examples

[1493] Examples of hiking trails

[1494] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[1495] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[1496] The server receives the data and analyzes the photos using AI analysis methods, which recognizes the trail as covered in snow and generates a result that it is "impassable."

[1497] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1498] The device displays the latest map data and notifies User A, "This hiking trail is currently impassable. Please bring snow gear."

[1499] Example of a disaster

[1500] User B takes a photo of the road after the earthquake and uploads it through the app.

[1501] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[1502] The server receives the data and analyzes the photos using AI analysis methods. The AI ​​recognizes that the road has collapsed and generates a result that says "impassable."

[1503] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1504] The device displays the latest map data and notifies User B, "This road is currently impassable. Please choose another route."

[1505] This allows users to obtain the latest information in real time and take prompt action, improving the accuracy of map data and user safety.

[1506] The processing flow will be explained below.

[1507] Step 1:

[1508] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[1509] Step 2:

[1510] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[1511] Step 3:

[1512] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[1513] Step 4:

[1514] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[1515] Step 5:

[1516] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements within the image. Specifically, the AI ​​scans the image and recognizes conditions such as fallen trees, snow, collapsed roads, and flooding.

[1517] Step 6:

[1518] AI analysis methods determine passability, required equipment, and congestion status. For example, if a trail is covered in snow, the results will be "Impassable" and "Required Equipment: Snow Gear."

[1519] Step 7:

[1520] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[1521] Step 8:

[1522] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[1523] Step 9:

[1524] The device displays the received map data and notifies the user of the current situation in real time, for example by displaying warning messages and icons on the screen.

[1525] Step 10:

[1526] The server generates and sends an alert message to the user based on the analysis results. In the event of an emergency, the alert message is sent to the user as a push notification.

[1527] Step 11:

[1528] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action. For example, the message might read, "This road is currently collapsed and impassable. Please choose an alternative route."

[1529] Example 1

[1530] 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."

[1531] In modern society, it is extremely important to quickly grasp natural disasters and sudden changes in road conditions and take appropriate measures. However, conventional systems have difficulty collecting information and updating map data in real time, which means that users have to spend a great deal of time and effort obtaining the latest information. In addition, determining whether a road is passable and what equipment is needed is also manual, so a highly accurate and rapid response is required.

[1532] 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.

[1533] In this invention, the server includes means for transferring image data captured by a user via a network, means for acquiring geographic information of the current location using a position detection device, means for transmitting the captured image data and the geographic information to a data processing device, machine learning means in the data processing device for analyzing the received image data and geographic information, means for updating map data based on the information analyzed by the machine learning means, and means for displaying the updated map data on a user terminal. This makes it possible to grasp road conditions and mountain trail conditions in real time and quickly and accurately determine passability and necessary equipment.

[1534] A "user" is an entity that uses the system to capture image data and transmits related information to the server.

[1535] "Image data" refers to digital data of photographs and videos taken by the user.

[1536] "Network" refers generally to a communications network, including the Internet, used to transmit and receive data.

[1537] A "location detection device" is a hardware and software device, including a GPS module, that detects the geographic information (latitude and longitude) of a current location.

[1538] "Geographic information" refers to data on the latitude and longitude of a specific location obtained by a location detection device.

[1539] A "data processing device" is a computer system, including a server, for processing and analyzing received data.

[1540] "Machine learning means" refers to means, including an AI analysis module, that analyzes received image data and geographic information and generates a specific judgment result.

[1541] "Map Data" refers to digital maps that are updated in real time and provide users with location information and road and trail conditions.

[1542] A "user terminal" is a device such as a smartphone or computer owned by a user, which displays data and messages sent from the server.

[1543] A "warning message" is a message that is generated based on the analysis results and provides specific attention or information to the user.

[1544] System Overview

[1545] This invention is a system that grasps the condition of roads, hiking trails, etc. in real time based on image data taken by the user and reflects this information in map data. This system allows the user to check the latest conditions and take prompt and appropriate action.

[1546] Hardware and software used

[1547] In implementing this system, the following hardware and software are used:

[1548] Smartphone or dedicated device (user device)

[1549] GPS module (position detection device)

[1550] Internet communication function (network)

[1551] Server (data processing device)

[1552] AI analysis module (machine learning method)

[1553] Map display software (map data and display means)

[1554] Data collection and transmission

[1555] The user uses a smartphone or dedicated device to take photos and videos (image data) of the road or hiking trail of interest. For example, a photo of a fallen tree may be taken while hiking. The device uses its built-in GPS module to obtain the latitude and longitude of the location where the photo was taken. For example, the device obtains information such as "latitude 35.625, longitude 139.243."

[1556] Next, the device sends the acquired image data and location information (geographical information) to a server via the Internet. This data transmission uses the HTTPS protocol to ensure data security.

[1557] Receiving and analyzing data

[1558] The server receives data packets sent from the device. The received photos, videos, and location information are first stored in a database. The server then inputs the stored photos and videos into an AI analysis module.

[1559] The AI ​​analysis module analyzes elements in the image (e.g., fallen trees, snow, collapsed roads, etc.) to determine passability, required equipment, and congestion. For example, if a hiking trail is covered in snow, the analysis results will be "impassable" and "Required equipment: snow gear."

[1560] Data integration and map updates

[1561] The server integrates the analysis results returned by the AI ​​analysis tool with the location information. For example, if the location at latitude 35.625, longitude 139.243 is determined to be impassable, the map data is updated to show that location in red. This ensures that the map data viewed by the user is always up to date.

[1562] User Notification and Assistance

[1563] The server generates an alert message based on the analysis results. For example, it generates a message saying, "This hiking trail is currently impassable. Please bring snow gear." This alert message is then sent to the user's device via the network. The device receives this message and immediately notifies the user, who can then take appropriate action.

[1564] Specific examples

[1565] Examples of hiking trails

[1566] User A takes a photo on the hiking trail on Mount Takao and uploads it through the app. The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server. The server receives the data and analyzes the photo using AI analysis. The AI ​​recognizes that the hiking trail is covered in snow and generates a result that says "impassable." The server updates the map data based on the analysis results and sets the target area to be displayed in red. The device displays the latest map data and notifies User A, "This hiking trail is currently impassable. Please bring snow gear."

[1567] Example of a disaster

[1568] User B takes a photo of the road after the earthquake and uploads it through the app. The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server. The server receives the data and analyzes the photo using AI analysis. The AI ​​recognizes that the road has collapsed and generates a result that says "impassable." The server updates the map data based on the analysis results and sets the affected area to be displayed in red. The device displays the latest map data and notifies User B, "This road is currently impassable. Please select another route."

[1569] Prompt Sentence Examples

[1570] "Analyze the condition of the hiking trail in this photo and determine the necessary equipment and passability."

[1571] "Please analyze the photo of this road and let me know the current traffic situation."

[1572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1573] Step 1: Data collection

[1574] The user uses a smartphone or dedicated device to take photos and videos of the road or hiking trail of interest. The input data is the captured image data, and location information (latitude and longitude) is obtained using a GPS module. This location information becomes additional input data. Specifically, the user operates a camera app to take a photo. The device receives the GPS signal and adds the obtained location information to the image data.

[1575] Step 2: Send data

[1576] The terminal assembles the acquired image data and location information into packets and sends them to a server via the Internet. The input data is a packet containing the image data and location information, and the output data is the packet sent to the server. Specifically, the terminal uses an internal communication module to send data to the server securely and quickly using the HTTPS protocol.

[1577] Step 3: Receiving data

[1578] The server receives data packets sent from the device. The input data is image data and location information in packet format, and the output data is the storage of the received data in a repository for analysis. Specifically, the server receives data via a specific API endpoint and stores the received image data and location information in a database.

[1579] Step 4: Data analysis

[1580] The server inputs the stored image data and location information into the AI ​​analysis module. The input data is the image data and location information, and the output data is the analysis results. Specifically, the AI ​​analysis module analyzes elements in the image (e.g., fallen trees, snow, collapsed roads, etc.) and determines whether the path is passable, what equipment is required, and how crowded the path is. For example, the AI ​​recognizes that a hiking trail is covered in snow and generates the results "impassable" and "Required equipment: snow gear."

[1581] Step 5: Data Integration

[1582] The server integrates the analysis results and location information returned from the AI ​​analysis means. The input data are the analysis results and location information, and the output data is updated map data. Specifically, the server reflects the analysis results in the map data, making it possible to display the relevant points in a new state. For example, it can be set to display impassable points in red.

[1583] Step 6: Data Distribution

[1584] The server generates an alert message based on the analysis results and sends it to the user's terminal. The input data is the analysis results, and the output data is the generated alert message and its delivery to the user's terminal. Specifically, the server generates an appropriate warning message from the analysis results and sends it to the user's terminal via the network.

[1585] Step 7: User Notification

[1586] The terminal notifies the user of the alert message received from the server. The input data is the alert message, and the output data is the notification to the user. Specifically, the terminal displays the received message as a pop-up on the screen to immediately alert the user. The user can then take appropriate action based on this.

[1587] (Application example 1)

[1588] 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."

[1589] Conventional self-driving vehicles have had difficulty grasping road conditions in real time and immediately sharing appropriate information with other vehicles. As a result, they have been unable to respond quickly to emergencies such as road obstacles and accidents, which has led to problems such as reduced driving safety and efficiency.

[1590] 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.

[1591] In this invention, the server includes means for uploading photos and videos taken by the user via the Internet, means for acquiring current location information using a GPS module, means for transmitting the taken photos and videos and the location information to the server, artificial intelligence means for analyzing the received photos, videos and location information in the server, means for updating map data in real time based on the information analyzed by the artificial intelligence means and sending alerts to other autonomous vehicles, and means for displaying the updated map data on the user terminal, thereby enabling road obstacles and emergency situations to be quickly detected and information to be shared with other autonomous vehicles in real time.

[1592] A "User" is a person or organization that uses the System to take photos and videos and upload them via the Internet.

[1593] A "GPS module" is a device for obtaining geographical location information, and is used to obtain data such as latitude and longitude.

[1594] A "server" is a computer system that receives and processes data, stores photos, videos, and location information, and analyzes them using artificial intelligence.

[1595] "Artificial intelligence means" refers to technology that analyzes received photos and videos to determine road conditions, passability, and necessary equipment.

[1596] "Map data" refers to electronic data containing geographical information, and is used to visually display road conditions, passability, and the like.

[1597] "Updating means" refers to a device or system that has the function of updating map data in real time based on information analyzed by the artificial intelligence means.

[1598] "Means for sending alerts" is a function for sending emergency information or important notifications to other autonomous vehicles or users.

[1599] A "user terminal" is a device used by a user to receive and display information, such as a smartphone or tablet.

[1600] This system uses the camera and GPS module of the autonomous vehicle to grasp the current road conditions in real time and provides information to other autonomous vehicles and users based on the analysis results. The entire system consists of a terminal, a server, an artificial intelligence means, a map data update means, an alert sending means, and a user terminal.

[1601] program

[1602] The system's programming is structured as follows:

[1603] 1. An autonomous vehicle equipped with a camera and GPS module uses the camera to continuously take photos and videos of the road.

[1604] 2. The GPS module obtains the location information (latitude and longitude) of the current location where the photo or video was taken.

[1605] 3. The device sends the captured photos and videos, as well as location information, to a server via the Internet.

[1606] 4. The server stores the received photos, videos, and location information in a database.

[1607] 5. The server inputs the stored photos and videos into the artificial intelligence means, which analyzes elements within the images, including machine learning models to detect fallen trees, snow, and road collapses, for example.

[1608] 6. Based on the information analyzed by the artificial intelligence means, the map data update means updates the map data in real time. The updated map data is visually displayed, and impassable areas are indicated in red.

[1609] 7. The alert sending means sends the analysis results to other autonomous vehicles to alert them to road obstacles or emergency situations.

[1610] 8. The user device receives the updated map data and alert messages from the server and displays or notifies the user.

[1611] Hardware and Software

[1612] The hardware used is a camera and GPS module mounted on the autonomous vehicle, as well as a terminal with internet connectivity. The server is equipped with a database and artificial intelligence means, which are implemented using machine learning libraries such as Python and TensorFlow.

[1613] Specific examples

[1614] For example, while autonomous vehicle A is driving down a road, it takes pictures of a fallen tree on the road with its camera. These pictures, along with location information obtained by the GPS module, are sent to a server via the device. The server stores the received data and analyzes it using artificial intelligence means. When a fallen tree is detected, map data is updated in real time, and the affected area is highlighted in red. Furthermore, an alert is sent to other autonomous vehicles, allowing them to automatically change their routes to avoid the affected road. This improves the safety of autonomous vehicles overall.

[1615] Prompt Sentence Examples

[1616] "Please design a system that will analyze the current road conditions captured by my self-driving vehicle in real time and provide traffic updates. It will also use camera images and GPS data, perform AI analysis, and provide alerts to notify other vehicles."

[1617] The above is the mode for carrying out the invention in this system.

[1618] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1619] Step 1:

[1620] Taking photos and videos with the camera

[1621] Cameras in self-driving vehicles capture road conditions in real time.

[1622] Input: Road image

[1623] Output: Photos and videos taken

[1624] What it does: The camera periodically captures images and saves frames at regular intervals.

[1625] Step 2:

[1626] Obtaining location information using the GPS module

[1627] The GPS module installed in the autonomous vehicle acquires the latitude and longitude of its current location.

[1628] Input: Position data from satellites

[1629] Output: Current location latitude and longitude

[1630] How it works: The GPS module periodically communicates with satellites to update location information.

[1631] Step 3:

[1632] Sending data

[1633] The device sends the photos and videos taken and the acquired location information to a server via the Internet.

[1634] Input: photos, videos, location information

[1635] Output: Data sent to the server

[1636] Specific operation: The terminal packetizes the data and sends a POST request to the server using the HTTP protocol.

[1637] Step 4:

[1638] Receiving and storing data on the server

[1639] The server stores the received photos, videos, and location information in a database.

[1640] Input: photos, videos, location information

[1641] Output: Data recorded in the database

[1642] Specific operation: The server parses the received data and stores it in the corresponding database fields.

[1643] Step 5:

[1644] Data analysis by means of artificial intelligence

[1645] The server's artificial intelligence means analyzes the received photos and videos to determine the condition of the road and whether it is passable.

[1646] Input: photos, videos, location information

[1647] Output: Analysis results such as "fallen tree detection" and "passage impassable"

[1648] Specific operation: The artificial intelligence means analyzes the image using a machine learning model, extracts features, and generates a judgment result.

[1649] Step 6:

[1650] Real-time map data updates

[1651] The server updates the map data in real time based on the analysis results.

[1652] Input: Analysis results, location information

[1653] Output: Updated map data

[1654] Specific operation: The server reads map data from the database, corrects the relevant parts based on the analysis results, and re-saves them.

[1655] Step 7:

[1656] Sending alerts

[1657] Based on the analysis results, the server sends alerts to other self-driving vehicles and users as needed.

[1658] Input: Analysis results, location information

[1659] Output: Alert message sent

[1660] Specific operation: The server generates an alert message and sends it to other autonomous vehicles via means such as push notifications.

[1661] Step 8:

[1662] Displaying information on the user's device

[1663] The user terminal receives the latest map data and alert message and displays or notifies the user.

[1664] Input: Updated map data, alert message

[1665] Output: Map data and alert messages displayed to the user

[1666] Specific operation: The user terminal receives the data from the server and displays it to the user through the GUI.

[1667] Through these processing steps, autonomous vehicles can monitor road conditions in real time and quickly share information with other vehicles, greatly improving road safety and efficiency.

[1668] 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.

[1669] System Overview

[1670] This invention is a system that grasps the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflects this in map data. This system also incorporates an emotion engine that recognizes the user's emotions, and generates customized alert messages according to the user's situation, thereby providing more effective information provision and support for countermeasures.

[1671] Data Collection Stage

[1672] Users use their smartphones or dedicated devices to take photos and videos of the roads and hiking trails in question.

[1673] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[1674] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[1675] Data reception and analysis stage

[1676] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[1677] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[1678] Emotion Engine

[1679] The device also captures the user's facial expressions and voice when taking photos and videos.

[1680] The server sends this emotional data to the emotion engine, which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state.

[1681] The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[1682] Data linkage and display stage

[1683] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[1684] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[1685] User Support Stage

[1686] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[1687] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action.

[1688] Specific examples

[1689] Examples of hiking trails

[1690] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[1691] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[1692] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the mountain trail is covered in snow and generates a result of "impassable." The emotion engine recognizes the impatience in the user's facial expression and generates a message saying, "Please stay calm. Please bring snow gear."

[1693] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1694] The device displays the latest map data and notifies the user with customized alert messages.

[1695] Example of a disaster

[1696] User B takes a photo of the road after the earthquake and uploads it through the app.

[1697] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[1698] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying, "Please remain calm. Please choose another route."

[1699] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1700] The device displays the latest map data and notifies the user with customized alert messages.

[1701] In this way, the system not only improves the accuracy of map data but also provides customized messages based on the user's emotional state, supporting safe and effective responses.

[1702] The processing flow will be explained below.

[1703] Step 1:

[1704] Users use their smartphones or dedicated devices to take photos and videos of roads and hiking trails.

[1705] Step 2:

[1706] The device uses a camera module to simultaneously capture the user's facial expressions and voice, and this data is used by the emotion engine.

[1707] Step 3:

[1708] The device uses the GPS module to obtain the location information (latitude and longitude) of the current location where the photo or video was taken, and the obtained location information is tagged to the photo or video.

[1709] Step 4:

[1710] The device sends the captured photos and videos, as well as current location information, and data on the user's facial expressions and voice, to a server via the Internet.

[1711] Step 5:

[1712] The server receives data packets sent from the device, analyzes the received data, and stores photos, videos, location information, facial expressions, and voice data in a database.

[1713] Step 6:

[1714] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[1715] Step 7:

[1716] The server inputs facial and voice data into the emotion engine, which then analyzes the user's emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as "anxiety" or "impatience."

[1717] Step 8:

[1718] AI tools determine whether a path is passable, what equipment is needed, and how crowded it is. At the same time, the emotion engine combines the analyzed emotional data and generates a customized alert message based on the analysis results. For example, if the trail is covered in snow and the user is feeling anxious, the message generated is, "Please stay calm. Please bring snow gear."

[1719] Step 9:

[1720] The server integrates the analysis results returned by the AI ​​analysis means and emotion engine with the location information, and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[1721] Step 10:

[1722] The server sends the updated map data to the device, and when the user opens the application, the latest map information is displayed.

[1723] Step 11:

[1724] The device displays the received map data and notifies the user of the current situation in real time, for example by displaying warning messages and icons on the screen.

[1725] Step 12:

[1726] The server generates and sends an alert message to the user based on the analysis results. In the event of an emergency, the alert message is sent to the user as a push notification.

[1727] Step 13:

[1728] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action. For example, the message might read, "This road is currently collapsed and impassable. Please choose an alternative route."

[1729] Example 2

[1730] 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."

[1731] Conventional map data update systems lack the functionality to reflect user-provided information in real time, and are insufficient in providing safety information based on the user's emotional state, making it difficult to take effective measures to ensure the user's safety. There is a growing need for a system that can instantly reflect the situation the user faces and alert them to the appropriate equipment and routes, especially during mountain climbing and disasters.

[1732] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1733] In this invention, the server includes artificial intelligence means for analyzing received photos, videos, and location information, means for including in the server an emotion engine for analyzing the user's emotional state based on the analysis, and means for updating map data based on the information analyzed by the artificial intelligence means and the emotion engine. This makes it possible to analyze data captured by the user in real time, generate customized alert messages according to the user's emotional state, and instantly update the map data.

[1734] A "user" is an entity that uses the system to take photos and videos and obtain location information.

[1735] A "terminal" is a smartphone or dedicated device used by a user, which takes photos and videos, obtains location information using a GPS module, and transmits the data to a server via the Internet.

[1736] A "GPS module" is a receiving device for identifying a location on Earth and is a device used to obtain latitude and longitude information.

[1737] A "server" is a device or system that receives photos, videos, and location information sent from a terminal, analyzes and stores this data, updates map data, and generates alert messages for users.

[1738] "Artificial intelligence means" is a general term for algorithms and technologies that analyze photographs and videos, recognize the elements and situations contained therein, and generate analytical results.

[1739] The "emotion engine" is a system that uses facial expression recognition technology and voice analysis technology to analyze a user's facial expressions and voice and recognize their emotional state.

[1740] "Map data" refers to data that represents geographical information, and is updated based on content including the status of roads, hiking trails, ports, houses, and the like.

[1741] An "alert message" is a notification message generated based on the analysis results and the user's emotional state, and encourages the user to take appropriate action.

[1742] System Overview

[1743] This system aims to grasp the status of roads, trails, ports, and houses in real time based on photos and videos taken by users and reflect them in map data. It also incorporates an emotion engine that recognizes the user's emotions and generates customized alert messages according to the user's emotional state. This allows for more effective provision of information and support for countermeasures to users.

[1744] Data Collection Stage

[1745] The user uses a smartphone or dedicated device to take photos and videos of the road or hiking trail in question by pressing the camera button on the smartphone's camera application.

[1746] The device uses the GPS module to obtain location information (latitude and longitude) of the current location where the photo or video was taken. The obtained location information is tagged with the photo or video. Specifically, the GPS sensor in the device measures the current location, and the location information is added to the metadata based on this.

[1747] The device sends the captured photos and videos, as well as the current location information, to a server via the Internet. This process involves the user pressing the "upload" button in the application.

[1748] Data reception and analysis stage

[1749] The server receives data packets sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database. The server listens for data on the receiving port, analyzes the data packets for photos, videos, and location information, and stores them in a database.

[1750] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the image. Specifically, the AI ​​module (e.g., TensorFlow, OpenCV) recognizes situations such as fallen trees, snow, collapsed roads, and flooding.

[1751] Emotion Engine

[1752] When taking photos or videos, the device also captures the user's facial expressions and voice. The device's camera and microphone are activated to collect the user's facial and voice data.

[1753] The server sends this emotional data to an emotion engine (e.g., Microsoft Azure Emotion API), which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state. The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[1754] Data linkage and display stage

[1755] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[1756] The server sends the updated map data to the device. When the user opens the application, the latest map information is displayed. The server generates the latest map data and sends it to the device in a data packet.

[1757] User Support Stage

[1758] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[1759] The device receives the alert message and displays a notification to the user. The user can check the notification and consider what action is necessary. The device displays the received alert message as a pop-up notification, prompting the user to take action.

[1760] Specific examples

[1761] Examples of hiking trails

[1762] User A takes a photo on the hiking trail on Mount Takao and uploads it through the app. The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server. The server receives the data and analyzes the photo and the user's emotional state using AI analysis means and an emotion engine. The AI ​​recognizes that the hiking trail is covered in snow and generates a result saying "impassable." The emotion engine recognizes the user's impatience from their facial expression and generates a message saying "Please stay calm. Please bring snow gear." The server updates the map data based on the analysis results and sets the target area to be displayed in red. The device displays the latest map data and notifies the user with a customized alert message.

[1763] Example of a disaster

[1764] User B takes a photo of the road after the earthquake and uploads it through the app. The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server. The server receives the data and analyzes the photo and the user's emotional state using AI analysis means and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying "Please remain calm. Please select an alternative route." The server updates the map data based on the analysis results and sets the affected area to be displayed in red. The device displays the latest map data and notifies the user with a customized alert message.

[1765] Prompt Sentence Examples

[1766] "Uploading the status of the hiking trails on Mount Takao. Current location is latitude 35.625, longitude 139.243. Snow is visible in the photo. I'm getting anxious."

[1767] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1768] System processing flow

[1769] Step 1:

[1770] Users take photos and videos using their smartphones or dedicated devices.

[1771] Input: Photos and videos taken by the user.

[1772] Output: Captured photo and video files.

[1773] Specific actions: The user opens the camera app on their smartphone, frames the subject, and presses the capture button.

[1774] Step 2:

[1775] The device uses the GPS module to obtain location information (latitude and longitude) and tag photos and videos.

[1776] Input: Photos and videos taken, location data from the GPS module.

[1777] Output: Location-tagged photo and video files.

[1778] How it works: The device's GPS sensor measures your current location and adds the acquired latitude and longitude as metadata to photo and video files.

[1779] Step 3:

[1780] The device sends photos, videos, and location information to a server via the Internet, and the user presses the "upload" button within the application.

[1781] Input: Location-tagged photo and video files.

[1782] Output: Data packets sent to the server.

[1783] Specific operation: The user presses the "upload" button in the application, and the device sends a data packet to the server via the Internet.

[1784] Step 4:

[1785] The server receives data packets sent from the device, analyzes the photos, videos, and location information, and stores them in a database.

[1786] Input: The data packet sent to the server.

[1787] Output: Photos, videos, and location information stored in a database.

[1788] Specific operation: The server listens for data on the receiving port, extracts the received data from the packets, and stores the photos, videos, and location information in a database.

[1789] Step 5:

[1790] The server inputs the stored photos and videos into an AI analysis module, which analyzes the elements within the images.

[1791] Input: Photos and videos stored in the database.

[1792] Output: Analysis results for each image.

[1793] Specific operation: The server inputs the image data into an AI analysis module (e.g., TensorFlow, OpenCV) and obtains the analysis results. The AI ​​recognizes situations such as fallen trees, snow, collapsed roads, and flooding.

[1794] Step 6:

[1795] The device also captures the user's facial expressions and voice when taking photos and videos.

[1796] Input: The user's facial expressions and voice at the time of capture.

[1797] Output: User's facial and voice data.

[1798] Specific operation: The device's camera and microphone are activated to collect the user's facial and voice data.

[1799] Step 7:

[1800] The server sends the user's facial expression and voice data to the emotion engine, which analyzes the user's emotional state.

[1801] Input: User facial and voice data.

[1802] Output: Parsed emotional state of the user.

[1803] Specific operation: The server inputs the received data into an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotional state.

[1804] Step 8:

[1805] The server integrates the analysis results of the AI ​​analysis means and emotion engine with the location information and updates the map data.

[1806] Input: Analysis results of AI analysis method and emotion engine, and location information.

[1807] Output: Updated map data.

[1808] Specific operation: The server integrates the analysis results and location information into a map database, color-coding the relevant area and updating it.

[1809] Step 9:

[1810] The server sends the updated map data to the terminal, and the latest map information is displayed when the user opens the application.

[1811] Input: Updated map data.

[1812] Output: The latest map data sent to the device.

[1813] Specific operation: The server generates the latest map data and sends it to the device as a data packet.

[1814] Step 10:

[1815] The server generates a customized alert message based on the analysis results and sends it to the user.

[1816] Input: Analysis results of AI analysis method and emotion engine.

[1817] Output: The alert message sent to the user.

[1818] Specific operation: The server generates an alert message based on the analysis results and sends it to the terminal as a data packet.

[1819] Step 11:

[1820] The device receives the alert message and displays a notification to the user, who then checks the notification and considers what action to take.

[1821] Input: The alert message sent by the server.

[1822] Output: The alert message that will be displayed to the user.

[1823] Specific operation: The device displays the received alert message as a pop-up notification, prompting the user to take action.

[1824] (Application example 2)

[1825] 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."

[1826] Conventional road condition monitoring systems and hiking trail information systems have difficulty accurately grasping local conditions in real time. Furthermore, they are unable to provide information that takes into account the user's psychological state, making it difficult to support appropriate responses in emergencies or stressful situations. This presents a problem of a lack of support for users to make calm decisions on their own.

[1827] 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.

[1828] In this invention, the server includes means for uploading photos and videos taken by the user via the Internet, means for acquiring current location information using a GPS module, means for transmitting the taken photos and videos and the location information to the server, AI means for analyzing the received photos, videos, and location information, means including an emotion engine for capturing the user's facial expressions and voice and analyzing their emotional state, means for updating map data based on the information analyzed by the AI ​​means and the analysis results of the emotion engine, and means for displaying the updated map data and a customized alert message on the user terminal. This makes it possible to grasp the local situation in real time and provide an appropriate alert message based on the user's emotional state.

[1829] A "User" is any person or entity that utilizes the system to take photos and videos and upload the data over the Internet.

[1830] "Photos and videos" are still images and videos taken by users to record local conditions and upload them to the system.

[1831] The "Internet" is a global network for transmitting and receiving data between user terminals and servers.

[1832] A "GPS module" is a device that measures the current location of a user terminal and acquires latitude and longitude information.

[1833] A "server" is a centralized computer system that receives, processes, and analyzes data sent from user terminals over the Internet.

[1834] "AI means" is an artificial intelligence system that analyzes road conditions and hiking trail conditions from photos and videos, determines passability and necessary equipment, and generates customized alert messages based on the user's emotional state.

[1835] The "emotion engine" is a system that captures the user's facial expressions and voice and analyzes their emotional state.

[1836] "Map data" refers to geographical information data that is updated based on location information obtained from users and the results of analysis of photos and videos.

[1837] An "alert message" is a warning or advice message that is generated based on the analysis results and the user's emotional state.

[1838] A "user terminal" is an electronic device such as a smartphone or dedicated terminal that a user uses to access the system.

[1839] MODE FOR CARRYING OUT THE INVENTION

[1840] In order to implement the present invention, the following hardware and software are used.

[1841] Hardware

[1842] 1. Smartphone or dedicated device: Equipped with a camera function and a GPS module.

[1843] 2. Server: A centralized computer system with a powerful processor and large amount of storage.

[1844] software

[1845] 1. AI analysis module: An image analysis system that uses machine learning libraries such as TensorFlow and PyTorch.

[1846] 2. Emotion recognition engine: The EmotionRecognizer library combines facial expression recognition technology and voice analysis technology.

[1847] 3. Data communication libraries: Software for sending and receiving data over the Internet, such as the requests library.

[1848] System Flow

[1849] 1. Data Collection Stage

[1850] Users use their smartphones or dedicated devices to take photos and videos of local roads and hiking trails.

[1851] The device uses a GPS module to obtain location information and tag photos and videos.

[1852] These data are sent to a server over the Internet, where the user presses the "upload" button in the application.

[1853] 2. Data Reception and Analysis Stage

[1854] The server receives the data sent from the device, analyzes the received data, and stores the photos, videos, and location information in a database.

[1855] The server inputs the stored photos and videos into an AI analysis module, which then analyzes elements in the images, recognizing conditions such as fallen trees, snow, collapsed roads, and flooding.

[1856] 3. Emotion Engine

[1857] The device also captures the user's facial expressions and voice when taking photos and videos.

[1858] The server sends this emotional data to the emotion engine, which uses facial expression recognition and voice analysis technologies to analyze the user's emotional state.

[1859] The analyzed emotional data is passed to an AI tool, which generates a customized alert message based on the analysis results.

[1860] 4. Data linkage and display stage

[1861] The server integrates the analysis results returned by the AI ​​analysis tool with the location information and updates the map data in real time. Based on the analysis results, the corresponding areas on the map are color-coded to make them easy for users to understand.

[1862] The server sends the updated map data to the user's device, and when the user opens the application, the latest map information is displayed.

[1863] 5. User Support Stage

[1864] The server generates and sends an alert message to the user based on the analysis results. For example, if the user shows signs of impatience or anxiety, a customized message such as "Please stay calm. We will suggest an alternative route." is sent.

[1865] The device receives the alert message and displays a notification to the user, who can then review the notification and consider any necessary action.

[1866] Specific examples

[1867] Examples of hiking trails

[1868] User A takes a photo on the hiking trail of Mount Takao and uploads it from the app.

[1869] The device sends the photo and GPS information (latitude 35.625, longitude 139.243) to the server.

[1870] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the mountain trail is covered in snow and generates a result of "impassable." The emotion engine recognizes the impatience in the user's facial expression and generates a message saying, "Please stay calm. Please bring snow gear."

[1871] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1872] The device displays the latest map data and notifies the user with customized alert messages.

[1873] Example of a disaster

[1874] User B takes a photo of the road after the earthquake and uploads it through the app.

[1875] The device sends the photo and GPS information (latitude 34.693, longitude 135.502) to the server.

[1876] The server receives the data and analyzes the photo and the user's emotional state using AI analysis tools and an emotion engine. The AI ​​recognizes that the road has collapsed and generates a result saying "impassable." The emotion engine recognizes tension in the user's voice and generates a message saying, "Please remain calm. Please choose another route."

[1877] The server updates the map data based on the analysis results and sets the target area to be displayed in red.

[1878] The device displays the latest map data and notifies the user with customized alert messages.

[1879] Prompt Sentence Examples

[1880] "The map should reflect the latest road conditions based on photos taken by the user. The alert message should also take into account the user's emotional state."

[1881] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1882] Step 1:

[1883] Users use their smartphones or dedicated devices to take photos and videos of local roads and hiking trails.

[1884] Input: Photos and videos taken

[1885] Output: None (Photos and videos are saved on the device)

[1886] Specific actions: The user launches the device's camera app and presses the capture button.

[1887] Step 2:

[1888] The device uses a GPS module to obtain location information of the current location.

[1889] Input: GPS module of the device

[1890] Output: Current location (latitude and longitude)

[1891] Specific operation: The device's GPS function will automatically activate and obtain the current latitude and longitude.

[1892] Step 3:

[1893] The user presses the "upload" button in the application to send the photos and videos they have taken and their location information to the server.

[1894] Input: Photos, videos, location information

[1895] Output: Send data to the server

[1896] What happens: The user presses the upload button in the application, and the data is sent over the Internet to the server.

[1897] Step 4:

[1898] The server stores the received photos, videos, and location information in a database for analysis.

[1899] Input: Photos, videos, and location information sent

[1900] Output: Data stored in the database

[1901] Specific operation: The server writes the received data to the database.

[1902] Step 5:

[1903] The server inputs the stored photos and videos into an AI analysis module, which analyzes the elements within the images.

[1904] Input: Photos and videos stored in the database

[1905] Output: Analyzed image data (e.g., fallen trees, snow, collapsed roads, etc.)

[1906] Specific operation: The AI ​​analysis module analyzes the image and outputs the recognized elements.

[1907] Step 6:

[1908] The device captures the user's facial expressions and voice along with photos and videos and sends them to the server.

[1909] Input: User's facial expressions, voice, photos and videos

[1910] Output: Send emotion data to the server

[1911] Specific operation: The device's camera and microphone capture the user's facial expressions and voice, and the data is sent to the server.

[1912] Step 7:

[1913] The server inputs the emotion data into an emotion engine to analyze the user's emotional state.

[1914] Input: Emotion data

[1915] Output: Analyzed emotional state (e.g., impatience, anxiety, tension, etc.)

[1916] Specific operation: The emotion engine analyzes facial and vocal data to determine the emotional state.

[1917] Step 8:

[1918] The server integrates the analysis results with location information and updates the map data in real time.

[1919] Input: Image analysis results, location information, emotional state

[1920] Output: Updated map data

[1921] Specific operation: The server integrates the analysis results and location information into the map database and updates the map.

[1922] Step 9:

[1923] The server generates a customized alert message based on the map data.

[1924] Input: Emotional state, analysis results

[1925] Output: Customized alert message

[1926] Specific behavior: The server takes into account the emotional state and analysis results to generate an appropriate alert message to send to the user.

[1927] Step 10:

[1928] The server sends updated map data and customized alert messages to the user terminal.

[1929] Input: Updated map data, alert message

[1930] Output: Sending data to the user's terminal

[1931] Specific operation: The server sends map data and an alert message to the user's device.

[1932] Step 11:

[1933] The device displays the latest map data and notifies the user with customized alert messages.

[1934] Input: Updated map data, alert message

[1935] Output: User notification

[1936] Specific operation: The device displays map data and presents an alert message to the user as a notification.

[1937] 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.

[1938] 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.

[1939] 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.

[1940] 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.

[1941] 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.

[1942] 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.

[1943] 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).

[1944] 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.

[1945] 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."

[1946] 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.

[1947] 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).

[1948] 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.

[1949] 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.

[1950] 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.

[1951] 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.

[1952] 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.

[1953] 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.

[1954] 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.

[1955] 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.

[1956] 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.

[1957] 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.

[1958] The following is further disclosed regarding the above embodiment.

[1959] (Claim 1)

[1960] A means for users to upload photos and videos they have taken via the Internet;

[1961] A means for obtaining current location information using a GPS module;

[1962] means for transmitting the captured photographs and videos and the location information to a server;

[1963] An AI means for analyzing the received photos, videos, and location information in the server;

[1964] means for updating map data based on the information analyzed by the AI ​​means;

[1965] means for displaying the updated map data on a user terminal;

[1966] A system including:

[1967] (Claim 2)

[1968] The system according to claim 1, wherein the AI ​​means is an AI means that determines road conditions, trail conditions, passability, and necessary equipment from photographs and videos.

[1969] (Claim 3)

[1970] The system of claim 1 , wherein the server includes means for generating and transmitting an alert message to a user based on the analysis result.

[1971] "Example 1"

[1972] (Claim 1)

[1973] A means for transferring image data captured by a user via a network;

[1974] means for acquiring geographic information of a current location using a position detection device;

[1975] means for transmitting the captured image data and the geographical information to a data processing device;

[1976] In the data processing device, a machine learning means for analyzing the received image data and geographic information;

[1977] means for updating map data based on the information analyzed by the machine learning means;

[1978] means for displaying the updated map data on a user terminal;

[1979] A system including:

[1980] (Claim 2)

[1981] The system according to claim 1, wherein the machine learning means is a machine learning means that determines road conditions, mountain trail conditions, passability, and necessary equipment from image data.

[1982] (Claim 3)

[1983] 10. The system of claim 1, wherein the data processing device includes means for generating and transmitting a warning message to a user based on the analysis result.

[1984] "Application Example 1"

[1985] (Claim 1)

[1986] A means for users to upload photos and videos they have taken via the Internet;

[1987] A means for obtaining current location information using a GPS module;

[1988] means for transmitting the captured photographs and videos and the location information to a server;

[1989] an artificial intelligence means in the server for analyzing the received photos, videos and location information;

[1990] means for updating map data based on the information analyzed by the artificial intelligence means;

[1991] means for updating map data in real time based on the information analyzed by the artificial intelligence means and sending alerts to other autonomous vehicles;

[1992] means for displaying the updated map data on a user terminal;

[1993] A system including:

[1994] (Claim 2)

[1995] 2. The system of claim 1, wherein the artificial intelligence means is a means for determining road conditions, passability, and necessary equipment from photos and videos, and sending alerts to other autonomous vehicles.

[1996] (Claim 3)

[1997] 10. The system of claim 1, wherein the server includes means for generating and transmitting an alert message to a user based on the analysis result to other autonomous vehicles.

[1998] "Example 2: Combining Emotion Engines"

[1999] (Claim 1)

[2000] A means for users to upload photos and videos they have taken via the Internet;

[2001] A means for obtaining current location information using a GPS module;

[2002] means for transmitting the captured photographs and videos and the location information to a server;

[2003] an artificial intelligence means in the server for analyzing the received photos, videos and location information;

[2004] means for including in the server an emotion engine for analyzing the user's emotional state based on said analysis;

[2005] means for updating map data based on the information analyzed by the artificial intelligence means and the emotion engine;

[2006] means for displaying the updated map data on a user terminal;

[2007] A system including:

[2008] (Claim 2)

[2009] 2. The system according to claim 1, wherein the artificial intelligence means is an artificial intelligence means that determines road conditions, trail conditions, passability, and necessary equipment from photographs and videos.

[2010] (Claim 3)

[2011] The system of claim 1 , wherein the server includes means for generating and sending a customized alert message to a user based on the analysis results.

[2012] "Application example 2 when combining emotion engines"

[2013] (Claim 1)

[2014] A means for users to upload photos and videos they have taken via the Internet;

[2015] A means for obtaining current location information using a GPS module;

[2016] means for transmitting the captured photographs and videos and the location information to a server;

[2017] An AI means for analyzing the received photos, videos, and location information in the server;

[2018] means including an emotion engine for capturing facial expressions and voice of a user and analyzing the user's emotional state;

[2019] means for updating map data based on the information analyzed by the AI ​​means and the analysis results of the emotion engine;

[2020] means for displaying the updated map data and the customized alert message on the user terminal;

[2021] A system including:

[2022] (Claim 2)

[2023] The system of claim 1, wherein the AI ​​means is an AI means for determining road conditions, trail conditions, passability, and necessary equipment from photos and videos, and also an AI means for generating customized alert messages based on the user's emotional state.

[2024] (Claim 3)

[2025] The system of claim 1 , wherein the server includes means for generating and sending an alert message to the user based on the analysis results and the user's emotional state. [Explanation of symbols]

[2026] 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 users to upload photos and videos they have taken via the Internet; A means for obtaining current location information using a GPS module; means for transmitting the captured photographs and videos and the location information to a server; An AI means for analyzing the received photos, videos, and location information in the server; means for updating map data based on the information analyzed by the AI ​​means; means for displaying the updated map data on a user terminal; A system including:

2. The system according to claim 1, wherein the AI ​​means is an AI means that determines road conditions, conditions of mountain trails, passability, and necessary equipment from photographs and videos.

3. The system of claim 1 , wherein the server includes means for generating and transmitting an alert message to a user based on the analysis result.

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