System

Abstract

A system is provided.SOLUTION: A system includes a terminal unit that acquires position information, a terminal unit that captures at least one image data, a server unit that receives the position information and the image data transmitted from the terminal unit and acquires altitude data, a server unit that generates a topographical image from which an artificial object is excluded by using an image generation AI based on the acquired altitude data, and a server unit that transmits the generated topographical image to the terminal unit.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] In modern society, the abundance of buildings and man-made structures makes it difficult to grasp the terrain. This problem is particularly acute in fields such as disaster prevention, education, and historical research, where understanding the actual terrain is crucial, yet current methods often require access to information that is difficult to access. For example, in the event of a disaster, pure terrain information unaffected by buildings is required, but there is a lack of means to provide this information immediately. In education, there is also a lack of visual aids to help students understand the actual terrain. Furthermore, in historical research, limited means exist for recreating past terrain and landscapes, which can hinder research progress. Therefore, there is a need for technology that can provide terrain images unaffected by buildings and man-made structures. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system including a terminal means for acquiring location information, a terminal means for capturing at least one image data, a server means for receiving the location information and image data transmitted from the terminal means and acquiring elevation data, a server means for generating a terrain image from which artificial objects have been removed using an image generation AI based on the acquired elevation data, and a server means for transmitting the generated terrain image to the terminal means. Specifically, a user transmits an image captured with a smartphone and location information to the server, and the server generates a terrain image from which artificial objects have been removed using the elevation data and the image generation AI. The generated terrain image is then transmitted to the user's smartphone to help the user visually understand the terrain.

[0006] "Location information" is data that indicates a specific geographic location, usually expressed in the form of latitude and longitude.

[0007] The "terminal means" is a device operated by a user, and in the present invention refers to a smartphone, tablet, or the like, which acquires and transmits location information and image data.

[0008] "Image data" is data that digitally represents visual information captured using an optical device.

[0009] The "server means" is a server device that receives and analyzes data sent from multiple terminals, processes the data, and provides information.

[0010] "Elevation data" is data that indicates the height of a specific geographical point from the earth's surface, and is information used to represent the terrain three-dimensionally.

[0011] "Image generation AI" is a system that uses artificial intelligence technology to analyze and generate image data, and in the present invention is used particularly to detect and eliminate artifacts.

[0012] "Man-made objects" refer to structures and objects created by humans, including buildings, roads, and power lines.

[0013] A "terrain image" is a visual representation of the terrain, free of artificial objects, generated using elevation data and image generation AI. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

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

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] overview

[0036] This invention is a system that uses a smartphone (terminal), a server, image generation AI, and elevation data to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research.

[0037] Program processing overview

[0038] Step 1: User photography and data acquisition

[0039] The user takes a picture of the scenery using a smartphone. When taking a picture, the smartphone uses its GPS function to obtain latitude and longitude information, and also obtains direction information from the camera's direction sensor. The captured image and location information are then temporarily saved on the device.

[0040] Step 2: Send the data

[0041] The terminal transmits the temporarily saved image data, latitude and longitude information, and direction information to the server, and when transmission is complete, waits for a response from the server.

[0042] Step 3: Parsing on the server

[0043] The server obtains elevation data for the area based on the received latitude and longitude information, and performs a topographical analysis based on the obtained elevation data, image data, and direction information.

[0044] Step 4: Artifact removal using image generation AI

[0045] The server inputs the received image data into the image generation AI to detect man-made objects. The image generation AI then removes the detected man-made objects and generates a realistic terrain image. The generated terrain image is temporarily stored on the server.

[0046] Step 5: Submit the generated image

[0047] The server transmits the generated terrain image data to the user's terminal, where it is stored and displayed to the user.

[0048] Specific examples

[0049] Use case 1: Disaster prevention

[0050] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[0051] Terminal: The smartphone sends the captured image data, latitude and longitude information, and direction information to the server.

[0052] Server: Based on the received data, the server obtains elevation data for the relevant area from an elevation database and uses image generation AI to generate a terrain image that removes artificial objects.

[0053] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[0054] Use case 2: Education

[0055] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[0056] Device: Students take photos of the mountain behind the school using their smartphones and send the image data and location information to the server.

[0057] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[0058] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[0059] Use Case 3: Historical Research

[0060] User: A historian conducts fieldwork at a site known as a battlefield from the Sengoku period.

[0061] Terminal: The researcher takes a contemporary photograph and sends the image data and location information to the server.

[0062] Server: Elevation data is obtained based on the received data, and the landscape at that time is recreated using old map data and image generation AI.

[0063] Terminal: Images of the past terrain are sent to the researcher's smartphone, allowing them to visually understand the specific terrain of the battlefield at that time.

[0064] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] A user starts the camera application on their smartphone and takes a picture of a target scene. After taking the picture, the camera application acquires the image data.

[0068] Step 2:

[0069] The device acquires the latitude and longitude information of its current location using the built-in GPS module, and also acquires direction information from the camera's direction sensor.

[0070] Step 3:

[0071] The device temporarily stores the acquired image data, latitude and longitude information, and direction information in its internal storage.

[0072] Step 4:

[0073] The device establishes a connection to send the stored data to a server, which typically occurs over the Internet.

[0074] Step 5:

[0075] The terminal transmits the image data, latitude and longitude information, and direction information to the server. After the transmission is complete, the terminal waits for a response from the server.

[0076] Step 6:

[0077] Based on the received latitude and longitude information, the server retrieves the elevation data for the relevant area from the elevation database.

[0078] Step 7:

[0079] The server uses the received image data, elevation data, and direction information to prepare for analyzing the topography of the target area.

[0080] Step 8:

[0081] The server inputs the image data into the image generation AI, which then detects artificial objects (buildings, roads, power lines, etc.) in the image.

[0082] Step 9:

[0083] The image generation AI removes any detected artificial objects and generates a terrain image without any artificial objects. This generated terrain image is temporarily stored on the server.

[0084] Step 10:

[0085] The server prepares to send the generated topographical image data to the user's terminal, which is also sent via the Internet.

[0086] Step 11:

[0087] The server sends the generated terrain image to the terminal, confirms the completion of the transmission, and records the operation log.

[0088] Step 12:

[0089] The terminal stores the generated image data received from the server in local storage.

[0090] Step 13:

[0091] The device displays the saved generated image data within the application, allowing the user to visually confirm the terrain with the artificial objects removed.

[0092] Example 1

[0093] 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."

[0094] With conventional technology, artificial objects in landscape photographs often interfere with natural understanding of the terrain. This makes it difficult to obtain accurate and pure terrain information in fields such as disaster prevention, education, and historical research. Furthermore, no system exists for combining and analyzing multiple data sources. To solve this problem, a system is needed that can automatically remove artificial objects from images and generate realistic terrain information.

[0095] 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.

[0096] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring topographical data, server means for generating a topographical image from which artificial objects have been removed using an image generation AI model based on the acquired topographical data, and server means for transmitting the generated topographical image to the terminal means. This allows users to easily obtain topographical images from which artificial objects have been removed, enabling a more accurate understanding of topography in fields such as disaster prevention, education, and historical research.

[0097] "Location information" is data used to identify the location of an object, and specifically includes latitude and longitude information and direction information.

[0098] The "terminal means" is a device that is operated by a user to capture image data and acquire location information, and refers to a smartphone, tablet, or the like.

[0099] "Image data" is digital data that stores visual information of scenery or objects photographed by a terminal means.

[0100] The term "server means" refers to a computer system and its software for receiving data sent from the terminal means and analyzing and processing the data.

[0101] "Topographical data" refers to data relating to the elevation and geographical shape of a particular area, and is obtained from a map database or the like.

[0102] An "image generation AI model" refers to a program or algorithm that uses artificial intelligence to remove certain elements (such as man-made objects) from image data and generate realistic terrain images.

[0103] "Artificial objects" are objects made by humans that do not exist in the natural environment, including buildings, cars, and utility poles.

[0104] A "terrain image" is image data that represents the pure terrain, with artificial objects removed by a generative AI model.

[0105] This invention is a system that uses a smartphone (terminal), a server, an image generation AI model, and terrain data to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research.

[0106] Hardware and software used

[0107] Terminal

[0108] A user takes a photograph of a landscape using a smartphone. This device is equipped with a GPS function and a camera orientation sensor, and acquires latitude, longitude, and orientation information when taking a photograph. This allows the captured image data and location information to be temporarily stored on the device.

[0109] server

[0110] The server analyzes the received image data, latitude and longitude information, and direction information. Specifically, the server performs the following processes.

[0111] Based on the received latitude and longitude information, topographical data for the relevant area is obtained from a map database (e.g., elevation data).

[0112] The terrain is analyzed based on the acquired topographical data, image data, and directional information using terrain analysis software (analysis tools such as Python).

[0113] It uses an image generation AI model to detect and remove artifacts from the received image data, and this AI model operates based on deep learning techniques (using TensorFlow and PyTorch).

[0114] Processing flow

[0115] 1. User photography and data acquisition

[0116] The user takes a picture of a landscape using a smartphone. When taking the picture, the device acquires latitude and longitude information using the GPS function and direction information using the direction sensor, and these data are temporarily stored on the device along with the image data.

[0117] 2. Data transmission

[0118] The device sends the temporarily stored image data, latitude and longitude information, and direction information to the server. Data transmission uses a secure communication protocol.

[0119] 3. Analysis on the server

[0120] The server obtains topographical data for the relevant area based on the received data and performs analysis using analysis software.

[0121] 4. Artificial object removal using image generation AI

[0122] The server sends a prompt to the image generation AI model to generate a terrain image with artificial objects removed. For example, the prompt might say, "Please remove the artificial objects from this photo and generate a natural terrain image."

[0123] 5. Sending the generated image

[0124] The generated terrain image is sent to the user's device, which stores the received terrain image in local storage and displays it to the user.

[0125] Specific examples

[0126] Use case 1: Disaster prevention

[0127] Disaster prevention workers (users) take photos of disaster areas for earthquake risk assessment.

[0128] The smartphone (terminal) sends the image data, latitude and longitude information, and direction information after the photo is taken to the server.

[0129] Based on the received data, the server obtains topographical data for the relevant area and uses image generation AI to generate a topographical image that removes artificial objects.

[0130] The device displays the generated terrain image to the user and performs a risk assessment based on the natural topography of the affected area.

[0131] Use case 2: Education

[0132] A teacher (user) instructs students to acquire terrain images to study the natural terrain around the school.

[0133] Students take photos of the area around the school using their smartphones (devices) and send the image data and location information to the server.

[0134] The server obtains terrain data from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[0135] Students can view the generated terrain images on their smartphones, helping them understand the natural terrain around their school.

[0136] Use Case 3: Historical Research

[0137] Historical researchers (users) take contemporary photographs during fieldwork to investigate ancient battlefields.

[0138] Researchers send image data and location information to a server.

[0139] The server obtains topographical data based on the received data, and recreates past landscapes using old map data and image generation AI.

[0140] Researchers can view historical terrain images generated on smartphones and visually understand the specific terrain of the battlefields at the time.

[0141] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] The specific flow of this system's program processing

[0144] Step 1: User takes photos and acquires data

[0145] Operation: The user takes a landscape image using a smartphone (device).

[0146] Input: By pressing the capture button, image data captured by the camera, latitude and longitude information obtained by the GPS sensor, and direction information obtained by the direction sensor are collected.

[0147] Data processing: The collected image data and attached location information (latitude, longitude, and direction information) are stored in temporary storage.

[0148] Output: Image data and location information stored on the device.

[0149] Step 2: Sending data

[0150] Operation: The device sends the temporarily stored data to the server.

[0151] Input: Image data and location information saved in step 1.

[0152] Data processing: Image data and location information are sent to a server using the HTTPS protocol or similar.

[0153] Output: Data transferred to the server.

[0154] Step 3: Parsing on the server

[0155] Operation: Based on the received location information, the server obtains topographical data for the area and analyzes the image data.

[0156] Input: Received image data, latitude and longitude information, and direction information.

[0157] Data processing: The server retrieves terrain data based on location information from the map database and extracts terrain features using image analysis software (e.g., Python's OpenCV or GDAL).

[0158] Output: Topographic data and analyzed image data.

[0159] Step 4: Artifact removal using image generation AI

[0160] How it works: The server uses an image-generating AI model to remove artifacts from the images it receives.

[0161] Input: Analyzed image data, topographic data.

[0162] Data processing: The image generation AI model is sent a prompt such as, "Remove artificial objects from this photo and generate a natural-looking landscape image." The AI ​​model uses deep learning algorithms (e.g., TensorFlow or PyTorch) to detect and remove artificial objects.

[0163] Output: Terrain image with artifacts removed.

[0164] Step 5: Submit the generated image

[0165] Operation: The server sends the generated terrain image data to the user's terminal.

[0166] Input: Terrain image with artifacts removed.

[0167] Data processing: The generated topographical image data is sent to the user's device using the HTTPS protocol or similar.

[0168] Output: Generated terrain image saved and displayed on device.

[0169] (Application example 1)

[0170] 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."

[0171] Autonomous vehicles are required to more accurately grasp the landscape data around the vehicle and improve the accuracy of driver assistance systems. To achieve this, pure terrain data that excludes artificial objects is required. However, with current technology, much of the landscape data contains artificial objects, making it difficult to obtain accurate terrain data. This affects the performance of driver assistance systems and poses safety challenges.

[0172] 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.

[0173] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring elevation data, server means for generating a terrain image from which artificial objects have been removed using an image generation model based on the acquired elevation data, server means for transmitting the generated terrain image to the terminal means, and means for displaying the generated terrain image in a driving assistance system of the vehicle. This enables an autonomous vehicle to acquire accurate terrain data from which artificial objects have been removed, thereby improving the accuracy of the driving assistance system.

[0174] Key Word Definitions

[0175] "Location information" is data such as latitude and longitude that indicates the geographic location of an object.

[0176] "Image data" refers to visual information captured by a camera or other imaging device, and that information is stored in digital form.

[0177] "Terminal means" refers to a mobile device such as a smartphone, tablet, or camera, which is operated by a user to obtain location information and image data.

[0178] The "server means" is a computer system for processing and storing data, and receives and processes information sent from the terminal means via a network.

[0179] "Elevation data" is data that indicates information about the height of the earth's surface and is used in analyzing terrain.

[0180] An "image generation model" is an algorithm that uses machine learning and artificial intelligence techniques to generate a new image by eliminating certain elements (such as artificial objects) from an input image.

[0181] "Artifacts" refer to objects such as buildings and vehicles that are created by humans in the natural environment, and are the objects that can be identified in image data.

[0182] A "driving assistance system" is a system that supports automatic driving of a vehicle and the driver, and has the function of recognizing the environment around the vehicle in real time and assisting in appropriate operation.

[0183] MODE FOR CARRYING OUT THE INVENTION

[0184] To implement this invention, it is necessary to build a system according to the following steps.

[0185] 1. Hardware and Software Used

[0186] Hardware:

[0187] Smartphone or vehicle-mounted camera: Used to capture image data.

[0188] GPS sensor on your smartphone or vehicle: Obtains location information.

[0189] Server: Analyzes the received data and generates images.

[0190] software:

[0191] Azure Elevation Data API: Used to retrieve elevation data.

[0192] OpenAI API: Generates terrain images that remove artificial objects using an image generation model.

[0193] Driver assistance system for autonomous vehicles: Displays the generated terrain images to assist vehicle driving.

[0194] 2. System Overview

[0195] The user's smartphone or in-vehicle camera captures the scenery around the vehicle and acquires the image data and location information (latitude, longitude, and direction information). This data is temporarily stored in the device.

[0196] The device then sends the saved image data and location information to the server, which then retrieves elevation data for the area based on the received latitude and longitude information using the Azure elevation data API.

[0197] Based on the acquired elevation data and image data, the server uses OpenAI's image generation model to generate a terrain image that detects and removes artificial objects in the image data. During this generation process, the following prompts are used:

[0198] Prompt Sentence Examples

[0199] Please remove any artificial objects from this image to recreate a purely natural terrain. The latitude of this image is 35.6895 and the longitude is 139.6917. Please remove any artificial buildings or cars while preserving the current terrain.

[0200] The generated terrain image is then sent back to the terminal from the server. Finally, the terminal displays the received terrain image on the driver assistance system of the autonomous vehicle, enabling the vehicle's driver assistance system to support vehicle operation based on accurate terrain data with artificial objects removed.

[0201] 3. Specific Examples

[0202] For example, when a user drives an autonomous vehicle in the streets of Tokyo, the onboard camera captures the surrounding scenery, and the GPS sensor acquires latitude and longitude information. This information is sent to a server, which then uses the Azure elevation data API to collect elevation data. The collected data and captured images are then converted into a pure terrain image using OpenAI's image generation model, eliminating artifacts. The generated image is then displayed on the driver assistance system, providing more accurate driving assistance.

[0203] In this way, the system of the present invention plays an important role in enabling autonomous vehicles to more accurately grasp the surrounding environment and achieve safe driving.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Program processing flow

[0206] Step 1: User takes photos and acquires data

[0207] The user takes a photo of the surrounding scenery using their smartphone or in-car camera. When taking a photo, the smartphone or vehicle's GPS sensor acquires latitude and longitude information, and the camera's orientation sensor acquires direction information. The captured image and location information are then temporarily saved on the device.

[0208] Input: A photographed landscape image, latitude and longitude information, and direction information.

[0209] Output: Image data and location information stored on the device.

[0210] Step 2: Sending data

[0211] The terminal transmits the temporarily saved image data, latitude and longitude information, and direction information to the server, and when transmission is complete, waits for a response from the server.

[0212] Input: Image data and location information stored on the device.

[0213] Output: Image data and location information sent to the server.

[0214] Step 3: Parsing on the server

[0215] The server obtains elevation data for the area based on the received latitude and longitude information, using the Azure elevation data API for this process.

[0216] Input: Received latitude and longitude information.

[0217] Output: The retrieved elevation data.

[0218] Step 4: Artifact removal using image generation AI

[0219] The server uses the received image data and elevation data to input prompt statements into OpenAI's image generation model, generating a terrain image that removes artificial objects from the image data.

[0220] Input: Received image data and elevation data, prompt statement.

[0221] Output: Generated terrain image.

[0222] Specific operation:

[0223] 1. Insert a prompt such as "Remove any man-made objects from this image to recreate a purely natural terrain. The image is at latitude 35.6895 and longitude 139.6917. Please remove any man-made buildings or vehicles while preserving the current terrain."

[0224] 2. Send the data to OpenAI's API and perform the artifact removal process.

[0225] Step 5: Submit the generated image

[0226] The server then transmits the generated topographical image data to the user's terminal, checking the integrity of the data during transmission.

[0227] Input: Generated terrain image data.

[0228] Output: Terrain image data sent to the device.

[0229] Step 6: Display by driver assistance system

[0230] The terminal receives terrain image data from the server and displays it on the driver assistance system of the autonomous vehicle, allowing the vehicle to provide driving assistance based on accurate terrain data that has been stripped of artificial objects.

[0231] Input: Terrain image data sent to the device.

[0232] Output: Terrain image data displayed on the driver assistance system.

[0233] This series of processes allows for the acquisition of terrain images with artificial objects removed, which can then be displayed on the driver assistance system of an autonomous vehicle, enabling safer and more accurate driving assistance.

[0234] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0235] overview

[0236] This system uses a smartphone (terminal), a server, image generation AI, elevation data, and an emotion engine to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research. It also provides flexible visual information that takes into account the user's emotional state.

[0237] Program processing overview

[0238] Step 1: User photography and data acquisition

[0239] The user takes a picture of the scenery using a smartphone. When taking a picture, the smartphone uses its GPS function to obtain latitude and longitude information, and also obtains direction information from the camera's direction sensor. The captured image and location information are then temporarily saved on the device.

[0240] Step 2: Obtaining emotion data

[0241] The smartphone's built-in emotion engine analyzes the user's facial expressions through the camera or performs voice analysis to obtain the user's emotion data, which is also temporarily stored on the device.

[0242] Step 3: Send the data

[0243] The terminal establishes communication for transmitting the acquired image data, latitude and longitude information, direction information, and emotion data to the server. After the transmission is completed, the terminal waits for a response from the server.

[0244] Step 4: Parsing on the server

[0245] The server obtains elevation data for the area based on the received latitude and longitude information, and prepares to analyze the terrain based on the obtained elevation data, image data, and direction information.

[0246] Step 5: Artifact removal using image generation AI

[0247] The server inputs the received image data into the image generation AI to detect man-made objects (buildings, roads, power lines, etc.). The image generation AI removes the detected man-made objects and generates a terrain image without the man-made objects. This generated terrain image is temporarily stored on the server.

[0248] Step 6: Optimize based on user sentiment

[0249] The server analyzes the received user emotion data and selects the most appropriate display format for the terrain image according to the user's emotional state. This optimization process allows the user to receive information more comfortably.

[0250] Step 7: Submit the generated image

[0251] The server prepares to send the generated topographical image data to the user's device. This transmission is also performed via the Internet. The server confirms the completion of the transmission and records the operation log.

[0252] Step 8: Displaying the generated image

[0253] The device stores the generated image data received from the server in local storage. The stored generated image data is displayed within the application, allowing the user to visually confirm the terrain with no artificial objects removed. The display format is optimized based on emotion data, allowing the user to view the information without stress.

[0254] Specific examples

[0255] Use case 1: Disaster prevention

[0256] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[0257] Device: The smartphone sends the captured image data, latitude and longitude information, direction information, and emotion data to the server.

[0258] Server: Based on the received data, the server obtains elevation data for the relevant area from an elevation database and uses image generation AI to generate a terrain image that removes artificial objects.

[0259] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[0260] Use case 2: Education

[0261] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[0262] Device: Students take photos of the mountain behind the school using their smartphones and send the image data, location information, and emotion data to the server.

[0263] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[0264] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[0265] Use Case 3: Historical Research

[0266] User: A historian conducts fieldwork at a site known as a battlefield from the Sengoku period.

[0267] Terminal: The researcher takes a modern photograph and sends the image data, location information, and emotional data to the server.

[0268] Server: Elevation data is obtained based on the received data, and the landscape at that time is recreated using old map data and image generation AI.

[0269] Terminal: Images of the past terrain are sent to the researcher's smartphone, allowing them to visually understand the specific terrain of the battlefield at that time.

[0270] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research, and also provides flexible information that responds to the user's emotional state.

[0271] The processing flow will be explained below.

[0272] Step 1:

[0273] A user starts the camera application on their smartphone and takes a picture of a target scene. After taking the picture, the camera application acquires the image data.

[0274] Step 2:

[0275] The device acquires the latitude and longitude information of its current location using the built-in GPS module, and also acquires direction information from the camera's direction sensor.

[0276] Step 3:

[0277] The device temporarily stores the acquired image data, latitude and longitude information, and direction information in its internal storage.

[0278] Step 4:

[0279] The emotion engine installed on the device analyzes the user's facial expressions through the camera or performs voice analysis to obtain the user's emotional data, which is also temporarily stored in the device's internal storage.

[0280] Step 5:

[0281] The device establishes communication to transmit the stored image data, latitude and longitude information, orientation information, and emotion data to a server, typically via the Internet.

[0282] Step 6:

[0283] The device transmits image data, latitude and longitude information, direction information, and emotion data to the server. After transmission is complete, the device waits for a response from the server.

[0284] Step 7:

[0285] Based on the received latitude and longitude information, the server retrieves the elevation data for the relevant area from the elevation database.

[0286] Step 8:

[0287] The server uses the received image data, elevation data, and orientation information to prepare for analyzing the topography of the target area.

[0288] Step 9:

[0289] The server inputs the image data into the image generation AI, which then detects artificial objects (buildings, roads, power lines, etc.) in the image.

[0290] Step 10:

[0291] The image generation AI removes any detected artificial objects and generates a terrain image without any artificial objects. This generated terrain image is temporarily stored on the server.

[0292] Step 11:

[0293] The server analyzes the received user emotional data and selects the optimal display format for the terrain image according to the user's emotional state. As a result of the emotional analysis, the display is customized according to the user's stress and interests.

[0294] Step 12:

[0295] The server prepares to send the generated topographical image data to the user's device, which is also sent via the Internet.

[0296] Step 13:

[0297] The server sends the generated terrain image to the terminal, confirms the completion of the transmission, and records the operation log.

[0298] Step 14:

[0299] The terminal stores the generated image data received from the server in local storage.

[0300] Step 15:

[0301] The device displays the saved generated image data within the application, allowing the user to visually confirm the terrain with no artificial objects removed. The display format is optimized based on the emotion data, allowing the user to view the information without stress.

[0302] Example 2

[0303] 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."

[0304] Current image processing technology has difficulty in removing artificial objects from landscape images and reproducing natural topography. It also lacks the ability to provide optimal information display formats based on the user's emotional state. As a result, it is difficult to provide appropriate information to deepen understanding of topography in fields such as disaster prevention, education, and historical research.

[0305] 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.

[0306] In this invention, the server includes terminal means for acquiring location data, terminal means for capturing at least one image data, server means for receiving the location data and image data transmitted from the terminal means and acquiring terrain data, server means for generating a terrain image from which artificial objects have been removed using a generation AI based on the acquired terrain data, server means for analyzing the user's emotional data and selecting an optimal information display format according to the user's emotional state, and server means for transmitting the generated terrain image to the terminal means. This not only makes it possible to reproduce a natural terrain from which artificial objects have been removed, but also enables flexible information provision according to the user's emotional state.

[0307] "Location data" is information for identifying a location, and typically includes coordinate information and direction information.

[0308] "Terminal means" refers to a device used by a user, specifically a smartphone, tablet, etc.

[0309] "Image data" refers to digital data of photographs or videos taken using a terminal means.

[0310] "Server Means" refers to a central server that receives, analyzes, processes and transmits data.

[0311] "Terrain Data" refers to data relating to terrain, such as elevation information for a particular geographic area.

[0312] "Generative AI" refers to artificial intelligence algorithms and models that process image data to generate new images.

[0313] "Man-made objects" refers to man-made structures that are not natural elements, such as buildings, roads, or power lines.

[0314] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and the like.

[0315] "Information presentation format" refers to the visual or auditory format of information presented to a user.

[0316] "Image generation means" refers to the technology or algorithm used to detect and edit specific objects from image data.

[0317] MODE FOR CARRYING OUT THE INVENTION

[0318] System Configuration

[0319] This invention is a system that includes a user terminal, a central server, an image generation AI, a terrain database, and an emotion analysis engine. The purpose of this system is to generate terrain images that are free of artificial objects and provide them in an optimal information display format according to the user's emotional state.

[0320] Hardware Configuration

[0321] The terminals used are mobile devices such as smartphones and tablets, which must be equipped with a GPS module, a camera, a direction sensor, and a front-facing camera and microphone for emotion analysis.

[0322] Servers use high-performance computer systems to receive, analyze, process, and transmit data, and require large amounts of storage, powerful CPUs and GPUs, and network communication capabilities.

[0323] Software Configuration

[0324] The device comes with an application that integrates a camera app, GPS function, orientation sensor function, and emotion analysis engine. For example, OpenCV and TensorFlow Lite can be used for image processing.

[0325] On the server side, the image analysis system is built using programming languages ​​such as Python and Java, and uses an API to reference the terrain database and generative AI (e.g., Stable Diffusion and GAN models) as an image generation AI model.

[0326] Operation overview

[0327] The user takes a photo of the target scene using their smartphone. This acquires image data, latitude and longitude information from GPS, and camera direction information. Furthermore, emotional data is analyzed from the user's facial expressions and voice, and temporarily saved on the device.

[0328] The device then compiles this data and sends it to a central server. The server then consults an elevation database based on the received latitude and longitude information to obtain topographical data for the relevant area. Based on this data, an image generation AI generates a topographical image that eliminates artificial objects. The AI ​​also analyzes the user's emotional data and selects the optimal information display format based on their emotional state.

[0329] The server then sends the generated terrain image to the terminal, which then displays it, allowing the user to view a natural terrain image with no artificial objects present.

[0330] Specific examples

[0331] Use case 1: Disaster prevention

[0332] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[0333] Device: The smartphone sends the captured image data, latitude and longitude information, direction information, and emotion data to the server.

[0334] Server: Based on the received data, the server obtains topographical data for the relevant area from an elevation database, and uses image generation AI to generate a topographical image that removes artificial objects.

[0335] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[0336] Use case 2: Education

[0337] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[0338] Device: Students take photos of the mountain behind the school using their smartphones and send the image data, location information, and emotion data to the server.

[0339] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[0340] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[0341] Prompt statement

[0342] Here are some example prompts for the image generation AI:

[0343] "Detect and remove man-made objects such as buildings and power lines from this landscape to generate a natural-looking terrain image."

[0344] An example prompt for the sentiment analysis engine is:

[0345] "Analyze the user's facial expressions to determine their current emotional state."

[0346] As explained above, the present invention provides practical value in fields such as disaster prevention, education, and historical research, and also realizes flexible information provision according to the user's emotional state.

[0347] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0348] Step 1:

[0349] The user uses a smartphone to capture the scenery they want to shoot. When taking the photo, the device uses its GPS function to obtain latitude and longitude information, and obtains orientation information from the camera's orientation sensor. This provides image data, latitude and longitude information, and orientation information as input. This data is temporarily stored within the device. Specifically, the user launches the smartphone's camera app and takes a photo of the scenery in the specified area, at the same time automatically activating the GPS function and orientation sensor to obtain the necessary data.

[0350] Step 2:

[0351] After capturing the image, the device activates its emotion engine and analyzes the user's facial expressions through the camera. If necessary, it records the user's voice and performs audio analysis. This provides input data on the user's facial expressions and voice, which are then used to generate emotion data. This emotion data is temporarily stored within the device. Specifically, the emotion engine captures the user's facial expressions, and the emotion analysis algorithm analyzes them to determine the user's emotional state.

[0352] Step 3:

[0353] The device establishes communication to send the acquired image data, latitude and longitude information, direction information, and emotion data to the server. Once data transmission is complete, it waits for a response from the server. Data stored in the device as input is sent to the server, which triggers the server to receive the data. Specifically, the device connects to the server using Wi-Fi or mobile data communication and sends all data at once.

[0354] Step 4:

[0355] The server retrieves elevation data for the relevant area from the terrain database based on the received latitude and longitude information. The latitude and longitude information is provided as input, and terrain data is retrieved based on that. The server then prepares to analyze the terrain based on this data. Specifically, the server calls the elevation database API to retrieve the necessary terrain data.

[0356] Step 5:

[0357] The server inputs the received image data into the generation AI, which then processes it to detect and remove man-made objects (buildings, roads, power lines, etc.). Image data is provided as input, and the generation AI analyzes it to generate a terrain image with the man-made objects removed. This generated terrain image is temporarily stored on the server. Specifically, the server sends a prompt to the image generation AI saying, "Please detect and remove man-made objects such as buildings and power lines from this landscape and generate a natural-looking terrain image."

[0358] Step 6:

[0359] The server analyzes the received user emotion data and selects the optimal display format for the terrain image based on the user's emotional state. The emotion data is provided as input, and the display format is selected based on that. Specifically, the server applies an emotion data analysis algorithm to determine the optimal display format based on the user's emotional state.

[0360] Step 7:

[0361] The server prepares to send the generated terrain image data to the user's device. When the data transmission is complete, an operation log is recorded. The generated terrain image data is provided as input, and this data is sent to the user's device. Specifically, the server formats the image data into transmission packets and sends them to the terminal via the Internet.

[0362] Step 8:

[0363] The device saves the generated image data received from the server in local storage. The saved generated image data is displayed in the application so that the user can check it. The image data received from the server is provided as input and displayed in the application. Specifically, the device decompresses the data received from the server and saves it in local storage, and the application reads and displays the data.

[0364] (Application example 2)

[0365] 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."

[0366] Conventional food delivery systems have the problem of being unable to flexibly respond to obstacles and temporary traffic restrictions that delivery workers encounter on their routes. Furthermore, routes are provided without taking into account the emotional state of delivery workers, which can increase stress and reduce work efficiency. Furthermore, conventional systems do not adequately consider the topography of the delivery route, nor do they adequately optimize the safety and comfort of delivery work.

[0367] 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.

[0368] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring elevation data, server means for generating a terrain image from which artificial objects have been removed using an image generation AI based on the acquired elevation data, server means for transmitting the generated terrain image to the terminal means, and server means for receiving emotion data acquired by the terminal means and selecting an optimal terrain image display format based on the emotion data. This enables delivery personnel to deliver goods along safe and efficient routes that take into account their emotional state as well as the latest terrain information.

[0369] "Location information" is information that indicates the geographical location where a target terminal or object is currently located, and typically includes latitude, longitude, and direction information.

[0370] "Terminal means" refers to a device that has the function of acquiring location information, image data, emotional data, etc. and transmitting it to a server.

[0371] "Image data" is a digital representation of visual information captured by a photographic device such as a camera.

[0372] "Elevation data" is geographical data that indicates the height of a specific location above the ground surface, and is used in topographical analysis.

[0373] "Server Means" refers to a system that has the function of receiving, processing, and transmitting data through the cloud or a network.

[0374] "Image generation AI" is an algorithm or system that uses artificial intelligence technology to analyze input images and generate new images.

[0375] "Emotion data" is digital information that indicates the user's emotional state, as analyzed from the user's facial expressions, voice, etc.

[0376] "Terrain image display format" refers to the layout and visual style when displaying the generated terrain image to the user.

[0377] This invention provides a system for food delivery businesses that enables delivery personnel to deliver food along safe and efficient routes. The system is composed of a smartphone, a server, an elevation database, an image generation AI, and an emotion engine.

[0378] First, the delivery person uses their smartphone to take a photo of the scenery along their current delivery route. The smartphone uses its GPS to obtain latitude and longitude information and direction information from the camera's orientation sensor. The smartphone's built-in emotion engine also analyzes the delivery person's facial expressions through the camera to obtain emotional data. This data is temporarily stored on the device.

[0379] Next, the device transmits the acquired image data, latitude and longitude information, direction information, and emotion data to the server. The server acquires elevation data for the relevant area based on the received latitude and longitude information, and uses image generation AI to generate a terrain image that removes artificial objects (buildings, roads, power lines, etc.). This terrain image is temporarily stored on the server.

[0380] Furthermore, the server analyzes the received emotional data and selects the optimal terrain image display format based on the delivery person's emotional state, allowing the delivery person to receive route information more comfortably. After this optimization process, the generated terrain image data is sent to the delivery person's device. The device then stores the received generated image data in local storage, allowing the delivery person to visually confirm the terrain with no artificial objects removed while understanding the delivery route.

[0381] Specifically, the following hardware and software are used:

[0382] Smartphone: Sensors include GPS, orientation sensor, camera, and emotion engine (e.g., Affectiva's SDK)

[0383] Server: Terrain database and elevation database as databases, GAN (Generative Adversarial Network) model as image generation AI, Python library (TensorFlow, OpenCV, etc.) as analysis engine

[0384] Specific examples

[0385] When a delivery person is driving through an area in the city center where road construction and temporary traffic restrictions are frequent, the delivery person selects an avoidance route based on the latest artificial object-free terrain image that reflects the situation.

[0386] Prompt Sentence Examples

[0387] "Create a system that allows food delivery riders to take photos of their latest delivery route in Roppongi, Minato-ku, Tokyo with their smartphones, and then uses the photos to reconstruct natural terrain information by removing artificial objects, and then generates and displays the optimal delivery route to reduce stress."

[0388] This system enables delivery personnel to carry out safe and efficient deliveries based on up-to-date terrain information and thoughtful route information.

[0389] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0390] Step 1:

[0391] The delivery person uses a device (smartphone) to take photos of the local scenery.

[0392] —Input: landscape image, GPS location, direction

[0393] —Operation: The device camera captures a landscape image, while the GPS sensor acquires latitude and longitude information and the orientation sensor acquires orientation information. These data are temporarily stored in local storage.

[0394] —Output: Landscape image data, latitude and longitude information, direction information

[0395] Step 2:

[0396] The device (smartphone) analyzes the delivery person's facial expressions using an emotion engine to obtain emotional data.

[0397] —Input: Facial expression of delivery person (camera image), voice data (if necessary)

[0398] - Operation: The emotion engine (e.g., Affectiva's SDK) installed on the device analyzes the delivery person's facial expressions through the camera and obtains their emotional state as numerical data.

[0399] —Output: Emotion data

[0400] Step 3:

[0401] The terminal (smartphone) transmits the acquired image data, latitude and longitude information, direction information, and emotion data to the server.

[0402] —Input: Image data, latitude and longitude information, direction information, emotion data

[0403] Operation: The device establishes communication with the server via the Internet, sends the above information to the server, and then waits for a response from the server.

[0404] —Output: Various data sent to the server

[0405] Step 4:

[0406] The server obtains elevation data for the relevant area based on the received latitude and longitude information.

[0407] —Input: Latitude and longitude information

[0408] Operation: The server accesses the terrain database and the elevation database to obtain the elevation data corresponding to the relevant latitude and longitude information.

[0409] —Output: Elevation data

[0410] Step 5:

[0411] The server uses image generation AI to generate a terrain image that removes artificial objects.

[0412] —Input: Image data, elevation data, orientation information

[0413] Operation: The server inputs the received image data into an image generation AI (such as a GAN model) to detect and remove artificial objects. As a result, a terrain image without artificial objects is generated.

[0414] —Output: Terrain image with artifacts removed

[0415] Step 6:

[0416] The server selects the optimum display format for the terrain image based on the emotion data.

[0417] —Input: emotion data, terrain image

[0418] - Operation: The server analyzes the emotional data and selects the display format that allows the user to receive information most comfortably. The display format may include color filters, contrast adjustment, etc.

[0419] —Output: Optimized terrain image display format

[0420] Step 7:

[0421] The server transmits the generated topographical image data to the terminal.

[0422] —Input: Optimized terrain image

[0423] - Operation: The server sends the generated topographical image data to the terminal. After the transmission is complete, the operation log is recorded.

[0424] —Output: Terrain image data sent to the device

[0425] Step 8:

[0426] The terminal displays the generated image data received from the server.

[0427] —Input: Generated image data

[0428] Operation: The device receives terrain image data from the server, stores it in local storage, and displays it through the application. The delivery person visually checks the terrain, free of artificial objects, and delivers along a safe and efficient route.

[0429] —Output: Terrain image displayed on the terminal

[0430] 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.

[0431] 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.

[0432] 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.

[0433] [Second embodiment]

[0434] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0445] 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."

[0446] overview

[0447] This invention is a system that uses a smartphone (terminal), a server, image generation AI, and elevation data to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research.

[0448] Program processing overview

[0449] Step 1: User photography and data acquisition

[0450] The user takes a picture of the scenery using a smartphone. When taking a picture, the smartphone uses its GPS function to obtain latitude and longitude information, and also obtains direction information from the camera's direction sensor. The captured image and location information are then temporarily saved on the device.

[0451] Step 2: Send the data

[0452] The terminal transmits the temporarily saved image data, latitude and longitude information, and direction information to the server, and when transmission is complete, waits for a response from the server.

[0453] Step 3: Parsing on the server

[0454] The server obtains elevation data for the area based on the received latitude and longitude information, and performs a topographical analysis based on the obtained elevation data, image data, and direction information.

[0455] Step 4: Artifact removal using image generation AI

[0456] The server inputs the received image data into the image generation AI to detect man-made objects. The image generation AI then removes the detected man-made objects and generates a realistic terrain image. The generated terrain image is temporarily stored on the server.

[0457] Step 5: Submit the generated image

[0458] The server transmits the generated terrain image data to the user's terminal, where it is stored and displayed to the user.

[0459] Specific examples

[0460] Use case 1: Disaster prevention

[0461] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[0462] Terminal: The smartphone sends the captured image data, latitude and longitude information, and direction information to the server.

[0463] Server: Based on the received data, the server obtains elevation data for the relevant area from an elevation database and uses image generation AI to generate a terrain image that removes artificial objects.

[0464] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[0465] Use case 2: Education

[0466] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[0467] Device: Students take photos of the mountain behind the school using their smartphones and send the image data and location information to the server.

[0468] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[0469] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[0470] Use Case 3: Historical Research

[0471] User: A historian conducts fieldwork at a site known as a battlefield from the Sengoku period.

[0472] Terminal: The researcher takes a contemporary photograph and sends the image data and location information to the server.

[0473] Server: Elevation data is obtained based on the received data, and the landscape at that time is recreated using old map data and image generation AI.

[0474] Terminal: Images of the past terrain are sent to the researcher's smartphone, allowing them to visually understand the specific terrain of the battlefield at that time.

[0475] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research.

[0476] The processing flow will be explained below.

[0477] Step 1:

[0478] A user starts the camera application on their smartphone and takes a picture of a target scene. After taking the picture, the camera application acquires the image data.

[0479] Step 2:

[0480] The device acquires the latitude and longitude information of its current location using the built-in GPS module, and also acquires direction information from the camera's direction sensor.

[0481] Step 3:

[0482] The device temporarily stores the acquired image data, latitude and longitude information, and direction information in its internal storage.

[0483] Step 4:

[0484] The device establishes a connection to send the stored data to a server, which typically occurs over the Internet.

[0485] Step 5:

[0486] The terminal transmits the image data, latitude and longitude information, and direction information to the server. After the transmission is complete, the terminal waits for a response from the server.

[0487] Step 6:

[0488] Based on the received latitude and longitude information, the server retrieves the elevation data for the relevant area from the elevation database.

[0489] Step 7:

[0490] The server uses the received image data, elevation data, and direction information to prepare for analyzing the topography of the target area.

[0491] Step 8:

[0492] The server inputs the image data into the image generation AI, which then detects artificial objects (buildings, roads, power lines, etc.) in the image.

[0493] Step 9:

[0494] The image generation AI removes any detected artificial objects and generates a terrain image without any artificial objects. This generated terrain image is temporarily stored on the server.

[0495] Step 10:

[0496] The server prepares to send the generated topographical image data to the user's terminal, which is also sent via the Internet.

[0497] Step 11:

[0498] The server sends the generated terrain image to the terminal, confirms the completion of the transmission, and records the operation log.

[0499] Step 12:

[0500] The terminal stores the generated image data received from the server in local storage.

[0501] Step 13:

[0502] The device displays the saved generated image data within the application, allowing the user to visually confirm the terrain with the artificial objects removed.

[0503] Example 1

[0504] 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."

[0505] With conventional technology, artificial objects in landscape photographs often interfere with natural understanding of the terrain. This makes it difficult to obtain accurate and pure terrain information in fields such as disaster prevention, education, and historical research. Furthermore, no system exists for combining and analyzing multiple data sources. To solve this problem, a system is needed that can automatically remove artificial objects from images and generate realistic terrain information.

[0506] 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.

[0507] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring topographical data, server means for generating a topographical image from which artificial objects have been removed using an image generation AI model based on the acquired topographical data, and server means for transmitting the generated topographical image to the terminal means. This allows users to easily obtain topographical images from which artificial objects have been removed, enabling a more accurate understanding of topography in fields such as disaster prevention, education, and historical research.

[0508] "Location information" is data used to identify the location of an object, and specifically includes latitude and longitude information and direction information.

[0509] The "terminal means" is a device that is operated by a user to capture image data and acquire location information, and refers to a smartphone, tablet, or the like.

[0510] "Image data" is digital data that stores visual information of scenery or objects photographed by a terminal means.

[0511] The term "server means" refers to a computer system and its software for receiving data sent from the terminal means and analyzing and processing the data.

[0512] "Topographical data" refers to data relating to the elevation and geographical shape of a particular area, and is obtained from a map database or the like.

[0513] An "image generation AI model" refers to a program or algorithm that uses artificial intelligence to remove certain elements (such as man-made objects) from image data and generate realistic terrain images.

[0514] "Artificial objects" are objects made by humans that do not exist in the natural environment, including buildings, cars, and utility poles.

[0515] A "terrain image" is image data that represents the pure terrain, with artificial objects removed by a generative AI model.

[0516] This invention is a system that uses a smartphone (terminal), a server, an image generation AI model, and terrain data to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research.

[0517] Hardware and software used

[0518] Terminal

[0519] A user takes a photograph of a landscape using a smartphone. This device is equipped with a GPS function and a camera orientation sensor, and acquires latitude, longitude, and orientation information when taking a photograph. This allows the captured image data and location information to be temporarily stored on the device.

[0520] server

[0521] The server analyzes the received image data, latitude and longitude information, and direction information. Specifically, the server performs the following processes.

[0522] Based on the received latitude and longitude information, topographical data for the relevant area is obtained from a map database (e.g., elevation data).

[0523] The terrain is analyzed based on the acquired topographical data, image data, and directional information using terrain analysis software (analysis tools such as Python).

[0524] It uses an image generation AI model to detect and remove artifacts from the received image data, and this AI model operates based on deep learning techniques (using TensorFlow and PyTorch).

[0525] Processing flow

[0526] 1. User photography and data acquisition

[0527] The user takes a picture of a landscape using a smartphone. When taking the picture, the device acquires latitude and longitude information using the GPS function and direction information using the direction sensor, and these data are temporarily stored on the device along with the image data.

[0528] 2. Data transmission

[0529] The device sends the temporarily stored image data, latitude and longitude information, and direction information to the server. Data transmission uses a secure communication protocol.

[0530] 3. Analysis on the server

[0531] The server obtains topographical data for the relevant area based on the received data and performs analysis using analysis software.

[0532] 4. Artificial object removal using image generation AI

[0533] The server sends a prompt to the image generation AI model to generate a terrain image with artificial objects removed. For example, the prompt might say, "Please remove the artificial objects from this photo and generate a natural terrain image."

[0534] 5. Sending the generated image

[0535] The generated terrain image is sent to the user's device, which stores the received terrain image in local storage and displays it to the user.

[0536] Specific examples

[0537] Use case 1: Disaster prevention

[0538] Disaster prevention workers (users) take photos of disaster areas for earthquake risk assessment.

[0539] The smartphone (terminal) sends the image data, latitude and longitude information, and direction information after the photo is taken to the server.

[0540] Based on the received data, the server obtains topographical data for the relevant area and uses image generation AI to generate a topographical image that removes artificial objects.

[0541] The device displays the generated terrain image to the user and performs a risk assessment based on the natural topography of the affected area.

[0542] Use case 2: Education

[0543] A teacher (user) instructs students to acquire terrain images to study the natural terrain around the school.

[0544] Students take photos of the area around the school using their smartphones (devices) and send the image data and location information to the server.

[0545] The server obtains terrain data from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[0546] Students can view the generated terrain images on their smartphones, helping them understand the natural terrain around their school.

[0547] Use Case 3: Historical Research

[0548] Historical researchers (users) take contemporary photographs during fieldwork to investigate ancient battlefields.

[0549] Researchers send image data and location information to a server.

[0550] The server obtains topographical data based on the received data, and recreates past landscapes using old map data and image generation AI.

[0551] Researchers can view historical terrain images generated on smartphones and visually understand the specific terrain of the battlefields at the time.

[0552] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research.

[0553] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0554] The specific flow of this system's program processing

[0555] Step 1: User takes photos and acquires data

[0556] Operation: The user takes a landscape image using a smartphone (device).

[0557] Input: By pressing the capture button, image data captured by the camera, latitude and longitude information obtained by the GPS sensor, and direction information obtained by the direction sensor are collected.

[0558] Data processing: The collected image data and attached location information (latitude, longitude, and direction information) are stored in temporary storage.

[0559] Output: Image data and location information stored on the device.

[0560] Step 2: Sending data

[0561] Operation: The device sends the temporarily stored data to the server.

[0562] Input: Image data and location information saved in step 1.

[0563] Data processing: Image data and location information are sent to a server using the HTTPS protocol or similar.

[0564] Output: Data transferred to the server.

[0565] Step 3: Parsing on the server

[0566] Operation: Based on the received location information, the server obtains topographical data for the area and analyzes the image data.

[0567] Input: Received image data, latitude and longitude information, and direction information.

[0568] Data processing: The server retrieves terrain data based on location information from the map database and extracts terrain features using image analysis software (e.g., Python's OpenCV or GDAL).

[0569] Output: Topographic data and analyzed image data.

[0570] Step 4: Artifact removal using image generation AI

[0571] How it works: The server uses an image-generating AI model to remove artifacts from the images it receives.

[0572] Input: Analyzed image data, topographic data.

[0573] Data processing: The image generation AI model is sent a prompt such as, "Remove artificial objects from this photo and generate a natural-looking landscape image." The AI ​​model uses deep learning algorithms (e.g., TensorFlow or PyTorch) to detect and remove artificial objects.

[0574] Output: Terrain image with artifacts removed.

[0575] Step 5: Submit the generated image

[0576] Operation: The server sends the generated terrain image data to the user's terminal.

[0577] Input: Terrain image with artifacts removed.

[0578] Data processing: The generated topographical image data is sent to the user's device using the HTTPS protocol or similar.

[0579] Output: Generated terrain image saved and displayed on device.

[0580] (Application example 1)

[0581] 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."

[0582] Autonomous vehicles are required to more accurately grasp the landscape data around the vehicle and improve the accuracy of driver assistance systems. To achieve this, pure terrain data that excludes artificial objects is required. However, with current technology, much of the landscape data contains artificial objects, making it difficult to obtain accurate terrain data. This affects the performance of driver assistance systems and poses safety challenges.

[0583] 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.

[0584] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring elevation data, server means for generating a terrain image from which artificial objects have been removed using an image generation model based on the acquired elevation data, server means for transmitting the generated terrain image to the terminal means, and means for displaying the generated terrain image in a driving assistance system of the vehicle. This enables an autonomous vehicle to acquire accurate terrain data from which artificial objects have been removed, thereby improving the accuracy of the driving assistance system.

[0585] Key Word Definitions

[0586] "Location information" is data such as latitude and longitude that indicates the geographic location of an object.

[0587] "Image data" refers to visual information captured by a camera or other imaging device, and that information is stored in digital form.

[0588] "Terminal means" refers to a mobile device such as a smartphone, tablet, or camera, which is operated by a user to obtain location information and image data.

[0589] The "server means" is a computer system for processing and storing data, and receives and processes information sent from the terminal means via a network.

[0590] "Elevation data" is data that indicates information about the height of the earth's surface and is used in analyzing terrain.

[0591] An "image generation model" is an algorithm that uses machine learning and artificial intelligence techniques to generate a new image by eliminating certain elements (such as artificial objects) from an input image.

[0592] "Artifacts" refer to objects such as buildings and vehicles that are created by humans in the natural environment, and are the objects that can be identified in image data.

[0593] A "driving assistance system" is a system that supports automatic driving of a vehicle and the driver, and has the function of recognizing the environment around the vehicle in real time and assisting in appropriate operation.

[0594] MODE FOR CARRYING OUT THE INVENTION

[0595] To implement this invention, it is necessary to build a system according to the following steps.

[0596] 1. Hardware and Software Used

[0597] Hardware:

[0598] Smartphone or vehicle-mounted camera: Used to capture image data.

[0599] GPS sensor on your smartphone or vehicle: Obtains location information.

[0600] Server: Analyzes the received data and generates images.

[0601] software:

[0602] Azure Elevation Data API: Used to retrieve elevation data.

[0603] OpenAI API: Generates terrain images that remove artificial objects using an image generation model.

[0604] Driver assistance system for autonomous vehicles: Displays the generated terrain images to assist vehicle driving.

[0605] 2. System Overview

[0606] The user's smartphone or in-vehicle camera captures the scenery around the vehicle and acquires the image data and location information (latitude, longitude, and direction information). This data is temporarily stored in the device.

[0607] The device then sends the saved image data and location information to the server, which then retrieves elevation data for the area based on the received latitude and longitude information using the Azure elevation data API.

[0608] Based on the acquired elevation data and image data, the server uses OpenAI's image generation model to generate a terrain image that detects and removes artificial objects in the image data. During this generation process, the following prompts are used:

[0609] Prompt Sentence Examples

[0610] Please remove any artificial objects from this image to recreate a purely natural terrain. The latitude of this image is 35.6895 and the longitude is 139.6917. Please remove any artificial buildings or cars while preserving the current terrain.

[0611] The generated terrain image is then sent back to the terminal from the server. Finally, the terminal displays the received terrain image on the driver assistance system of the autonomous vehicle, enabling the vehicle's driver assistance system to support vehicle operation based on accurate terrain data with artificial objects removed.

[0612] 3. Specific Examples

[0613] For example, when a user drives an autonomous vehicle in the streets of Tokyo, the onboard camera captures the surrounding scenery, and the GPS sensor acquires latitude and longitude information. This information is sent to a server, which then uses the Azure elevation data API to collect elevation data. The collected data and captured images are then converted into a pure terrain image using OpenAI's image generation model, eliminating artifacts. The generated image is then displayed on the driver assistance system, providing more accurate driving assistance.

[0614] In this way, the system of the present invention plays an important role in enabling autonomous vehicles to more accurately grasp the surrounding environment and achieve safe driving.

[0615] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0616] Program processing flow

[0617] Step 1: User takes photos and acquires data

[0618] The user takes a photo of the surrounding scenery using their smartphone or in-car camera. When taking a photo, the smartphone or vehicle's GPS sensor acquires latitude and longitude information, and the camera's orientation sensor acquires direction information. The captured image and location information are then temporarily saved on the device.

[0619] Input: A photographed landscape image, latitude and longitude information, and direction information.

[0620] Output: Image data and location information stored on the device.

[0621] Step 2: Sending data

[0622] The terminal transmits the temporarily saved image data, latitude and longitude information, and direction information to the server, and when transmission is complete, waits for a response from the server.

[0623] Input: Image data and location information stored on the device.

[0624] Output: Image data and location information sent to the server.

[0625] Step 3: Parsing on the server

[0626] The server obtains elevation data for the area based on the received latitude and longitude information, using the Azure elevation data API for this process.

[0627] Input: Received latitude and longitude information.

[0628] Output: The retrieved elevation data.

[0629] Step 4: Artifact removal using image generation AI

[0630] The server uses the received image data and elevation data to input prompt statements into OpenAI's image generation model, generating a terrain image that removes artificial objects from the image data.

[0631] Input: Received image data and elevation data, prompt statement.

[0632] Output: Generated terrain image.

[0633] Specific operation:

[0634] 1. Insert a prompt such as "Remove any man-made objects from this image to recreate a purely natural terrain. The image is at latitude 35.6895 and longitude 139.6917. Please remove any man-made buildings or vehicles while preserving the current terrain."

[0635] 2. Send the data to OpenAI's API and perform the artifact removal process.

[0636] Step 5: Submit the generated image

[0637] The server then transmits the generated topographical image data to the user's terminal, checking the integrity of the data during transmission.

[0638] Input: Generated terrain image data.

[0639] Output: Terrain image data sent to the device.

[0640] Step 6: Display by driver assistance system

[0641] The terminal receives terrain image data from the server and displays it on the driver assistance system of the autonomous vehicle, allowing the vehicle to provide driving assistance based on accurate terrain data that has been stripped of artificial objects.

[0642] Input: Terrain image data sent to the device.

[0643] Output: Terrain image data displayed on the driver assistance system.

[0644] This series of processes allows for the acquisition of terrain images with artificial objects removed, which can then be displayed on the driver assistance system of an autonomous vehicle, enabling safer and more accurate driving assistance.

[0645] 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.

[0646] overview

[0647] This system uses a smartphone (terminal), a server, image generation AI, elevation data, and an emotion engine to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research. It also provides flexible visual information that takes into account the user's emotional state.

[0648] Program processing overview

[0649] Step 1: User photography and data acquisition

[0650] The user takes a picture of the scenery using a smartphone. When taking a picture, the smartphone uses its GPS function to obtain latitude and longitude information, and also obtains direction information from the camera's direction sensor. The captured image and location information are then temporarily saved on the device.

[0651] Step 2: Obtaining emotion data

[0652] The smartphone's built-in emotion engine analyzes the user's facial expressions through the camera or performs voice analysis to obtain the user's emotion data, which is also temporarily stored on the device.

[0653] Step 3: Send the data

[0654] The terminal establishes communication for transmitting the acquired image data, latitude and longitude information, direction information, and emotion data to the server. After the transmission is completed, the terminal waits for a response from the server.

[0655] Step 4: Parsing on the server

[0656] The server obtains elevation data for the area based on the received latitude and longitude information, and prepares to analyze the terrain based on the obtained elevation data, image data, and direction information.

[0657] Step 5: Artifact removal using image generation AI

[0658] The server inputs the received image data into the image generation AI to detect man-made objects (buildings, roads, power lines, etc.). The image generation AI removes the detected man-made objects and generates a terrain image without the man-made objects. This generated terrain image is temporarily stored on the server.

[0659] Step 6: Optimize based on user sentiment

[0660] The server analyzes the received user emotion data and selects the most appropriate display format for the terrain image according to the user's emotional state. This optimization process allows the user to receive information more comfortably.

[0661] Step 7: Submit the generated image

[0662] The server prepares to send the generated topographical image data to the user's device. This transmission is also performed via the Internet. The server confirms the completion of the transmission and records the operation log.

[0663] Step 8: Displaying the generated image

[0664] The device stores the generated image data received from the server in local storage. The stored generated image data is displayed within the application, allowing the user to visually confirm the terrain with no artificial objects removed. The display format is optimized based on emotion data, allowing the user to view the information without stress.

[0665] Specific examples

[0666] Use case 1: Disaster prevention

[0667] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[0668] Device: The smartphone sends the captured image data, latitude and longitude information, direction information, and emotion data to the server.

[0669] Server: Based on the received data, the server obtains elevation data for the relevant area from an elevation database and uses image generation AI to generate a terrain image that removes artificial objects.

[0670] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[0671] Use case 2: Education

[0672] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[0673] Device: Students take photos of the mountain behind the school using their smartphones and send the image data, location information, and emotion data to the server.

[0674] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[0675] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[0676] Use Case 3: Historical Research

[0677] User: A historian conducts fieldwork at a site known as a battlefield from the Sengoku period.

[0678] Terminal: The researcher takes a modern photograph and sends the image data, location information, and emotional data to the server.

[0679] Server: Elevation data is obtained based on the received data, and the landscape at that time is recreated using old map data and image generation AI.

[0680] Terminal: Images of the past terrain are sent to the researcher's smartphone, allowing them to visually understand the specific terrain of the battlefield at that time.

[0681] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research, and also provides flexible information that responds to the user's emotional state.

[0682] The processing flow will be explained below.

[0683] Step 1:

[0684] A user starts the camera application on their smartphone and takes a picture of a target scene. After taking the picture, the camera application acquires the image data.

[0685] Step 2:

[0686] The device acquires the latitude and longitude information of its current location using the built-in GPS module, and also acquires direction information from the camera's direction sensor.

[0687] Step 3:

[0688] The device temporarily stores the acquired image data, latitude and longitude information, and direction information in its internal storage.

[0689] Step 4:

[0690] The emotion engine installed on the device analyzes the user's facial expressions through the camera or performs voice analysis to obtain the user's emotional data, which is also temporarily stored in the device's internal storage.

[0691] Step 5:

[0692] The device establishes communication to transmit the stored image data, latitude and longitude information, orientation information, and emotion data to a server, typically via the Internet.

[0693] Step 6:

[0694] The device transmits image data, latitude and longitude information, direction information, and emotion data to the server. After transmission is complete, the device waits for a response from the server.

[0695] Step 7:

[0696] Based on the received latitude and longitude information, the server retrieves the elevation data for the relevant area from the elevation database.

[0697] Step 8:

[0698] The server uses the received image data, elevation data, and orientation information to prepare for analyzing the topography of the target area.

[0699] Step 9:

[0700] The server inputs the image data into the image generation AI, which then detects artificial objects (buildings, roads, power lines, etc.) in the image.

[0701] Step 10:

[0702] The image generation AI removes any detected artificial objects and generates a terrain image without any artificial objects. This generated terrain image is temporarily stored on the server.

[0703] Step 11:

[0704] The server analyzes the received user emotional data and selects the optimal display format for the terrain image according to the user's emotional state. As a result of the emotional analysis, the display is customized according to the user's stress and interests.

[0705] Step 12:

[0706] The server prepares to send the generated topographical image data to the user's device, which is also sent via the Internet.

[0707] Step 13:

[0708] The server sends the generated terrain image to the terminal, confirms the completion of the transmission, and records the operation log.

[0709] Step 14:

[0710] The terminal stores the generated image data received from the server in local storage.

[0711] Step 15:

[0712] The device displays the saved generated image data within the application, allowing the user to visually confirm the terrain with no artificial objects removed. The display format is optimized based on the emotion data, allowing the user to view the information without stress.

[0713] Example 2

[0714] 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."

[0715] Current image processing technology has difficulty in removing artificial objects from landscape images and reproducing natural topography. It also lacks the ability to provide optimal information display formats based on the user's emotional state. As a result, it is difficult to provide appropriate information to deepen understanding of topography in fields such as disaster prevention, education, and historical research.

[0716] 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.

[0717] In this invention, the server includes terminal means for acquiring location data, terminal means for capturing at least one image data, server means for receiving the location data and image data transmitted from the terminal means and acquiring terrain data, server means for generating a terrain image from which artificial objects have been removed using a generation AI based on the acquired terrain data, server means for analyzing the user's emotional data and selecting an optimal information display format according to the user's emotional state, and server means for transmitting the generated terrain image to the terminal means. This not only makes it possible to reproduce a natural terrain from which artificial objects have been removed, but also enables flexible information provision according to the user's emotional state.

[0718] "Location data" is information for identifying a location, and typically includes coordinate information and direction information.

[0719] "Terminal means" refers to a device used by a user, specifically a smartphone, tablet, etc.

[0720] "Image data" refers to digital data of photographs or videos taken using a terminal means.

[0721] "Server Means" refers to a central server that receives, analyzes, processes and transmits data.

[0722] "Terrain Data" refers to data relating to terrain, such as elevation information for a particular geographic area.

[0723] "Generative AI" refers to artificial intelligence algorithms and models that process image data to generate new images.

[0724] "Man-made objects" refers to man-made structures that are not natural elements, such as buildings, roads, or power lines.

[0725] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and the like.

[0726] "Information presentation format" refers to the visual or auditory format of information presented to a user.

[0727] "Image generation means" refers to the technology or algorithm used to detect and edit specific objects from image data.

[0728] MODE FOR CARRYING OUT THE INVENTION

[0729] System Configuration

[0730] This invention is a system that includes a user terminal, a central server, an image generation AI, a terrain database, and an emotion analysis engine. The purpose of this system is to generate terrain images that are free of artificial objects and provide them in an optimal information display format according to the user's emotional state.

[0731] Hardware Configuration

[0732] The terminals used are mobile devices such as smartphones and tablets, which must be equipped with a GPS module, a camera, a direction sensor, and a front-facing camera and microphone for emotion analysis.

[0733] Servers use high-performance computer systems to receive, analyze, process, and transmit data, and require large amounts of storage, powerful CPUs and GPUs, and network communication capabilities.

[0734] Software Configuration

[0735] The device comes with an application that integrates a camera app, GPS function, orientation sensor function, and emotion analysis engine. For example, OpenCV and TensorFlow Lite can be used for image processing.

[0736] On the server side, the image analysis system is built using programming languages ​​such as Python and Java, and uses an API to reference the terrain database and generative AI (e.g., Stable Diffusion and GAN models) as an image generation AI model.

[0737] Operation overview

[0738] The user takes a photo of the target scene using their smartphone. This acquires image data, latitude and longitude information from GPS, and camera direction information. Furthermore, emotional data is analyzed from the user's facial expressions and voice, and temporarily saved on the device.

[0739] The device then compiles this data and sends it to a central server. The server then consults an elevation database based on the received latitude and longitude information to obtain topographical data for the relevant area. Based on this data, an image generation AI generates a topographical image that eliminates artificial objects. The AI ​​also analyzes the user's emotional data and selects the optimal information display format based on their emotional state.

[0740] The server then sends the generated terrain image to the terminal, which then displays it, allowing the user to view a natural terrain image with no artificial objects present.

[0741] Specific examples

[0742] Use case 1: Disaster prevention

[0743] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[0744] Device: The smartphone sends the captured image data, latitude and longitude information, direction information, and emotion data to the server.

[0745] Server: Based on the received data, the server obtains topographical data for the relevant area from an elevation database, and uses image generation AI to generate a topographical image that removes artificial objects.

[0746] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[0747] Use case 2: Education

[0748] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[0749] Device: Students take photos of the mountain behind the school using their smartphones and send the image data, location information, and emotion data to the server.

[0750] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[0751] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[0752] Prompt statement

[0753] Here are some example prompts for the image generation AI:

[0754] "Detect and remove man-made objects such as buildings and power lines from this landscape to generate a natural-looking terrain image."

[0755] An example prompt for the sentiment analysis engine is:

[0756] "Analyze the user's facial expressions to determine their current emotional state."

[0757] As explained above, the present invention provides practical value in fields such as disaster prevention, education, and historical research, and also realizes flexible information provision according to the user's emotional state.

[0758] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0759] Step 1:

[0760] The user uses a smartphone to capture the scenery they want to shoot. When taking the photo, the device uses its GPS function to obtain latitude and longitude information, and obtains orientation information from the camera's orientation sensor. This provides image data, latitude and longitude information, and orientation information as input. This data is temporarily stored within the device. Specifically, the user launches the smartphone's camera app and takes a photo of the scenery in the specified area, at the same time automatically activating the GPS function and orientation sensor to obtain the necessary data.

[0761] Step 2:

[0762] After capturing the image, the device activates its emotion engine and analyzes the user's facial expressions through the camera. If necessary, it records the user's voice and performs audio analysis. This provides input data on the user's facial expressions and voice, which are then used to generate emotion data. This emotion data is temporarily stored within the device. Specifically, the emotion engine captures the user's facial expressions, and the emotion analysis algorithm analyzes them to determine the user's emotional state.

[0763] Step 3:

[0764] The device establishes communication to send the acquired image data, latitude and longitude information, direction information, and emotion data to the server. Once data transmission is complete, it waits for a response from the server. Data stored in the device as input is sent to the server, which triggers the server to receive the data. Specifically, the device connects to the server using Wi-Fi or mobile data communication and sends all data at once.

[0765] Step 4:

[0766] The server retrieves elevation data for the relevant area from the terrain database based on the received latitude and longitude information. The latitude and longitude information is provided as input, and terrain data is retrieved based on that. The server then prepares to analyze the terrain based on this data. Specifically, the server calls the elevation database API to retrieve the necessary terrain data.

[0767] Step 5:

[0768] The server inputs the received image data into the generation AI, which then processes it to detect and remove man-made objects (buildings, roads, power lines, etc.). Image data is provided as input, and the generation AI analyzes it to generate a terrain image with the man-made objects removed. This generated terrain image is temporarily stored on the server. Specifically, the server sends a prompt to the image generation AI saying, "Please detect and remove man-made objects such as buildings and power lines from this landscape and generate a natural-looking terrain image."

[0769] Step 6:

[0770] The server analyzes the received user emotion data and selects the optimal display format for the terrain image based on the user's emotional state. The emotion data is provided as input, and the display format is selected based on that. Specifically, the server applies an emotion data analysis algorithm to determine the optimal display format based on the user's emotional state.

[0771] Step 7:

[0772] The server prepares to send the generated terrain image data to the user's device. When the data transmission is complete, an operation log is recorded. The generated terrain image data is provided as input, and this data is sent to the user's device. Specifically, the server formats the image data into transmission packets and sends them to the terminal via the Internet.

[0773] Step 8:

[0774] The device saves the generated image data received from the server in local storage. The saved generated image data is displayed in the application so that the user can check it. The image data received from the server is provided as input and displayed in the application. Specifically, the device decompresses the data received from the server and saves it in local storage, and the application reads and displays the data.

[0775] (Application example 2)

[0776] 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."

[0777] Conventional food delivery systems have the problem of being unable to flexibly respond to obstacles and temporary traffic restrictions that delivery workers encounter on their routes. Furthermore, routes are provided without taking into account the emotional state of delivery workers, which can increase stress and reduce work efficiency. Furthermore, conventional systems do not adequately consider the topography of the delivery route, nor do they adequately optimize the safety and comfort of delivery work.

[0778] 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.

[0779] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring elevation data, server means for generating a terrain image from which artificial objects have been removed using an image generation AI based on the acquired elevation data, server means for transmitting the generated terrain image to the terminal means, and server means for receiving emotion data acquired by the terminal means and selecting an optimal terrain image display format based on the emotion data. This enables delivery personnel to deliver goods along safe and efficient routes that take into account their emotional state as well as the latest terrain information.

[0780] "Location information" is information that indicates the geographical location where a target terminal or object is currently located, and typically includes latitude, longitude, and direction information.

[0781] "Terminal means" refers to a device that has the function of acquiring location information, image data, emotional data, etc. and transmitting it to a server.

[0782] "Image data" is a digital representation of visual information captured by a photographic device such as a camera.

[0783] "Elevation data" is geographical data that indicates the height of a specific location above the ground surface, and is used in topographical analysis.

[0784] "Server Means" refers to a system that has the function of receiving, processing, and transmitting data through the cloud or a network.

[0785] "Image generation AI" is an algorithm or system that uses artificial intelligence technology to analyze input images and generate new images.

[0786] "Emotion data" is digital information that indicates the user's emotional state, as analyzed from the user's facial expressions, voice, etc.

[0787] "Terrain image display format" refers to the layout and visual style when displaying the generated terrain image to the user.

[0788] This invention provides a system for food delivery businesses that enables delivery personnel to deliver food along safe and efficient routes. The system is composed of a smartphone, a server, an elevation database, an image generation AI, and an emotion engine.

[0789] First, the delivery person uses their smartphone to take a photo of the scenery along their current delivery route. The smartphone uses its GPS to obtain latitude and longitude information and direction information from the camera's orientation sensor. The smartphone's built-in emotion engine also analyzes the delivery person's facial expressions through the camera to obtain emotional data. This data is temporarily stored on the device.

[0790] Next, the device transmits the acquired image data, latitude and longitude information, direction information, and emotion data to the server. The server acquires elevation data for the relevant area based on the received latitude and longitude information, and uses image generation AI to generate a terrain image that removes artificial objects (buildings, roads, power lines, etc.). This terrain image is temporarily stored on the server.

[0791] Furthermore, the server analyzes the received emotional data and selects the optimal terrain image display format based on the delivery person's emotional state, allowing the delivery person to receive route information more comfortably. After this optimization process, the generated terrain image data is sent to the delivery person's device. The device then stores the received generated image data in local storage, allowing the delivery person to visually confirm the terrain with no artificial objects removed while understanding the delivery route.

[0792] Specifically, the following hardware and software are used:

[0793] Smartphone: Sensors include GPS, orientation sensor, camera, and emotion engine (e.g., Affectiva's SDK)

[0794] Server: Terrain database and elevation database as databases, GAN (Generative Adversarial Network) model as image generation AI, Python library (TensorFlow, OpenCV, etc.) as analysis engine

[0795] Specific examples

[0796] When a delivery person is driving through an area in the city center where road construction and temporary traffic restrictions are frequent, the delivery person selects an avoidance route based on the latest artificial object-free terrain image that reflects the situation.

[0797] Prompt Sentence Examples

[0798] "Create a system that allows food delivery riders to take photos of their latest delivery route in Roppongi, Minato-ku, Tokyo with their smartphones, and then uses the photos to reconstruct natural terrain information by removing artificial objects, and then generates and displays the optimal delivery route to reduce stress."

[0799] This system enables delivery personnel to carry out safe and efficient deliveries based on up-to-date terrain information and thoughtful route information.

[0800] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0801] Step 1:

[0802] The delivery person uses a device (smartphone) to take photos of the local scenery.

[0803] —Input: landscape image, GPS location, direction

[0804] —Operation: The device camera captures a landscape image, while the GPS sensor acquires latitude and longitude information and the orientation sensor acquires orientation information. These data are temporarily stored in local storage.

[0805] —Output: Landscape image data, latitude and longitude information, direction information

[0806] Step 2:

[0807] The device (smartphone) analyzes the delivery person's facial expressions using an emotion engine to obtain emotional data.

[0808] —Input: Facial expression of delivery person (camera image), voice data (if necessary)

[0809] - Operation: The emotion engine (e.g., Affectiva's SDK) installed on the device analyzes the delivery person's facial expressions through the camera and obtains their emotional state as numerical data.

[0810] —Output: Emotion data

[0811] Step 3:

[0812] The terminal (smartphone) transmits the acquired image data, latitude and longitude information, direction information, and emotion data to the server.

[0813] —Input: Image data, latitude and longitude information, direction information, emotion data

[0814] Operation: The device establishes communication with the server via the Internet, sends the above information to the server, and then waits for a response from the server.

[0815] —Output: Various data sent to the server

[0816] Step 4:

[0817] The server obtains elevation data for the relevant area based on the received latitude and longitude information.

[0818] —Input: Latitude and longitude information

[0819] Operation: The server accesses the terrain database and the elevation database to obtain the elevation data corresponding to the relevant latitude and longitude information.

[0820] —Output: Elevation data

[0821] Step 5:

[0822] The server uses image generation AI to generate a terrain image that removes artificial objects.

[0823] —Input: Image data, elevation data, orientation information

[0824] Operation: The server inputs the received image data into an image generation AI (such as a GAN model) to detect and remove artificial objects. As a result, a terrain image without artificial objects is generated.

[0825] —Output: Terrain image with artifacts removed

[0826] Step 6:

[0827] The server selects the optimum display format for the terrain image based on the emotion data.

[0828] —Input: emotion data, terrain image

[0829] - Operation: The server analyzes the emotional data and selects the display format that allows the user to receive information most comfortably. The display format may include color filters, contrast adjustment, etc.

[0830] —Output: Optimized terrain image display format

[0831] Step 7:

[0832] The server transmits the generated topographical image data to the terminal.

[0833] —Input: Optimized terrain image

[0834] - Operation: The server sends the generated topographical image data to the terminal. After the transmission is complete, the operation log is recorded.

[0835] —Output: Terrain image data sent to the device

[0836] Step 8:

[0837] The terminal displays the generated image data received from the server.

[0838] —Input: Generated image data

[0839] Operation: The device receives terrain image data from the server, stores it in local storage, and displays it through the application. The delivery person visually checks the terrain, free of artificial objects, and delivers along a safe and efficient route.

[0840] —Output: Terrain image displayed on the terminal

[0841] 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.

[0842] 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.

[0843] 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.

[0844] [Third embodiment]

[0845] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0846] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0847] 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).

[0848] 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.

[0849] 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.

[0850] 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).

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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."

[0857] overview

[0858] This invention is a system that uses a smartphone (terminal), a server, image generation AI, and elevation data to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research.

[0859] Program processing overview

[0860] Step 1: User photography and data acquisition

[0861] The user takes a picture of the scenery using a smartphone. When taking a picture, the smartphone uses its GPS function to obtain latitude and longitude information, and also obtains direction information from the camera's direction sensor. The captured image and location information are then temporarily saved on the device.

[0862] Step 2: Send the data

[0863] The terminal transmits the temporarily saved image data, latitude and longitude information, and direction information to the server, and when transmission is complete, waits for a response from the server.

[0864] Step 3: Parsing on the server

[0865] The server obtains elevation data for the area based on the received latitude and longitude information, and performs a topographical analysis based on the obtained elevation data, image data, and direction information.

[0866] Step 4: Artifact removal using image generation AI

[0867] The server inputs the received image data into the image generation AI to detect man-made objects. The image generation AI then removes the detected man-made objects and generates a realistic terrain image. The generated terrain image is temporarily stored on the server.

[0868] Step 5: Submit the generated image

[0869] The server transmits the generated terrain image data to the user's terminal, where it is stored and displayed to the user.

[0870] Specific examples

[0871] Use case 1: Disaster prevention

[0872] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[0873] Terminal: The smartphone sends the captured image data, latitude and longitude information, and direction information to the server.

[0874] Server: Based on the received data, the server obtains elevation data for the relevant area from an elevation database and uses image generation AI to generate a terrain image that removes artificial objects.

[0875] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[0876] Use case 2: Education

[0877] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[0878] Device: Students take photos of the mountain behind the school using their smartphones and send the image data and location information to the server.

[0879] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[0880] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[0881] Use Case 3: Historical Research

[0882] User: A historian conducts fieldwork at a site known as a battlefield from the Sengoku period.

[0883] Terminal: The researcher takes a contemporary photograph and sends the image data and location information to the server.

[0884] Server: Elevation data is obtained based on the received data, and the landscape at that time is recreated using old map data and image generation AI.

[0885] Terminal: Images of the past terrain are sent to the researcher's smartphone, allowing them to visually understand the specific terrain of the battlefield at that time.

[0886] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research.

[0887] The processing flow will be explained below.

[0888] Step 1:

[0889] A user starts the camera application on their smartphone and takes a picture of a target scene. After taking the picture, the camera application acquires the image data.

[0890] Step 2:

[0891] The device acquires the latitude and longitude information of its current location using the built-in GPS module, and also acquires direction information from the camera's direction sensor.

[0892] Step 3:

[0893] The device temporarily stores the acquired image data, latitude and longitude information, and direction information in its internal storage.

[0894] Step 4:

[0895] The device establishes a connection to send the stored data to a server, which typically occurs over the Internet.

[0896] Step 5:

[0897] The terminal transmits the image data, latitude and longitude information, and direction information to the server. After the transmission is complete, the terminal waits for a response from the server.

[0898] Step 6:

[0899] Based on the received latitude and longitude information, the server retrieves the elevation data for the relevant area from the elevation database.

[0900] Step 7:

[0901] The server uses the received image data, elevation data, and direction information to prepare for analyzing the topography of the target area.

[0902] Step 8:

[0903] The server inputs the image data into the image generation AI, which then detects artificial objects (buildings, roads, power lines, etc.) in the image.

[0904] Step 9:

[0905] The image generation AI removes any detected artificial objects and generates a terrain image without any artificial objects. This generated terrain image is temporarily stored on the server.

[0906] Step 10:

[0907] The server prepares to send the generated topographical image data to the user's terminal, which is also sent via the Internet.

[0908] Step 11:

[0909] The server sends the generated terrain image to the terminal, confirms the completion of the transmission, and records the operation log.

[0910] Step 12:

[0911] The terminal stores the generated image data received from the server in local storage.

[0912] Step 13:

[0913] The device displays the saved generated image data within the application, allowing the user to visually confirm the terrain with the artificial objects removed.

[0914] Example 1

[0915] 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."

[0916] With conventional technology, artificial objects in landscape photographs often interfere with natural understanding of the terrain. This makes it difficult to obtain accurate and pure terrain information in fields such as disaster prevention, education, and historical research. Furthermore, no system exists for combining and analyzing multiple data sources. To solve this problem, a system is needed that can automatically remove artificial objects from images and generate realistic terrain information.

[0917] 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.

[0918] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring topographical data, server means for generating a topographical image from which artificial objects have been removed using an image generation AI model based on the acquired topographical data, and server means for transmitting the generated topographical image to the terminal means. This allows users to easily obtain topographical images from which artificial objects have been removed, enabling a more accurate understanding of topography in fields such as disaster prevention, education, and historical research.

[0919] "Location information" is data used to identify the location of an object, and specifically includes latitude and longitude information and direction information.

[0920] The "terminal means" is a device that is operated by a user to capture image data and acquire location information, and refers to a smartphone, tablet, or the like.

[0921] "Image data" is digital data that stores visual information of scenery or objects photographed by a terminal means.

[0922] The term "server means" refers to a computer system and its software for receiving data sent from the terminal means and analyzing and processing the data.

[0923] "Topographical data" refers to data relating to the elevation and geographical shape of a particular area, and is obtained from a map database or the like.

[0924] An "image generation AI model" refers to a program or algorithm that uses artificial intelligence to remove certain elements (such as man-made objects) from image data and generate realistic terrain images.

[0925] "Artificial objects" are objects made by humans that do not exist in the natural environment, including buildings, cars, and utility poles.

[0926] A "terrain image" is image data that represents the pure terrain, with artificial objects removed by a generative AI model.

[0927] This invention is a system that uses a smartphone (terminal), a server, an image generation AI model, and terrain data to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research.

[0928] Hardware and software used

[0929] Terminal

[0930] A user takes a photograph of a landscape using a smartphone. This device is equipped with a GPS function and a camera orientation sensor, and acquires latitude, longitude, and orientation information when taking a photograph. This allows the captured image data and location information to be temporarily stored on the device.

[0931] server

[0932] The server analyzes the received image data, latitude and longitude information, and direction information. Specifically, the server performs the following processes.

[0933] Based on the received latitude and longitude information, topographical data for the relevant area is obtained from a map database (e.g., elevation data).

[0934] The terrain is analyzed based on the acquired topographical data, image data, and directional information using terrain analysis software (analysis tools such as Python).

[0935] It uses an image generation AI model to detect and remove artifacts from the received image data, and this AI model operates based on deep learning techniques (using TensorFlow and PyTorch).

[0936] Processing flow

[0937] 1. User photography and data acquisition

[0938] The user takes a picture of a landscape using a smartphone. When taking the picture, the device acquires latitude and longitude information using the GPS function and direction information using the direction sensor, and these data are temporarily stored on the device along with the image data.

[0939] 2. Data transmission

[0940] The device sends the temporarily stored image data, latitude and longitude information, and direction information to the server. Data transmission uses a secure communication protocol.

[0941] 3. Analysis on the server

[0942] The server obtains topographical data for the relevant area based on the received data and performs analysis using analysis software.

[0943] 4. Artificial object removal using image generation AI

[0944] The server sends a prompt to the image generation AI model to generate a terrain image with artificial objects removed. For example, the prompt might say, "Please remove the artificial objects from this photo and generate a natural terrain image."

[0945] 5. Sending the generated image

[0946] The generated terrain image is sent to the user's device, which stores the received terrain image in local storage and displays it to the user.

[0947] Specific examples

[0948] Use case 1: Disaster prevention

[0949] Disaster prevention workers (users) take photos of disaster areas for earthquake risk assessment.

[0950] The smartphone (terminal) sends the image data, latitude and longitude information, and direction information after the photo is taken to the server.

[0951] Based on the received data, the server obtains topographical data for the relevant area and uses image generation AI to generate a topographical image that removes artificial objects.

[0952] The device displays the generated terrain image to the user and performs a risk assessment based on the natural topography of the affected area.

[0953] Use case 2: Education

[0954] A teacher (user) instructs students to acquire terrain images to study the natural terrain around the school.

[0955] Students take photos of the area around the school using their smartphones (devices) and send the image data and location information to the server.

[0956] The server obtains terrain data from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[0957] Students can view the generated terrain images on their smartphones, helping them understand the natural terrain around their school.

[0958] Use Case 3: Historical Research

[0959] Historical researchers (users) take contemporary photographs during fieldwork to investigate ancient battlefields.

[0960] Researchers send image data and location information to a server.

[0961] The server obtains topographical data based on the received data, and recreates past landscapes using old map data and image generation AI.

[0962] Researchers can view historical terrain images generated on smartphones and visually understand the specific terrain of the battlefields at the time.

[0963] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research.

[0964] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0965] The specific flow of this system's program processing

[0966] Step 1: User takes photos and acquires data

[0967] Operation: The user takes a landscape image using a smartphone (device).

[0968] Input: By pressing the capture button, image data captured by the camera, latitude and longitude information obtained by the GPS sensor, and direction information obtained by the direction sensor are collected.

[0969] Data processing: The collected image data and attached location information (latitude, longitude, and direction information) are stored in temporary storage.

[0970] Output: Image data and location information stored on the device.

[0971] Step 2: Sending data

[0972] Operation: The device sends the temporarily stored data to the server.

[0973] Input: Image data and location information saved in step 1.

[0974] Data processing: Image data and location information are sent to a server using the HTTPS protocol or similar.

[0975] Output: Data transferred to the server.

[0976] Step 3: Parsing on the server

[0977] Operation: Based on the received location information, the server obtains topographical data for the area and analyzes the image data.

[0978] Input: Received image data, latitude and longitude information, and direction information.

[0979] Data processing: The server retrieves terrain data based on location information from the map database and extracts terrain features using image analysis software (e.g., Python's OpenCV or GDAL).

[0980] Output: Topographic data and analyzed image data.

[0981] Step 4: Artifact removal using image generation AI

[0982] How it works: The server uses an image-generating AI model to remove artifacts from the images it receives.

[0983] Input: Analyzed image data, topographic data.

[0984] Data processing: The image generation AI model is sent a prompt such as, "Remove artificial objects from this photo and generate a natural-looking landscape image." The AI ​​model uses deep learning algorithms (e.g., TensorFlow or PyTorch) to detect and remove artificial objects.

[0985] Output: Terrain image with artifacts removed.

[0986] Step 5: Submit the generated image

[0987] Operation: The server sends the generated terrain image data to the user's terminal.

[0988] Input: Terrain image with artifacts removed.

[0989] Data processing: The generated topographical image data is sent to the user's device using the HTTPS protocol or similar.

[0990] Output: Generated terrain image saved and displayed on device.

[0991] (Application example 1)

[0992] 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."

[0993] Autonomous vehicles are required to more accurately grasp the landscape data around the vehicle and improve the accuracy of driver assistance systems. To achieve this, pure terrain data that excludes artificial objects is required. However, with current technology, much of the landscape data contains artificial objects, making it difficult to obtain accurate terrain data. This affects the performance of driver assistance systems and poses safety challenges.

[0994] 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.

[0995] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring elevation data, server means for generating a terrain image from which artificial objects have been removed using an image generation model based on the acquired elevation data, server means for transmitting the generated terrain image to the terminal means, and means for displaying the generated terrain image in a driving assistance system of the vehicle. This enables an autonomous vehicle to acquire accurate terrain data from which artificial objects have been removed, thereby improving the accuracy of the driving assistance system.

[0996] Key Word Definitions

[0997] "Location information" is data such as latitude and longitude that indicates the geographic location of an object.

[0998] "Image data" refers to visual information captured by a camera or other imaging device, and that information is stored in digital form.

[0999] "Terminal means" refers to a mobile device such as a smartphone, tablet, or camera, which is operated by a user to obtain location information and image data.

[1000] The "server means" is a computer system for processing and storing data, and receives and processes information sent from the terminal means via a network.

[1001] "Elevation data" is data that indicates information about the height of the earth's surface and is used in analyzing terrain.

[1002] An "image generation model" is an algorithm that uses machine learning and artificial intelligence techniques to generate a new image by eliminating certain elements (such as artificial objects) from an input image.

[1003] "Artifacts" refer to objects such as buildings and vehicles that are created by humans in the natural environment, and are the objects that can be identified in image data.

[1004] A "driving assistance system" is a system that supports automatic driving of a vehicle and the driver, and has the function of recognizing the environment around the vehicle in real time and assisting in appropriate operation.

[1005] MODE FOR CARRYING OUT THE INVENTION

[1006] To implement this invention, it is necessary to build a system according to the following steps.

[1007] 1. Hardware and Software Used

[1008] Hardware:

[1009] Smartphone or vehicle-mounted camera: Used to capture image data.

[1010] GPS sensor on your smartphone or vehicle: Obtains location information.

[1011] Server: Analyzes the received data and generates images.

[1012] software:

[1013] Azure Elevation Data API: Used to retrieve elevation data.

[1014] OpenAI API: Generates terrain images that remove artificial objects using an image generation model.

[1015] Driver assistance system for autonomous vehicles: Displays the generated terrain images to assist vehicle driving.

[1016] 2. System Overview

[1017] The user's smartphone or in-vehicle camera captures the scenery around the vehicle and acquires the image data and location information (latitude, longitude, and direction information). This data is temporarily stored in the device.

[1018] The device then sends the saved image data and location information to the server, which then retrieves elevation data for the area based on the received latitude and longitude information using the Azure elevation data API.

[1019] Based on the acquired elevation data and image data, the server uses OpenAI's image generation model to generate a terrain image that detects and removes artificial objects in the image data. During this generation process, the following prompts are used:

[1020] Prompt Sentence Examples

[1021] Please remove any artificial objects from this image to recreate a purely natural terrain. The latitude of this image is 35.6895 and the longitude is 139.6917. Please remove any artificial buildings or cars while preserving the current terrain.

[1022] The generated terrain image is then sent back to the terminal from the server. Finally, the terminal displays the received terrain image on the driver assistance system of the autonomous vehicle, enabling the vehicle's driver assistance system to support vehicle operation based on accurate terrain data with artificial objects removed.

[1023] 3. Specific Examples

[1024] For example, when a user drives an autonomous vehicle in the streets of Tokyo, the onboard camera captures the surrounding scenery, and the GPS sensor acquires latitude and longitude information. This information is sent to a server, which then uses the Azure elevation data API to collect elevation data. The collected data and captured images are then converted into a pure terrain image using OpenAI's image generation model, eliminating artifacts. The generated image is then displayed on the driver assistance system, providing more accurate driving assistance.

[1025] In this way, the system of the present invention plays an important role in enabling autonomous vehicles to more accurately grasp the surrounding environment and achieve safe driving.

[1026] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1027] Program processing flow

[1028] Step 1: User takes photos and acquires data

[1029] The user takes a photo of the surrounding scenery using their smartphone or in-car camera. When taking a photo, the smartphone or vehicle's GPS sensor acquires latitude and longitude information, and the camera's orientation sensor acquires direction information. The captured image and location information are then temporarily saved on the device.

[1030] Input: A photographed landscape image, latitude and longitude information, and direction information.

[1031] Output: Image data and location information stored on the device.

[1032] Step 2: Sending data

[1033] The terminal transmits the temporarily saved image data, latitude and longitude information, and direction information to the server, and when transmission is complete, waits for a response from the server.

[1034] Input: Image data and location information stored on the device.

[1035] Output: Image data and location information sent to the server.

[1036] Step 3: Parsing on the server

[1037] The server obtains elevation data for the area based on the received latitude and longitude information, using the Azure elevation data API for this process.

[1038] Input: Received latitude and longitude information.

[1039] Output: The retrieved elevation data.

[1040] Step 4: Artifact removal using image generation AI

[1041] The server uses the received image data and elevation data to input prompt statements into OpenAI's image generation model, generating a terrain image that removes artificial objects from the image data.

[1042] Input: Received image data and elevation data, prompt statement.

[1043] Output: Generated terrain image.

[1044] Specific operation:

[1045] 1. Insert a prompt such as "Remove any man-made objects from this image to recreate a purely natural terrain. The image is at latitude 35.6895 and longitude 139.6917. Please remove any man-made buildings or vehicles while preserving the current terrain."

[1046] 2. Send the data to OpenAI's API and perform the artifact removal process.

[1047] Step 5: Submit the generated image

[1048] The server then transmits the generated topographical image data to the user's terminal, checking the integrity of the data during transmission.

[1049] Input: Generated terrain image data.

[1050] Output: Terrain image data sent to the device.

[1051] Step 6: Display by driver assistance system

[1052] The terminal receives terrain image data from the server and displays it on the driver assistance system of the autonomous vehicle, allowing the vehicle to provide driving assistance based on accurate terrain data that has been stripped of artificial objects.

[1053] Input: Terrain image data sent to the device.

[1054] Output: Terrain image data displayed on the driver assistance system.

[1055] This series of processes allows for the acquisition of terrain images with artificial objects removed, which can then be displayed on the driver assistance system of an autonomous vehicle, enabling safer and more accurate driving assistance.

[1056] 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.

[1057] overview

[1058] This system uses a smartphone (terminal), a server, image generation AI, elevation data, and an emotion engine to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research. It also provides flexible visual information that takes into account the user's emotional state.

[1059] Program processing overview

[1060] Step 1: User photography and data acquisition

[1061] The user takes a picture of the scenery using a smartphone. When taking a picture, the smartphone uses its GPS function to obtain latitude and longitude information, and also obtains direction information from the camera's direction sensor. The captured image and location information are then temporarily saved on the device.

[1062] Step 2: Obtaining emotion data

[1063] The smartphone's built-in emotion engine analyzes the user's facial expressions through the camera or performs voice analysis to obtain the user's emotion data, which is also temporarily stored on the device.

[1064] Step 3: Send the data

[1065] The terminal establishes communication for transmitting the acquired image data, latitude and longitude information, direction information, and emotion data to the server. After the transmission is completed, the terminal waits for a response from the server.

[1066] Step 4: Parsing on the server

[1067] The server obtains elevation data for the area based on the received latitude and longitude information, and prepares to analyze the terrain based on the obtained elevation data, image data, and direction information.

[1068] Step 5: Artifact removal using image generation AI

[1069] The server inputs the received image data into the image generation AI to detect man-made objects (buildings, roads, power lines, etc.). The image generation AI removes the detected man-made objects and generates a terrain image without the man-made objects. This generated terrain image is temporarily stored on the server.

[1070] Step 6: Optimize based on user sentiment

[1071] The server analyzes the received user emotion data and selects the most appropriate display format for the terrain image according to the user's emotional state. This optimization process allows the user to receive information more comfortably.

[1072] Step 7: Submit the generated image

[1073] The server prepares to send the generated topographical image data to the user's device. This transmission is also performed via the Internet. The server confirms the completion of the transmission and records the operation log.

[1074] Step 8: Displaying the generated image

[1075] The device stores the generated image data received from the server in local storage. The stored generated image data is displayed within the application, allowing the user to visually confirm the terrain with no artificial objects removed. The display format is optimized based on emotion data, allowing the user to view the information without stress.

[1076] Specific examples

[1077] Use case 1: Disaster prevention

[1078] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[1079] Device: The smartphone sends the captured image data, latitude and longitude information, direction information, and emotion data to the server.

[1080] Server: Based on the received data, the server obtains elevation data for the relevant area from an elevation database and uses image generation AI to generate a terrain image that removes artificial objects.

[1081] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[1082] Use case 2: Education

[1083] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[1084] Device: Students take photos of the mountain behind the school using their smartphones and send the image data, location information, and emotion data to the server.

[1085] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[1086] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[1087] Use Case 3: Historical Research

[1088] User: A historian conducts fieldwork at a site known as a battlefield from the Sengoku period.

[1089] Terminal: The researcher takes a modern photograph and sends the image data, location information, and emotional data to the server.

[1090] Server: Elevation data is obtained based on the received data, and the landscape at that time is recreated using old map data and image generation AI.

[1091] Terminal: Images of the past terrain are sent to the researcher's smartphone, allowing them to visually understand the specific terrain of the battlefield at that time.

[1092] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research, and also provides flexible information that responds to the user's emotional state.

[1093] The processing flow will be explained below.

[1094] Step 1:

[1095] A user starts the camera application on their smartphone and takes a picture of a target scene. After taking the picture, the camera application acquires the image data.

[1096] Step 2:

[1097] The device acquires the latitude and longitude information of its current location using the built-in GPS module, and also acquires direction information from the camera's direction sensor.

[1098] Step 3:

[1099] The device temporarily stores the acquired image data, latitude and longitude information, and direction information in its internal storage.

[1100] Step 4:

[1101] The emotion engine installed on the device analyzes the user's facial expressions through the camera or performs voice analysis to obtain the user's emotional data, which is also temporarily stored in the device's internal storage.

[1102] Step 5:

[1103] The device establishes communication to transmit the stored image data, latitude and longitude information, orientation information, and emotion data to a server, typically via the Internet.

[1104] Step 6:

[1105] The device transmits image data, latitude and longitude information, direction information, and emotion data to the server. After transmission is complete, the device waits for a response from the server.

[1106] Step 7:

[1107] Based on the received latitude and longitude information, the server retrieves the elevation data for the relevant area from the elevation database.

[1108] Step 8:

[1109] The server uses the received image data, elevation data, and orientation information to prepare for analyzing the topography of the target area.

[1110] Step 9:

[1111] The server inputs the image data into the image generation AI, which then detects artificial objects (buildings, roads, power lines, etc.) in the image.

[1112] Step 10:

[1113] The image generation AI removes any detected artificial objects and generates a terrain image without any artificial objects. This generated terrain image is temporarily stored on the server.

[1114] Step 11:

[1115] The server analyzes the received user emotional data and selects the optimal display format for the terrain image according to the user's emotional state. As a result of the emotional analysis, the display is customized according to the user's stress and interests.

[1116] Step 12:

[1117] The server prepares to send the generated topographical image data to the user's device, which is also sent via the Internet.

[1118] Step 13:

[1119] The server sends the generated terrain image to the terminal, confirms the completion of the transmission, and records the operation log.

[1120] Step 14:

[1121] The terminal stores the generated image data received from the server in local storage.

[1122] Step 15:

[1123] The device displays the saved generated image data within the application, allowing the user to visually confirm the terrain with no artificial objects removed. The display format is optimized based on the emotion data, allowing the user to view the information without stress.

[1124] Example 2

[1125] 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."

[1126] Current image processing technology has difficulty in removing artificial objects from landscape images and reproducing natural topography. It also lacks the ability to provide optimal information display formats based on the user's emotional state. As a result, it is difficult to provide appropriate information to deepen understanding of topography in fields such as disaster prevention, education, and historical research.

[1127] 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.

[1128] In this invention, the server includes terminal means for acquiring location data, terminal means for capturing at least one image data, server means for receiving the location data and image data transmitted from the terminal means and acquiring terrain data, server means for generating a terrain image from which artificial objects have been removed using a generation AI based on the acquired terrain data, server means for analyzing the user's emotional data and selecting an optimal information display format according to the user's emotional state, and server means for transmitting the generated terrain image to the terminal means. This not only makes it possible to reproduce a natural terrain from which artificial objects have been removed, but also enables flexible information provision according to the user's emotional state.

[1129] "Location data" is information for identifying a location, and typically includes coordinate information and direction information.

[1130] "Terminal means" refers to a device used by a user, specifically a smartphone, tablet, etc.

[1131] "Image data" refers to digital data of photographs or videos taken using a terminal means.

[1132] "Server Means" refers to a central server that receives, analyzes, processes and transmits data.

[1133] "Terrain Data" refers to data relating to terrain, such as elevation information for a particular geographic area.

[1134] "Generative AI" refers to artificial intelligence algorithms and models that process image data to generate new images.

[1135] "Man-made objects" refers to man-made structures that are not natural elements, such as buildings, roads, or power lines.

[1136] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and the like.

[1137] "Information presentation format" refers to the visual or auditory format of information presented to a user.

[1138] "Image generation means" refers to the technology or algorithm used to detect and edit specific objects from image data.

[1139] MODE FOR CARRYING OUT THE INVENTION

[1140] System Configuration

[1141] This invention is a system that includes a user terminal, a central server, an image generation AI, a terrain database, and an emotion analysis engine. The purpose of this system is to generate terrain images that are free of artificial objects and provide them in an optimal information display format according to the user's emotional state.

[1142] Hardware Configuration

[1143] The terminals used are mobile devices such as smartphones and tablets, which must be equipped with a GPS module, a camera, a direction sensor, and a front-facing camera and microphone for emotion analysis.

[1144] Servers use high-performance computer systems to receive, analyze, process, and transmit data, and require large amounts of storage, powerful CPUs and GPUs, and network communication capabilities.

[1145] Software Configuration

[1146] The device comes with an application that integrates a camera app, GPS function, orientation sensor function, and emotion analysis engine. For example, OpenCV and TensorFlow Lite can be used for image processing.

[1147] On the server side, the image analysis system is built using programming languages ​​such as Python and Java, and uses an API to reference the terrain database and generative AI (e.g., Stable Diffusion and GAN models) as an image generation AI model.

[1148] Operation overview

[1149] The user takes a photo of the target scene using their smartphone. This acquires image data, latitude and longitude information from GPS, and camera direction information. Furthermore, emotional data is analyzed from the user's facial expressions and voice, and temporarily saved on the device.

[1150] The device then compiles this data and sends it to a central server. The server then consults an elevation database based on the received latitude and longitude information to obtain topographical data for the relevant area. Based on this data, an image generation AI generates a topographical image that eliminates artificial objects. The AI ​​also analyzes the user's emotional data and selects the optimal information display format based on their emotional state.

[1151] The server then sends the generated terrain image to the terminal, which then displays it, allowing the user to view a natural terrain image with no artificial objects present.

[1152] Specific examples

[1153] Use case 1: Disaster prevention

[1154] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[1155] Device: The smartphone sends the captured image data, latitude and longitude information, direction information, and emotion data to the server.

[1156] Server: Based on the received data, the server obtains topographical data for the relevant area from an elevation database, and uses image generation AI to generate a topographical image that removes artificial objects.

[1157] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[1158] Use case 2: Education

[1159] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[1160] Device: Students take photos of the mountain behind the school using their smartphones and send the image data, location information, and emotion data to the server.

[1161] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[1162] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[1163] Prompt statement

[1164] Here are some example prompts for the image generation AI:

[1165] "Detect and remove man-made objects such as buildings and power lines from this landscape to generate a natural-looking terrain image."

[1166] An example prompt for the sentiment analysis engine is:

[1167] "Analyze the user's facial expressions to determine their current emotional state."

[1168] As explained above, the present invention provides practical value in fields such as disaster prevention, education, and historical research, and also realizes flexible information provision according to the user's emotional state.

[1169] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1170] Step 1:

[1171] The user uses a smartphone to capture the scenery they want to shoot. When taking the photo, the device uses its GPS function to obtain latitude and longitude information, and obtains orientation information from the camera's orientation sensor. This provides image data, latitude and longitude information, and orientation information as input. This data is temporarily stored within the device. Specifically, the user launches the smartphone's camera app and takes a photo of the scenery in the specified area, at the same time automatically activating the GPS function and orientation sensor to obtain the necessary data.

[1172] Step 2:

[1173] After capturing the image, the device activates its emotion engine and analyzes the user's facial expressions through the camera. If necessary, it records the user's voice and performs audio analysis. This provides input data on the user's facial expressions and voice, which are then used to generate emotion data. This emotion data is temporarily stored within the device. Specifically, the emotion engine captures the user's facial expressions, and the emotion analysis algorithm analyzes them to determine the user's emotional state.

[1174] Step 3:

[1175] The device establishes communication to send the acquired image data, latitude and longitude information, direction information, and emotion data to the server. Once data transmission is complete, it waits for a response from the server. Data stored in the device as input is sent to the server, which triggers the server to receive the data. Specifically, the device connects to the server using Wi-Fi or mobile data communication and sends all data at once.

[1176] Step 4:

[1177] The server retrieves elevation data for the relevant area from the terrain database based on the received latitude and longitude information. The latitude and longitude information is provided as input, and terrain data is retrieved based on that. The server then prepares to analyze the terrain based on this data. Specifically, the server calls the elevation database API to retrieve the necessary terrain data.

[1178] Step 5:

[1179] The server inputs the received image data into the generation AI, which then processes it to detect and remove man-made objects (buildings, roads, power lines, etc.). Image data is provided as input, and the generation AI analyzes it to generate a terrain image with the man-made objects removed. This generated terrain image is temporarily stored on the server. Specifically, the server sends a prompt to the image generation AI saying, "Please detect and remove man-made objects such as buildings and power lines from this landscape and generate a natural-looking terrain image."

[1180] Step 6:

[1181] The server analyzes the received user emotion data and selects the optimal display format for the terrain image based on the user's emotional state. The emotion data is provided as input, and the display format is selected based on that. Specifically, the server applies an emotion data analysis algorithm to determine the optimal display format based on the user's emotional state.

[1182] Step 7:

[1183] The server prepares to send the generated terrain image data to the user's device. When the data transmission is complete, an operation log is recorded. The generated terrain image data is provided as input, and this data is sent to the user's device. Specifically, the server formats the image data into transmission packets and sends them to the terminal via the Internet.

[1184] Step 8:

[1185] The device saves the generated image data received from the server in local storage. The saved generated image data is displayed in the application so that the user can check it. The image data received from the server is provided as input and displayed in the application. Specifically, the device decompresses the data received from the server and saves it in local storage, and the application reads and displays the data.

[1186] (Application example 2)

[1187] 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."

[1188] Conventional food delivery systems have the problem of being unable to flexibly respond to obstacles and temporary traffic restrictions that delivery workers encounter on their routes. Furthermore, routes are provided without taking into account the emotional state of delivery workers, which can increase stress and reduce work efficiency. Furthermore, conventional systems do not adequately consider the topography of the delivery route, nor do they adequately optimize the safety and comfort of delivery work.

[1189] 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.

[1190] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring elevation data, server means for generating a terrain image from which artificial objects have been removed using an image generation AI based on the acquired elevation data, server means for transmitting the generated terrain image to the terminal means, and server means for receiving emotion data acquired by the terminal means and selecting an optimal terrain image display format based on the emotion data. This enables delivery personnel to deliver goods along safe and efficient routes that take into account their emotional state as well as the latest terrain information.

[1191] "Location information" is information that indicates the geographical location where a target terminal or object is currently located, and typically includes latitude, longitude, and direction information.

[1192] "Terminal means" refers to a device that has the function of acquiring location information, image data, emotional data, etc. and transmitting it to a server.

[1193] "Image data" is a digital representation of visual information captured by a photographic device such as a camera.

[1194] "Elevation data" is geographical data that indicates the height of a specific location above the ground surface, and is used in topographical analysis.

[1195] "Server Means" refers to a system that has the function of receiving, processing, and transmitting data through the cloud or a network.

[1196] "Image generation AI" is an algorithm or system that uses artificial intelligence technology to analyze input images and generate new images.

[1197] "Emotion data" is digital information that indicates the user's emotional state, as analyzed from the user's facial expressions, voice, etc.

[1198] "Terrain image display format" refers to the layout and visual style when displaying the generated terrain image to the user.

[1199] This invention provides a system for food delivery businesses that enables delivery personnel to deliver food along safe and efficient routes. The system is composed of a smartphone, a server, an elevation database, an image generation AI, and an emotion engine.

[1200] First, the delivery person uses their smartphone to take a photo of the scenery along their current delivery route. The smartphone uses its GPS to obtain latitude and longitude information and direction information from the camera's orientation sensor. The smartphone's built-in emotion engine also analyzes the delivery person's facial expressions through the camera to obtain emotional data. This data is temporarily stored on the device.

[1201] Next, the device transmits the acquired image data, latitude and longitude information, direction information, and emotion data to the server. The server acquires elevation data for the relevant area based on the received latitude and longitude information, and uses image generation AI to generate a terrain image that removes artificial objects (buildings, roads, power lines, etc.). This terrain image is temporarily stored on the server.

[1202] Furthermore, the server analyzes the received emotional data and selects the optimal terrain image display format based on the delivery person's emotional state, allowing the delivery person to receive route information more comfortably. After this optimization process, the generated terrain image data is sent to the delivery person's device. The device then stores the received generated image data in local storage, allowing the delivery person to visually confirm the terrain with no artificial objects removed while understanding the delivery route.

[1203] Specifically, the following hardware and software are used:

[1204] Smartphone: Sensors include GPS, orientation sensor, camera, and emotion engine (e.g., Affectiva's SDK)

[1205] Server: Terrain database and elevation database as databases, GAN (Generative Adversarial Network) model as image generation AI, Python library (TensorFlow, OpenCV, etc.) as analysis engine

[1206] Specific examples

[1207] When a delivery person is driving through an area in the city center where road construction and temporary traffic restrictions are frequent, the delivery person selects an avoidance route based on the latest artificial object-free terrain image that reflects the situation.

[1208] Prompt Sentence Examples

[1209] "Create a system that allows food delivery riders to take photos of their latest delivery route in Roppongi, Minato-ku, Tokyo with their smartphones, and then uses the photos to reconstruct natural terrain information by removing artificial objects, and then generates and displays the optimal delivery route to reduce stress."

[1210] This system enables delivery personnel to carry out safe and efficient deliveries based on up-to-date terrain information and thoughtful route information.

[1211] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1212] Step 1:

[1213] The delivery person uses a device (smartphone) to take photos of the local scenery.

[1214] —Input: landscape image, GPS location, direction

[1215] —Operation: The device camera captures a landscape image, while the GPS sensor acquires latitude and longitude information and the orientation sensor acquires orientation information. These data are temporarily stored in local storage.

[1216] —Output: Landscape image data, latitude and longitude information, direction information

[1217] Step 2:

[1218] The device (smartphone) analyzes the delivery person's facial expressions using an emotion engine to obtain emotional data.

[1219] —Input: Facial expression of delivery person (camera image), voice data (if necessary)

[1220] - Operation: The emotion engine (e.g., Affectiva's SDK) installed on the device analyzes the delivery person's facial expressions through the camera and obtains their emotional state as numerical data.

[1221] —Output: Emotion data

[1222] Step 3:

[1223] The terminal (smartphone) transmits the acquired image data, latitude and longitude information, direction information, and emotion data to the server.

[1224] —Input: Image data, latitude and longitude information, direction information, emotion data

[1225] Operation: The device establishes communication with the server via the Internet, sends the above information to the server, and then waits for a response from the server.

[1226] —Output: Various data sent to the server

[1227] Step 4:

[1228] The server obtains elevation data for the relevant area based on the received latitude and longitude information.

[1229] —Input: Latitude and longitude information

[1230] Operation: The server accesses the terrain database and the elevation database to obtain the elevation data corresponding to the relevant latitude and longitude information.

[1231] —Output: Elevation data

[1232] Step 5:

[1233] The server uses image generation AI to generate a terrain image that removes artificial objects.

[1234] —Input: Image data, elevation data, orientation information

[1235] Operation: The server inputs the received image data into an image generation AI (such as a GAN model) to detect and remove artificial objects. As a result, a terrain image without artificial objects is generated.

[1236] —Output: Terrain image with artifacts removed

[1237] Step 6:

[1238] The server selects the optimum display format for the terrain image based on the emotion data.

[1239] —Input: emotion data, terrain image

[1240] - Operation: The server analyzes the emotional data and selects the display format that allows the user to receive information most comfortably. The display format may include color filters, contrast adjustment, etc.

[1241] —Output: Optimized terrain image display format

[1242] Step 7:

[1243] The server transmits the generated topographical image data to the terminal.

[1244] —Input: Optimized terrain image

[1245] - Operation: The server sends the generated topographical image data to the terminal. After the transmission is complete, the operation log is recorded.

[1246] —Output: Terrain image data sent to the device

[1247] Step 8:

[1248] The terminal displays the generated image data received from the server.

[1249] —Input: Generated image data

[1250] Operation: The device receives terrain image data from the server, stores it in local storage, and displays it through the application. The delivery person visually checks the terrain, free of artificial objects, and delivers along a safe and efficient route.

[1251] —Output: Terrain image displayed on the terminal

[1252] 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.

[1253] 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.

[1254] 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.

[1255] [Fourth embodiment]

[1256] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1257] 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.

[1258] 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).

[1259] 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.

[1260] 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.

[1261] 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).

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] 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.

[1267] 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.

[1268] 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."

[1269] overview

[1270] This invention is a system that uses a smartphone (terminal), a server, image generation AI, and elevation data to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research.

[1271] Program processing overview

[1272] Step 1: User photography and data acquisition

[1273] The user takes a picture of the scenery using a smartphone. When taking a picture, the smartphone uses its GPS function to obtain latitude and longitude information, and also obtains direction information from the camera's direction sensor. The captured image and location information are then temporarily saved on the device.

[1274] Step 2: Send the data

[1275] The terminal transmits the temporarily saved image data, latitude and longitude information, and direction information to the server, and when transmission is complete, waits for a response from the server.

[1276] Step 3: Parsing on the server

[1277] The server obtains elevation data for the area based on the received latitude and longitude information, and performs a topographical analysis based on the obtained elevation data, image data, and direction information.

[1278] Step 4: Artifact removal using image generation AI

[1279] The server inputs the received image data into the image generation AI to detect man-made objects. The image generation AI then removes the detected man-made objects and generates a realistic terrain image. The generated terrain image is temporarily stored on the server.

[1280] Step 5: Submit the generated image

[1281] The server transmits the generated terrain image data to the user's terminal, where it is stored and displayed to the user.

[1282] Specific examples

[1283] Use case 1: Disaster prevention

[1284] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[1285] Terminal: The smartphone sends the captured image data, latitude and longitude information, and direction information to the server.

[1286] Server: Based on the received data, the server obtains elevation data for the relevant area from an elevation database and uses image generation AI to generate a terrain image that removes artificial objects.

[1287] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[1288] Use case 2: Education

[1289] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[1290] Device: Students take photos of the mountain behind the school using their smartphones and send the image data and location information to the server.

[1291] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[1292] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[1293] Use Case 3: Historical Research

[1294] User: A historian conducts fieldwork at a site known as a battlefield from the Sengoku period.

[1295] Terminal: The researcher takes a contemporary photograph and sends the image data and location information to the server.

[1296] Server: Elevation data is obtained based on the received data, and the landscape at that time is recreated using old map data and image generation AI.

[1297] Terminal: Images of the past terrain are sent to the researcher's smartphone, allowing them to visually understand the specific terrain of the battlefield at that time.

[1298] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research.

[1299] The processing flow will be explained below.

[1300] Step 1:

[1301] A user starts the camera application on their smartphone and takes a picture of a target scene. After taking the picture, the camera application acquires the image data.

[1302] Step 2:

[1303] The device acquires the latitude and longitude information of its current location using the built-in GPS module, and also acquires direction information from the camera's direction sensor.

[1304] Step 3:

[1305] The device temporarily stores the acquired image data, latitude and longitude information, and direction information in its internal storage.

[1306] Step 4:

[1307] The device establishes a connection to send the stored data to a server, which typically occurs over the Internet.

[1308] Step 5:

[1309] The terminal transmits the image data, latitude and longitude information, and direction information to the server. After the transmission is complete, the terminal waits for a response from the server.

[1310] Step 6:

[1311] Based on the received latitude and longitude information, the server retrieves the elevation data for the relevant area from the elevation database.

[1312] Step 7:

[1313] The server uses the received image data, elevation data, and direction information to prepare for analyzing the topography of the target area.

[1314] Step 8:

[1315] The server inputs the image data into the image generation AI, which then detects artificial objects (buildings, roads, power lines, etc.) in the image.

[1316] Step 9:

[1317] The image generation AI removes any detected artificial objects and generates a terrain image without any artificial objects. This generated terrain image is temporarily stored on the server.

[1318] Step 10:

[1319] The server prepares to send the generated topographical image data to the user's terminal, which is also sent via the Internet.

[1320] Step 11:

[1321] The server sends the generated terrain image to the terminal, confirms the completion of the transmission, and records the operation log.

[1322] Step 12:

[1323] The terminal stores the generated image data received from the server in local storage.

[1324] Step 13:

[1325] The device displays the saved generated image data within the application, allowing the user to visually confirm the terrain with the artificial objects removed.

[1326] Example 1

[1327] 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."

[1328] With conventional technology, artificial objects in landscape photographs often interfere with natural understanding of the terrain. This makes it difficult to obtain accurate and pure terrain information in fields such as disaster prevention, education, and historical research. Furthermore, no system exists for combining and analyzing multiple data sources. To solve this problem, a system is needed that can automatically remove artificial objects from images and generate realistic terrain information.

[1329] 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.

[1330] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring topographical data, server means for generating a topographical image from which artificial objects have been removed using an image generation AI model based on the acquired topographical data, and server means for transmitting the generated topographical image to the terminal means. This allows users to easily obtain topographical images from which artificial objects have been removed, enabling a more accurate understanding of topography in fields such as disaster prevention, education, and historical research.

[1331] "Location information" is data used to identify the location of an object, and specifically includes latitude and longitude information and direction information.

[1332] The "terminal means" is a device that is operated by a user to capture image data and acquire location information, and refers to a smartphone, tablet, or the like.

[1333] "Image data" is digital data that stores visual information of scenery or objects photographed by a terminal means.

[1334] The term "server means" refers to a computer system and its software for receiving data sent from the terminal means and analyzing and processing the data.

[1335] "Topographical data" refers to data relating to the elevation and geographical shape of a particular area, and is obtained from a map database or the like.

[1336] An "image generation AI model" refers to a program or algorithm that uses artificial intelligence to remove certain elements (such as man-made objects) from image data and generate realistic terrain images.

[1337] "Artificial objects" are objects made by humans that do not exist in the natural environment, including buildings, cars, and utility poles.

[1338] A "terrain image" is image data that represents the pure terrain, with artificial objects removed by a generative AI model.

[1339] This invention is a system that uses a smartphone (terminal), a server, an image generation AI model, and terrain data to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research.

[1340] Hardware and software used

[1341] Terminal

[1342] A user takes a photograph of a landscape using a smartphone. This device is equipped with a GPS function and a camera orientation sensor, and acquires latitude, longitude, and orientation information when taking a photograph. This allows the captured image data and location information to be temporarily stored on the device.

[1343] server

[1344] The server analyzes the received image data, latitude and longitude information, and direction information. Specifically, the server performs the following processes.

[1345] Based on the received latitude and longitude information, topographical data for the relevant area is obtained from a map database (e.g., elevation data).

[1346] The terrain is analyzed based on the acquired topographical data, image data, and directional information using terrain analysis software (analysis tools such as Python).

[1347] It uses an image generation AI model to detect and remove artifacts from the received image data, and this AI model operates based on deep learning techniques (using TensorFlow and PyTorch).

[1348] Processing flow

[1349] 1. User photography and data acquisition

[1350] The user takes a picture of a landscape using a smartphone. When taking the picture, the device acquires latitude and longitude information using the GPS function and direction information using the direction sensor, and these data are temporarily stored on the device along with the image data.

[1351] 2. Data transmission

[1352] The device sends the temporarily stored image data, latitude and longitude information, and direction information to the server. Data transmission uses a secure communication protocol.

[1353] 3. Analysis on the server

[1354] The server obtains topographical data for the relevant area based on the received data and performs analysis using analysis software.

[1355] 4. Artificial object removal using image generation AI

[1356] The server sends a prompt to the image generation AI model to generate a terrain image with artificial objects removed. For example, the prompt might say, "Please remove the artificial objects from this photo and generate a natural terrain image."

[1357] 5. Sending the generated image

[1358] The generated terrain image is sent to the user's device, which stores the received terrain image in local storage and displays it to the user.

[1359] Specific examples

[1360] Use case 1: Disaster prevention

[1361] Disaster prevention workers (users) take photos of disaster areas for earthquake risk assessment.

[1362] The smartphone (terminal) sends the image data, latitude and longitude information, and direction information after the photo is taken to the server.

[1363] Based on the received data, the server obtains topographical data for the relevant area and uses image generation AI to generate a topographical image that removes artificial objects.

[1364] The device displays the generated terrain image to the user and performs a risk assessment based on the natural topography of the affected area.

[1365] Use case 2: Education

[1366] A teacher (user) instructs students to acquire terrain images to study the natural terrain around the school.

[1367] Students take photos of the area around the school using their smartphones (devices) and send the image data and location information to the server.

[1368] The server obtains terrain data from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[1369] Students can view the generated terrain images on their smartphones, helping them understand the natural terrain around their school.

[1370] Use Case 3: Historical Research

[1371] Historical researchers (users) take contemporary photographs during fieldwork to investigate ancient battlefields.

[1372] Researchers send image data and location information to a server.

[1373] The server obtains topographical data based on the received data, and recreates past landscapes using old map data and image generation AI.

[1374] Researchers can view historical terrain images generated on smartphones and visually understand the specific terrain of the battlefields at the time.

[1375] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research.

[1376] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1377] The specific flow of this system's program processing

[1378] Step 1: User takes photos and acquires data

[1379] Operation: The user takes a landscape image using a smartphone (device).

[1380] Input: By pressing the capture button, image data captured by the camera, latitude and longitude information obtained by the GPS sensor, and direction information obtained by the direction sensor are collected.

[1381] Data processing: The collected image data and attached location information (latitude, longitude, and direction information) are stored in temporary storage.

[1382] Output: Image data and location information stored on the device.

[1383] Step 2: Sending data

[1384] Operation: The device sends the temporarily stored data to the server.

[1385] Input: Image data and location information saved in step 1.

[1386] Data processing: Image data and location information are sent to a server using the HTTPS protocol or similar.

[1387] Output: Data transferred to the server.

[1388] Step 3: Parsing on the server

[1389] Operation: Based on the received location information, the server obtains topographical data for the area and analyzes the image data.

[1390] Input: Received image data, latitude and longitude information, and direction information.

[1391] Data processing: The server retrieves terrain data based on location information from the map database and extracts terrain features using image analysis software (e.g., Python's OpenCV or GDAL).

[1392] Output: Topographic data and analyzed image data.

[1393] Step 4: Artifact removal using image generation AI

[1394] How it works: The server uses an image-generating AI model to remove artifacts from the images it receives.

[1395] Input: Analyzed image data, topographic data.

[1396] Data processing: The image generation AI model is sent a prompt such as, "Remove artificial objects from this photo and generate a natural-looking landscape image." The AI ​​model uses deep learning algorithms (e.g., TensorFlow or PyTorch) to detect and remove artificial objects.

[1397] Output: Terrain image with artifacts removed.

[1398] Step 5: Submit the generated image

[1399] Operation: The server sends the generated terrain image data to the user's terminal.

[1400] Input: Terrain image with artifacts removed.

[1401] Data processing: The generated topographical image data is sent to the user's device using the HTTPS protocol or similar.

[1402] Output: Generated terrain image saved and displayed on device.

[1403] (Application example 1)

[1404] 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."

[1405] Autonomous vehicles are required to more accurately grasp the landscape data around the vehicle and improve the accuracy of driver assistance systems. To achieve this, pure terrain data that excludes artificial objects is required. However, with current technology, much of the landscape data contains artificial objects, making it difficult to obtain accurate terrain data. This affects the performance of driver assistance systems and poses safety challenges.

[1406] 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.

[1407] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring elevation data, server means for generating a terrain image from which artificial objects have been removed using an image generation model based on the acquired elevation data, server means for transmitting the generated terrain image to the terminal means, and means for displaying the generated terrain image in a driving assistance system of the vehicle. This enables an autonomous vehicle to acquire accurate terrain data from which artificial objects have been removed, thereby improving the accuracy of the driving assistance system.

[1408] Key Word Definitions

[1409] "Location information" is data such as latitude and longitude that indicates the geographic location of an object.

[1410] "Image data" refers to visual information captured by a camera or other imaging device, and that information is stored in digital form.

[1411] "Terminal means" refers to a mobile device such as a smartphone, tablet, or camera, which is operated by a user to obtain location information and image data.

[1412] The "server means" is a computer system for processing and storing data, and receives and processes information sent from the terminal means via a network.

[1413] "Elevation data" is data that indicates information about the height of the earth's surface and is used in analyzing terrain.

[1414] An "image generation model" is an algorithm that uses machine learning and artificial intelligence techniques to generate a new image by eliminating certain elements (such as artificial objects) from an input image.

[1415] "Artifacts" refer to objects such as buildings and vehicles that are created by humans in the natural environment, and are the objects that can be identified in image data.

[1416] A "driving assistance system" is a system that supports automatic driving of a vehicle and the driver, and has the function of recognizing the environment around the vehicle in real time and assisting in appropriate operation.

[1417] MODE FOR CARRYING OUT THE INVENTION

[1418] To implement this invention, it is necessary to build a system according to the following steps.

[1419] 1. Hardware and Software Used

[1420] Hardware:

[1421] Smartphone or vehicle-mounted camera: Used to capture image data.

[1422] GPS sensor on your smartphone or vehicle: Obtains location information.

[1423] Server: Analyzes the received data and generates images.

[1424] software:

[1425] Azure Elevation Data API: Used to retrieve elevation data.

[1426] OpenAI API: Generates terrain images that remove artificial objects using an image generation model.

[1427] Driver assistance system for autonomous vehicles: Displays the generated terrain images to assist vehicle driving.

[1428] 2. System Overview

[1429] The user's smartphone or in-vehicle camera captures the scenery around the vehicle and acquires the image data and location information (latitude, longitude, and direction information). This data is temporarily stored in the device.

[1430] The device then sends the saved image data and location information to the server, which then retrieves elevation data for the area based on the received latitude and longitude information using the Azure elevation data API.

[1431] Based on the acquired elevation data and image data, the server uses OpenAI's image generation model to generate a terrain image that detects and removes artificial objects in the image data. During this generation process, the following prompts are used:

[1432] Prompt Sentence Examples

[1433] Please remove any artificial objects from this image to recreate a purely natural terrain. The latitude of this image is 35.6895 and the longitude is 139.6917. Please remove any artificial buildings or cars while preserving the current terrain.

[1434] The generated terrain image is then sent back to the terminal from the server. Finally, the terminal displays the received terrain image on the driver assistance system of the autonomous vehicle, enabling the vehicle's driver assistance system to support vehicle operation based on accurate terrain data with artificial objects removed.

[1435] 3. Specific Examples

[1436] For example, when a user drives an autonomous vehicle in the streets of Tokyo, the onboard camera captures the surrounding scenery, and the GPS sensor acquires latitude and longitude information. This information is sent to a server, which then uses the Azure elevation data API to collect elevation data. The collected data and captured images are then converted into a pure terrain image using OpenAI's image generation model, eliminating artifacts. The generated image is then displayed on the driver assistance system, providing more accurate driving assistance.

[1437] In this way, the system of the present invention plays an important role in enabling autonomous vehicles to more accurately grasp the surrounding environment and achieve safe driving.

[1438] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1439] Program processing flow

[1440] Step 1: User takes photos and acquires data

[1441] The user takes a photo of the surrounding scenery using their smartphone or in-car camera. When taking a photo, the smartphone or vehicle's GPS sensor acquires latitude and longitude information, and the camera's orientation sensor acquires direction information. The captured image and location information are then temporarily saved on the device.

[1442] Input: A photographed landscape image, latitude and longitude information, and direction information.

[1443] Output: Image data and location information stored on the device.

[1444] Step 2: Sending data

[1445] The terminal transmits the temporarily saved image data, latitude and longitude information, and direction information to the server, and when transmission is complete, waits for a response from the server.

[1446] Input: Image data and location information stored on the device.

[1447] Output: Image data and location information sent to the server.

[1448] Step 3: Parsing on the server

[1449] The server obtains elevation data for the area based on the received latitude and longitude information, using the Azure elevation data API for this process.

[1450] Input: Received latitude and longitude information.

[1451] Output: The retrieved elevation data.

[1452] Step 4: Artifact removal using image generation AI

[1453] The server uses the received image data and elevation data to input prompt statements into OpenAI's image generation model, generating a terrain image that removes artificial objects from the image data.

[1454] Input: Received image data and elevation data, prompt statement.

[1455] Output: Generated terrain image.

[1456] Specific operation:

[1457] 1. Insert a prompt such as "Remove any man-made objects from this image to recreate a purely natural terrain. The image is at latitude 35.6895 and longitude 139.6917. Please remove any man-made buildings or vehicles while preserving the current terrain."

[1458] 2. Send the data to OpenAI's API and perform the artifact removal process.

[1459] Step 5: Submit the generated image

[1460] The server then transmits the generated topographical image data to the user's terminal, checking the integrity of the data during transmission.

[1461] Input: Generated terrain image data.

[1462] Output: Terrain image data sent to the device.

[1463] Step 6: Display by driver assistance system

[1464] The terminal receives terrain image data from the server and displays it on the driver assistance system of the autonomous vehicle, allowing the vehicle to provide driving assistance based on accurate terrain data that has been stripped of artificial objects.

[1465] Input: Terrain image data sent to the device.

[1466] Output: Terrain image data displayed on the driver assistance system.

[1467] This series of processes allows for the acquisition of terrain images with artificial objects removed, which can then be displayed on the driver assistance system of an autonomous vehicle, enabling safer and more accurate driving assistance.

[1468] 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.

[1469] overview

[1470] This system uses a smartphone (terminal), a server, image generation AI, elevation data, and an emotion engine to recreate terrain and past landscapes without artificial objects. This system is useful for deepening understanding of terrain in fields such as disaster prevention, education, and historical research. It also provides flexible visual information that takes into account the user's emotional state.

[1471] Program processing overview

[1472] Step 1: User photography and data acquisition

[1473] The user takes a picture of the scenery using a smartphone. When taking a picture, the smartphone uses its GPS function to obtain latitude and longitude information, and also obtains direction information from the camera's direction sensor. The captured image and location information are then temporarily saved on the device.

[1474] Step 2: Obtaining emotion data

[1475] The smartphone's built-in emotion engine analyzes the user's facial expressions through the camera or performs voice analysis to obtain the user's emotion data, which is also temporarily stored on the device.

[1476] Step 3: Send the data

[1477] The terminal establishes communication for transmitting the acquired image data, latitude and longitude information, direction information, and emotion data to the server. After the transmission is completed, the terminal waits for a response from the server.

[1478] Step 4: Parsing on the server

[1479] The server obtains elevation data for the area based on the received latitude and longitude information, and prepares to analyze the terrain based on the obtained elevation data, image data, and direction information.

[1480] Step 5: Artifact removal using image generation AI

[1481] The server inputs the received image data into the image generation AI to detect man-made objects (buildings, roads, power lines, etc.). The image generation AI removes the detected man-made objects and generates a terrain image without the man-made objects. This generated terrain image is temporarily stored on the server.

[1482] Step 6: Optimize based on user sentiment

[1483] The server analyzes the received user emotion data and selects the most appropriate display format for the terrain image according to the user's emotional state. This optimization process allows the user to receive information more comfortably.

[1484] Step 7: Submit the generated image

[1485] The server prepares to send the generated topographical image data to the user's device. This transmission is also performed via the Internet. The server confirms the completion of the transmission and records the operation log.

[1486] Step 8: Displaying the generated image

[1487] The device stores the generated image data received from the server in local storage. The stored generated image data is displayed within the application, allowing the user to visually confirm the terrain with no artificial objects removed. The display format is optimized based on emotion data, allowing the user to view the information without stress.

[1488] Specific examples

[1489] Use case 1: Disaster prevention

[1490] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[1491] Device: The smartphone sends the captured image data, latitude and longitude information, direction information, and emotion data to the server.

[1492] Server: Based on the received data, the server obtains elevation data for the relevant area from an elevation database and uses image generation AI to generate a terrain image that removes artificial objects.

[1493] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[1494] Use case 2: Education

[1495] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[1496] Device: Students take photos of the mountain behind the school using their smartphones and send the image data, location information, and emotion data to the server.

[1497] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[1498] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[1499] Use Case 3: Historical Research

[1500] User: A historian conducts fieldwork at a site known as a battlefield from the Sengoku period.

[1501] Terminal: The researcher takes a modern photograph and sends the image data, location information, and emotional data to the server.

[1502] Server: Elevation data is obtained based on the received data, and the landscape at that time is recreated using old map data and image generation AI.

[1503] Terminal: Images of the past terrain are sent to the researcher's smartphone, allowing them to visually understand the specific terrain of the battlefield at that time.

[1504] As described above, the present invention has a wide range of applications, and by recreating specific topography and past landscapes, it provides practical value in various fields such as disaster prevention, education, and historical research, and also provides flexible information that responds to the user's emotional state.

[1505] The processing flow will be explained below.

[1506] Step 1:

[1507] A user starts the camera application on their smartphone and takes a picture of a target scene. After taking the picture, the camera application acquires the image data.

[1508] Step 2:

[1509] The device acquires the latitude and longitude information of its current location using the built-in GPS module, and also acquires direction information from the camera's direction sensor.

[1510] Step 3:

[1511] The device temporarily stores the acquired image data, latitude and longitude information, and direction information in its internal storage.

[1512] Step 4:

[1513] The emotion engine installed on the device analyzes the user's facial expressions through the camera or performs voice analysis to obtain the user's emotional data, which is also temporarily stored in the device's internal storage.

[1514] Step 5:

[1515] The device establishes communication to transmit the stored image data, latitude and longitude information, orientation information, and emotion data to a server, typically via the Internet.

[1516] Step 6:

[1517] The device transmits image data, latitude and longitude information, direction information, and emotion data to the server. After transmission is complete, the device waits for a response from the server.

[1518] Step 7:

[1519] Based on the received latitude and longitude information, the server retrieves the elevation data for the relevant area from the elevation database.

[1520] Step 8:

[1521] The server uses the received image data, elevation data, and orientation information to prepare for analyzing the topography of the target area.

[1522] Step 9:

[1523] The server inputs the image data into the image generation AI, which then detects artificial objects (buildings, roads, power lines, etc.) in the image.

[1524] Step 10:

[1525] The image generation AI removes any detected artificial objects and generates a terrain image without any artificial objects. This generated terrain image is temporarily stored on the server.

[1526] Step 11:

[1527] The server analyzes the received user emotional data and selects the optimal display format for the terrain image according to the user's emotional state. As a result of the emotional analysis, the display is customized according to the user's stress and interests.

[1528] Step 12:

[1529] The server prepares to send the generated topographical image data to the user's device, which is also sent via the Internet.

[1530] Step 13:

[1531] The server sends the generated terrain image to the terminal, confirms the completion of the transmission, and records the operation log.

[1532] Step 14:

[1533] The terminal stores the generated image data received from the server in local storage.

[1534] Step 15:

[1535] The device displays the saved generated image data within the application, allowing the user to visually confirm the terrain with no artificial objects removed. The display format is optimized based on the emotion data, allowing the user to view the information without stress.

[1536] Example 2

[1537] 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."

[1538] Current image processing technology has difficulty in removing artificial objects from landscape images and reproducing natural topography. It also lacks the ability to provide optimal information display formats based on the user's emotional state. As a result, it is difficult to provide appropriate information to deepen understanding of topography in fields such as disaster prevention, education, and historical research.

[1539] 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.

[1540] In this invention, the server includes terminal means for acquiring location data, terminal means for capturing at least one image data, server means for receiving the location data and image data transmitted from the terminal means and acquiring terrain data, server means for generating a terrain image from which artificial objects have been removed using a generation AI based on the acquired terrain data, server means for analyzing the user's emotional data and selecting an optimal information display format according to the user's emotional state, and server means for transmitting the generated terrain image to the terminal means. This not only makes it possible to reproduce a natural terrain from which artificial objects have been removed, but also enables flexible information provision according to the user's emotional state.

[1541] "Location data" is information for identifying a location, and typically includes coordinate information and direction information.

[1542] "Terminal means" refers to a device used by a user, specifically a smartphone, tablet, etc.

[1543] "Image data" refers to digital data of photographs or videos taken using a terminal means.

[1544] "Server Means" refers to a central server that receives, analyzes, processes and transmits data.

[1545] "Terrain Data" refers to data relating to terrain, such as elevation information for a particular geographic area.

[1546] "Generative AI" refers to artificial intelligence algorithms and models that process image data to generate new images.

[1547] "Man-made objects" refers to man-made structures that are not natural elements, such as buildings, roads, or power lines.

[1548] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and the like.

[1549] "Information presentation format" refers to the visual or auditory format of information presented to a user.

[1550] "Image generation means" refers to the technology or algorithm used to detect and edit specific objects from image data.

[1551] MODE FOR CARRYING OUT THE INVENTION

[1552] System Configuration

[1553] This invention is a system that includes a user terminal, a central server, an image generation AI, a terrain database, and an emotion analysis engine. The purpose of this system is to generate terrain images that are free of artificial objects and provide them in an optimal information display format according to the user's emotional state.

[1554] Hardware Configuration

[1555] The terminals used are mobile devices such as smartphones and tablets, which must be equipped with a GPS module, a camera, a direction sensor, and a front-facing camera and microphone for emotion analysis.

[1556] Servers use high-performance computer systems to receive, analyze, process, and transmit data, and require large amounts of storage, powerful CPUs and GPUs, and network communication capabilities.

[1557] Software Configuration

[1558] The device comes with an application that integrates a camera app, GPS function, orientation sensor function, and emotion analysis engine. For example, OpenCV and TensorFlow Lite can be used for image processing.

[1559] On the server side, the image analysis system is built using programming languages ​​such as Python and Java, and uses an API to reference the terrain database and generative AI (e.g., Stable Diffusion and GAN models) as an image generation AI model.

[1560] Operation overview

[1561] The user takes a photo of the target scene using their smartphone. This acquires image data, latitude and longitude information from GPS, and camera direction information. Furthermore, emotional data is analyzed from the user's facial expressions and voice, and temporarily saved on the device.

[1562] The device then compiles this data and sends it to a central server. The server then consults an elevation database based on the received latitude and longitude information to obtain topographical data for the relevant area. Based on this data, an image generation AI generates a topographical image that eliminates artificial objects. The AI ​​also analyzes the user's emotional data and selects the optimal information display format based on their emotional state.

[1563] The server then sends the generated terrain image to the terminal, which then displays it, allowing the user to view a natural terrain image with no artificial objects present.

[1564] Specific examples

[1565] Use case 1: Disaster prevention

[1566] Users: Disaster prevention workers take photos of disaster areas for earthquake risk assessment.

[1567] Device: The smartphone sends the captured image data, latitude and longitude information, direction information, and emotion data to the server.

[1568] Server: Based on the received data, the server obtains topographical data for the relevant area from an elevation database, and uses image generation AI to generate a topographical image that removes artificial objects.

[1569] Terminal: The generated terrain image is displayed to the user, allowing the natural topography of the affected area to be used for risk assessment.

[1570] Use case 2: Education

[1571] User: A teacher guides students to capture terrain images to study the natural terrain around their school.

[1572] Device: Students take photos of the mountain behind the school using their smartphones and send the image data, location information, and emotion data to the server.

[1573] Server: Elevation data is obtained from the received data, and the image generation AI generates a terrain image that removes artificial objects.

[1574] Device: Students can view the generated terrain images on their smartphones, helping them understand the natural topography of the mountain behind the school.

[1575] Prompt statement

[1576] Here are some example prompts for the image generation AI:

[1577] "Detect and remove man-made objects such as buildings and power lines from this landscape to generate a natural-looking terrain image."

[1578] An example prompt for the sentiment analysis engine is:

[1579] "Analyze the user's facial expressions to determine their current emotional state."

[1580] As explained above, the present invention provides practical value in fields such as disaster prevention, education, and historical research, and also realizes flexible information provision according to the user's emotional state.

[1581] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1582] Step 1:

[1583] The user uses a smartphone to capture the scenery they want to shoot. When taking the photo, the device uses its GPS function to obtain latitude and longitude information, and obtains orientation information from the camera's orientation sensor. This provides image data, latitude and longitude information, and orientation information as input. This data is temporarily stored within the device. Specifically, the user launches the smartphone's camera app and takes a photo of the scenery in the specified area, at the same time automatically activating the GPS function and orientation sensor to obtain the necessary data.

[1584] Step 2:

[1585] After capturing the image, the device activates its emotion engine and analyzes the user's facial expressions through the camera. If necessary, it records the user's voice and performs audio analysis. This provides input data on the user's facial expressions and voice, which are then used to generate emotion data. This emotion data is temporarily stored within the device. Specifically, the emotion engine captures the user's facial expressions, and the emotion analysis algorithm analyzes them to determine the user's emotional state.

[1586] Step 3:

[1587] The device establishes communication to send the acquired image data, latitude and longitude information, direction information, and emotion data to the server. Once data transmission is complete, it waits for a response from the server. Data stored in the device as input is sent to the server, which triggers the server to receive the data. Specifically, the device connects to the server using Wi-Fi or mobile data communication and sends all data at once.

[1588] Step 4:

[1589] The server retrieves elevation data for the relevant area from the terrain database based on the received latitude and longitude information. The latitude and longitude information is provided as input, and terrain data is retrieved based on that. The server then prepares to analyze the terrain based on this data. Specifically, the server calls the elevation database API to retrieve the necessary terrain data.

[1590] Step 5:

[1591] The server inputs the received image data into the generation AI, which then processes it to detect and remove man-made objects (buildings, roads, power lines, etc.). Image data is provided as input, and the generation AI analyzes it to generate a terrain image with the man-made objects removed. This generated terrain image is temporarily stored on the server. Specifically, the server sends a prompt to the image generation AI saying, "Please detect and remove man-made objects such as buildings and power lines from this landscape and generate a natural-looking terrain image."

[1592] Step 6:

[1593] The server analyzes the received user emotion data and selects the optimal display format for the terrain image based on the user's emotional state. The emotion data is provided as input, and the display format is selected based on that. Specifically, the server applies an emotion data analysis algorithm to determine the optimal display format based on the user's emotional state.

[1594] Step 7:

[1595] The server prepares to send the generated terrain image data to the user's device. When the data transmission is complete, an operation log is recorded. The generated terrain image data is provided as input, and this data is sent to the user's device. Specifically, the server formats the image data into transmission packets and sends them to the terminal via the Internet.

[1596] Step 8:

[1597] The device saves the generated image data received from the server in local storage. The saved generated image data is displayed in the application so that the user can check it. The image data received from the server is provided as input and displayed in the application. Specifically, the device decompresses the data received from the server and saves it in local storage, and the application reads and displays the data.

[1598] (Application example 2)

[1599] 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."

[1600] Conventional food delivery systems have the problem of being unable to flexibly respond to obstacles and temporary traffic restrictions that delivery workers encounter on their routes. Furthermore, routes are provided without taking into account the emotional state of delivery workers, which can increase stress and reduce work efficiency. Furthermore, conventional systems do not adequately consider the topography of the delivery route, nor do they adequately optimize the safety and comfort of delivery work.

[1601] 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.

[1602] In this invention, the server includes terminal means for acquiring location information, terminal means for capturing at least one image data, server means for receiving the location information and image data transmitted from the terminal means and acquiring elevation data, server means for generating a terrain image from which artificial objects have been removed using an image generation AI based on the acquired elevation data, server means for transmitting the generated terrain image to the terminal means, and server means for receiving emotion data acquired by the terminal means and selecting an optimal terrain image display format based on the emotion data. This enables delivery personnel to deliver goods along safe and efficient routes that take into account their emotional state as well as the latest terrain information.

[1603] "Location information" is information that indicates the geographical location where a target terminal or object is currently located, and typically includes latitude, longitude, and direction information.

[1604] "Terminal means" refers to a device that has the function of acquiring location information, image data, emotional data, etc. and transmitting it to a server.

[1605] "Image data" is a digital representation of visual information captured by a photographic device such as a camera.

[1606] "Elevation data" is geographical data that indicates the height of a specific location above the ground surface, and is used in topographical analysis.

[1607] "Server Means" refers to a system that has the function of receiving, processing, and transmitting data through the cloud or a network.

[1608] "Image generation AI" is an algorithm or system that uses artificial intelligence technology to analyze input images and generate new images.

[1609] "Emotion data" is digital information that indicates the user's emotional state, as analyzed from the user's facial expressions, voice, etc.

[1610] "Terrain image display format" refers to the layout and visual style when displaying the generated terrain image to the user.

[1611] This invention provides a system for food delivery businesses that enables delivery personnel to deliver food along safe and efficient routes. The system is composed of a smartphone, a server, an elevation database, an image generation AI, and an emotion engine.

[1612] First, the delivery person uses their smartphone to take a photo of the scenery along their current delivery route. The smartphone uses its GPS to obtain latitude and longitude information and direction information from the camera's orientation sensor. The smartphone's built-in emotion engine also analyzes the delivery person's facial expressions through the camera to obtain emotional data. This data is temporarily stored on the device.

[1613] Next, the device transmits the acquired image data, latitude and longitude information, direction information, and emotion data to the server. The server acquires elevation data for the relevant area based on the received latitude and longitude information, and uses image generation AI to generate a terrain image that removes artificial objects (buildings, roads, power lines, etc.). This terrain image is temporarily stored on the server.

[1614] Furthermore, the server analyzes the received emotional data and selects the optimal terrain image display format based on the delivery person's emotional state, allowing the delivery person to receive route information more comfortably. After this optimization process, the generated terrain image data is sent to the delivery person's device. The device then stores the received generated image data in local storage, allowing the delivery person to visually confirm the terrain with no artificial objects removed while understanding the delivery route.

[1615] Specifically, the following hardware and software are used:

[1616] Smartphone: Sensors include GPS, orientation sensor, camera, and emotion engine (e.g., Affectiva's SDK)

[1617] Server: Terrain database and elevation database as databases, GAN (Generative Adversarial Network) model as image generation AI, Python library (TensorFlow, OpenCV, etc.) as analysis engine

[1618] Specific examples

[1619] When a delivery person is driving through an area in the city center where road construction and temporary traffic restrictions are frequent, the delivery person selects an avoidance route based on the latest artificial object-free terrain image that reflects the situation.

[1620] Prompt Sentence Examples

[1621] "Create a system that allows food delivery riders to take photos of their latest delivery route in Roppongi, Minato-ku, Tokyo with their smartphones, and then uses the photos to reconstruct natural terrain information by removing artificial objects, and then generates and displays the optimal delivery route to reduce stress."

[1622] This system enables delivery personnel to carry out safe and efficient deliveries based on up-to-date terrain information and thoughtful route information.

[1623] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1624] Step 1:

[1625] The delivery person uses a device (smartphone) to take photos of the local scenery.

[1626] —Input: landscape image, GPS location, direction

[1627] —Operation: The device camera captures a landscape image, while the GPS sensor acquires latitude and longitude information and the orientation sensor acquires orientation information. These data are temporarily stored in local storage.

[1628] —Output: Landscape image data, latitude and longitude information, direction information

[1629] Step 2:

[1630] The device (smartphone) analyzes the delivery person's facial expressions using an emotion engine to obtain emotional data.

[1631] —Input: Facial expression of delivery person (camera image), voice data (if necessary)

[1632] - Operation: The emotion engine (e.g., Affectiva's SDK) installed on the device analyzes the delivery person's facial expressions through the camera and obtains their emotional state as numerical data.

[1633] —Output: Emotion data

[1634] Step 3:

[1635] The terminal (smartphone) transmits the acquired image data, latitude and longitude information, direction information, and emotion data to the server.

[1636] —Input: Image data, latitude and longitude information, direction information, emotion data

[1637] Operation: The device establishes communication with the server via the Internet, sends the above information to the server, and then waits for a response from the server.

[1638] —Output: Various data sent to the server

[1639] Step 4:

[1640] The server obtains elevation data for the relevant area based on the received latitude and longitude information.

[1641] —Input: Latitude and longitude information

[1642] Operation: The server accesses the terrain database and the elevation database to obtain the elevation data corresponding to the relevant latitude and longitude information.

[1643] —Output: Elevation data

[1644] Step 5:

[1645] The server uses image generation AI to generate a terrain image that removes artificial objects.

[1646] —Input: Image data, elevation data, orientation information

[1647] Operation: The server inputs the received image data into an image generation AI (such as a GAN model) to detect and remove artificial objects. As a result, a terrain image without artificial objects is generated.

[1648] —Output: Terrain image with artifacts removed

[1649] Step 6:

[1650] The server selects the optimum display format for the terrain image based on the emotion data.

[1651] —Input: emotion data, terrain image

[1652] - Operation: The server analyzes the emotional data and selects the display format that allows the user to receive information most comfortably. The display format may include color filters, contrast adjustment, etc.

[1653] —Output: Optimized terrain image display format

[1654] Step 7:

[1655] The server transmits the generated topographical image data to the terminal.

[1656] —Input: Optimized terrain image

[1657] - Operation: The server sends the generated topographical image data to the terminal. After the transmission is complete, the operation log is recorded.

[1658] —Output: Terrain image data sent to the device

[1659] Step 8:

[1660] The terminal displays the generated image data received from the server.

[1661] —Input: Generated image data

[1662] Operation: The device receives terrain image data from the server, stores it in local storage, and displays it through the application. The delivery person visually checks the terrain, free of artificial objects, and delivers along a safe and efficient route.

[1663] —Output: Terrain image displayed on the terminal

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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.

[1670] 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).

[1671] 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.

[1672] 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."

[1673] 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.

[1674] 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).

[1675] 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.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] The following is further disclosed regarding the above embodiment.

[1686] (Claim 1)

[1687] a terminal means for acquiring location information;

[1688] a terminal means for capturing at least one image data;

[1689] a server means for receiving the location information and image data transmitted from the terminal means and acquiring the altitude data;

[1690] A server means for generating a topographical image from which artificial objects have been removed using an image generation AI based on the acquired elevation data;

[1691] a server means for transmitting the generated topographical image to the terminal means;

[1692] A system including:

[1693] (Claim 2)

[1694] The system of claim 1 , wherein the location information includes latitude and longitude information and orientation information.

[1695] (Claim 3)

[1696] 10. The system of claim 1, wherein the server means further comprises image recognition AI means for detecting man-made objects.

[1697] "Example 1"

[1698] (Claim 1)

[1699] a terminal means for acquiring location information;

[1700] a terminal means for capturing at least one image data;

[1701] a server means for receiving the location information and image data transmitted from the terminal means and acquiring topographical data;

[1702] A server means for generating a terrain image from which artificial objects have been removed using an image generation AI model based on the acquired terrain data;

[1703] a server means for transmitting the generated topographical image to the terminal means;

[1704] A system including:

[1705] (Claim 2)

[1706] The system of claim 1 , wherein the location information includes latitude and longitude information and direction information.

[1707] (Claim 3)

[1708] 10. The system of claim 1, wherein the server means further comprises image recognition AI means for detecting man-made objects.

[1709] "Application Example 1"

[1710] New Claims

[1711] (Claim 1)

[1712] a terminal means for acquiring location information;

[1713] a terminal means for capturing at least one image data;

[1714] a server means for receiving the location information and image data transmitted from the terminal means and acquiring the altitude data;

[1715] a server means for generating a topographical image from which artificial objects have been removed using an image generation model based on the acquired elevation data;

[1716] a server means for transmitting the generated topographical image to the terminal means;

[1717] means for displaying the generated terrain image on a driving assistance system of a vehicle;

[1718] A system including:

[1719] (Claim 2)

[1720] The system of claim 1 , wherein the location information includes latitude and longitude information and orientation information.

[1721] (Claim 3)

[1722] 10. The system of claim 1, wherein the server means further comprises image recognition AI means for detecting man-made objects.

[1723] "Example 2: Combining Emotion Engines"

[1724] (Claim 1)

[1725] a terminal means for acquiring location data;

[1726] a terminal means for capturing at least one image data;

[1727] a server means for receiving the position data and image data transmitted from the terminal means and acquiring the topographical data;

[1728] A server means for generating a terrain image from which artificial objects have been removed using a generation AI based on the acquired terrain data;

[1729] a server means for analyzing the user's emotional data and selecting an optimal information display format according to the user's emotional state;

[1730] a server means for transmitting the generated topographical image to the terminal means;

[1731] A system including:

[1732] (Claim 2)

[1733] The system of claim 1 , wherein the location data includes coordinate information and orientation information.

[1734] (Claim 3)

[1735] The system of claim 1 , wherein the server means further comprises image generation means for detecting artifacts.

[1736] "Application example 2 when combining emotion engines"

[1737] (Claim 1)

[1738] a terminal means for acquiring location information;

[1739] a terminal means for capturing at least one image data;

[1740] a server means for receiving the location information and image data transmitted from the terminal means and acquiring the altitude data;

[1741] A server means for generating a topographical image from which artificial objects have been removed using an image generation AI based on the acquired elevation data;

[1742] a server means for transmitting the generated topographical image to the terminal means;

[1743] a server means for receiving the emotion data acquired by the terminal means and selecting an optimum terrain image display format based on the emotion data;

[1744] A system including:

[1745] (Claim 2)

[1746] The system of claim 1 , wherein the location information includes latitude and longitude information and orientation information.

[1747] (Claim 3)

[1748] 10. The system of claim 1, wherein the server means further comprises image recognition AI means for detecting man-made objects. [Explanation of symbols]

[1749] 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 terminal means for acquiring location information; a terminal means for capturing at least one image data; a server means for receiving the location information and image data transmitted from the terminal means and acquiring the altitude data; A server means for generating a topographical image from which artificial objects have been removed using an image generation AI based on the acquired elevation data; a server means for transmitting the generated topographical image to the terminal means; A system including:

2. The system of claim 1 , wherein the location information includes latitude and longitude information and orientation information.

3. The system of claim 1 , wherein the server means further comprises image recognition AI means for detecting man-made objects.

Images