System

Abstract

A system is provided.SOLUTION: The system includes a means for acquiring a satellite image, a generation artificial intelligence means for increasing the resolution of the acquired satellite image, an image recognition artificial intelligence means for extracting geographic information from the image whose resolution is increased, a means for automatically generating map data on the basis of the extracted geographic information, and a communication means for providing the generated map data.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] Conventional map creation involves human intervention, which is costly and time-consuming, and delays in updates can lead to discrepancies with reality. This also hinders the development of fields that require accurate map data, such as autonomous driving. Given this background, there is a demand for technology that can provide the latest, accurate map data quickly and at low cost. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring satellite images, an AI generation means for increasing the resolution of the acquired satellite images, an AI image recognition means for extracting geographic information from the increased resolution images, a means for automatically generating map data based on the extracted geographic information, and a communication means for providing the generated map data. This system significantly reduces the cost and time required for map creation and makes it possible to always provide the latest map data.

[0006] "Satellite imagery" refers to images of the Earth's surface taken using a satellite.

[0007] "High-resolution" is the process of using artificial intelligence techniques to improve the resolution of low-resolution images and add detailed information.

[0008] "Generative artificial intelligence" is a technology that uses deep learning and neural networks to generate or convert images and data.

[0009] "Image recognition artificial intelligence" is a technology that uses artificial intelligence to identify objects and patterns in images and extract information about them.

[0010] "Geographic information" refers to information about the location and shape of the Earth's surface, such as topography, buildings, roads, and bodies of water.

[0011] "Map data" refers to digital data of maps created based on geographic information.

[0012] "Automatic generation" is the process by which a system automatically generates data or information without human intervention.

[0013] "Communication means" are techniques or methods for sending and receiving data. [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] The present invention provides a system for automatically generating map data after acquiring satellite images and increasing their resolution and extracting geographic information. Specific embodiments for carrying out the present invention will now be described.

[0036] System configuration

[0037] This system consists of the following main components:

[0038] 1. Server

[0039] Acquiring satellite images

[0040] High-resolution satellite images

[0041] Extracting geographic information

[0042] Map data generation

[0043] Saving map data

[0044] Processing user requests

[0045] Map data provided

[0046] 2. Terminal

[0047] User Interface

[0048] Viewing map data

[0049] Download map data

[0050] 3. Users

[0051] Map data request

[0052] Use of map data

[0053] Program processing

[0054] Acquiring satellite images

[0055] The server retrieves satellite imagery for a specified area. To do this, the server uses the API of an external satellite data provider to request the required area and date and time. For example, the server may download satellite imagery for an entire city taken on a specific date.

[0056] High-resolution satellite images

[0057] The acquired satellite images are then converted to high resolution using generation AI. The server inputs low-resolution image data into the generation AI to generate a detailed image. For example, an image taken at 300 dpi can be converted to 600 dpi, and detailed information can be added.

[0058] Extracting geographic information

[0059] Image recognition AI is used to extract geographic information from high-resolution images. The server recognizes information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel.

[0060] Map data generation

[0061] Map data is automatically generated based on the extracted geographic information. The server generates map data in standard GIS format based on the information stored in the database. For example, roads, buildings, parks, etc. are plotted on the map.

[0062] Processing user requests and providing map data

[0063] The user sends a request for map data to the server via their device. The server responds to the user's request by searching for and providing the latest map data for the relevant area. The user can then view the map data on their device and download it as needed.

[0064] Specific examples

[0065] For example, if a user wants to obtain the latest map data for Tokyo, the process would be as follows:

[0066] 1. The user requests the latest map data for Tokyo via the device's web browser.

[0067] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[0068] 3. Extract geographic information using image recognition AI and store it in a database.

[0069] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[0070] 5. The server provides the generated map data to the user's device, where the user can view and download it.

[0071] This invention significantly reduces the cost and time required for map creation and provides up-to-date map information, making it a useful tool in fields that require accurate map data, such as autonomous driving and logistics.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The server obtains satellite imagery for a specified area. The server sends a request to an external satellite data provider's API, specifying the required region and date and time dataset. For example, the server downloads satellite imagery for the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[0075] Step 2:

[0076] The server saves the downloaded satellite images in the internal storage. The acquired satellite images remain in low resolution, but are passed on to the next process for higher resolution.

[0077] Step 3:

[0078] The server inputs acquired satellite images into the generative AI model. The server converts the image data into an input format for the generative AI (for example, converting it into a pixel array).

[0079] Step 4:

[0080] The server runs a generation AI to convert low-resolution images to high-resolution ones. The generation AI increases the image resolution and adds detailed information. For example, converting a 300 dpi image to 600 dpi allows the details of buildings and roads to be distinguished.

[0081] Step 5:

[0082] The server saves the high-resolution image to its internal storage, ready to be passed on to the next processing step.

[0083] Step 6:

[0084] The server inputs the high-resolution image into the image recognition AI. The server then converts the image data into an input format for the image recognition AI.

[0085] Step 7:

[0086] The server runs image recognition AI to identify and extract geographic information such as terrain, buildings, and roads. The image recognition AI uses pattern recognition technology to extract various geographic elements pixel by pixel. For example, it identifies the width of roads, the height of buildings, and the flow of rivers.

[0087] Step 8:

[0088] The server saves the extracted geographic information in a database. The extracted information is stored in the database as structured data (e.g., GeoJSON format).

[0089] Step 9:

[0090] The server generates map data based on the geographic information stored in the database. The map data generation engine uses the stored information to visualize each geographic element and generate map data. For example, roads, buildings, parks, etc. are plotted on the map.

[0091] Step 10:

[0092] The server exports the generated map data to a GIS format (e.g. Shapefile), saves the exported map data in a file format, and prepares it for distribution to users.

[0093] Step 11:

[0094] A user sends a request to the server using a device (such as a PC or smartphone). The user accesses the map data API endpoint using a web browser and requests the latest map data for the relevant area (e.g., "latest map of Tokyo").

[0095] Step 12:

[0096] The server receives the user's request and searches for map data for the corresponding area. The server retrieves the latest map data for the specified area from the database.

[0097] Step 13:

[0098] The server returns the acquired map data to the user's device, and the map data is provided to the user in an appropriate format (e.g., GIS format, image format).

[0099] Step 14:

[0100] The user can view and use the latest map data returned on their device. The user can check the map data and download it or integrate it into their own application if necessary.

[0101] Through these processing steps, the system of the present invention can efficiently provide users with the most up-to-date, high-quality map data.

[0102] Example 1

[0103] 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."

[0104] Conventional map data generation systems have had problems with the time and effort required for processes such as acquiring satellite images, increasing the resolution of the images, extracting geographic information, and generating map data. Furthermore, performing these tasks with high accuracy requires advanced specialized knowledge and technology, resulting in increased costs and reduced efficiency. Providing the latest map information quickly and accurately has proven particularly challenging, making highly reliable map data essential in fields such as autonomous driving and logistics.

[0105] 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.

[0106] In this invention, the server includes means for acquiring satellite images of a specified area, means for increasing the resolution of the acquired satellite images using generation AI, means for extracting geographic information from the increased resolution satellite images using image recognition AI, means for automatically generating map data in a geographic information system format based on the extracted geographic information, and communication means for providing the generated map data. This makes it possible to generate and provide the latest map data more quickly and accurately than conventional systems.

[0107] "Means for acquiring satellite images of a designated area" refers to devices or software that acquire satellite images of a designated area through the API of an external satellite data provider, etc.

[0108] "Means for increasing resolution using generative AI" refers to devices or software that increase the resolution of low-resolution satellite images using a generative AI model (e.g., an image generation AI model).

[0109] "Means for extracting geographic information using image recognition artificial intelligence" refers to devices or software that use image recognition technology to identify geographic elements such as roads, buildings, and parks from high-resolution satellite images and extract them as data.

[0110] "Means for automatically generating map data in geographic information system format" refers to devices or software that generate map data in standard GIS (geographic information system) format based on extracted geographic information.

[0111] The "communication means for providing the generated map data" refers to a network communication device or software for providing the generated map data to the user's terminal.

[0112] "Remote sensing technology" is a technology for collecting information on the ground from remote locations such as satellites and aircraft.

[0113] A "user interface" is an interface through which a user interacts with a system, including a web browser or application form.

[0114] MODE FOR CARRYING OUT THE INVENTION

[0115] The present invention provides a system for automatically generating map data after acquiring satellite images and increasing their resolution and extracting geographic information. Specific embodiments for carrying out the present invention will now be described.

[0116] System configuration

[0117] This system consists of the following main components:

[0118] 1. Server:

[0119] Acquiring satellite images

[0120] High-resolution satellite images

[0121] Extracting geographic information

[0122] Map data generation

[0123] Saving map data

[0124] Processing user requests

[0125] Map data provided

[0126] 2. Terminal:

[0127] User Interface

[0128] Viewing map data

[0129] Download map data

[0130] 3. User:

[0131] Map data request

[0132] Use of map data

[0133] Acquiring satellite images

[0134] The server obtains satellite images of a specific area. In this process, the server uses the API of an external satellite data provider (for example, an external resource provision API) to make a request specifying the desired area and date and time. By using remote sensing technology, the server can obtain the latest and most detailed satellite images. For example, the server downloads satellite images of an entire city taken on a specific date.

[0135] High-resolution satellite images

[0136] The acquired low-resolution satellite images are then converted to high resolution using artificial intelligence. The server inputs a prompt to the image generation AI model (e.g., Stable Diffusion or DALL-E) to convert the low-resolution image to high resolution, and the process is carried out. As a concrete example, an image taken at 300 dpi is converted to 600 dpi, and detailed information is added. An example of a prompt is as follows:

[0137] "Please increase the resolution of a 300 dpi satellite image of Tokyo to 600 dpi."

[0138] Extracting geographic information

[0139] Image recognition AI is used to extract geographic information from high-resolution satellite images. The server uses image recognition AI (for example, YOLO or Mask R-CNN) to identify information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel. This makes it possible to obtain detailed geographic information.

[0140] Map data generation

[0141] The server automatically generates map data based on the extracted geographic information. This process outputs the data in a standard Geographic Information System (GIS) format. The server uses GIS software (e.g., QGIS or ArcGIS) to draw a map based on the information stored in the database and export it in GIS format. For example, it plots roads, buildings, parks, etc. on the map. An example of a prompt is:

[0142] "Generate the latest map data in GIS format based on the extracted geographic information."

[0143] Provision and viewing of map data

[0144] The map data generated by the server is provided in response to user requests. When a user requests map data via a device, the server searches for the latest map data for the relevant area and sends it to the device. The device then displays the received map data so that the user can view it. Furthermore, the user can download map data as needed. In this case, the user interface on the device is implemented using tools such as Google Maps API and Leaflet.

[0145] This invention will significantly reduce the cost and time required for map creation and provide constantly up-to-date map information, making it a useful tool in fields that require accurate map data, such as autonomous driving and logistics.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Program processing flow

[0148] Step 1:

[0149] Submitting a User Request

[0150] A user requests the latest map data for a specific area via a terminal.

[0151] Input: The area name and request content specified by the user, for example, "latest map data for Tokyo."

[0152] How it works: A user enters a request into the search field in their device's web browser and clicks the submit button.

[0153] Output: Request data is sent from the terminal to the server.

[0154] Step 2:

[0155] Acquiring satellite images

[0156] The server receives the user's request and retrieves satellite images of the specified area.

[0157] Input: Request data from the user (e.g., "latest satellite image of Tokyo").

[0158] How it works: The server accesses the API of an external satellite data provider and requests satellite imagery for a specified region and date and time.

[0159] Output: The acquired low-resolution satellite image data is stored in the server's internal storage.

[0160] Step 3:

[0161] High-resolution satellite images

[0162] The low-resolution satellite images acquired by the server are converted to high resolution using artificial intelligence.

[0163] Input: Acquired low-resolution satellite image data.

[0164] How it works: The server inputs a prompt to an image generation AI model (e.g., Stable Diffusion or DALL-E) and performs the process of converting low-resolution images to high-resolution ones.

[0165] Example prompt: "Please increase the resolution of a 300 dpi satellite image of Tokyo to 600 dpi."

[0166] Output: The generated high-resolution satellite image data is stored in the server's internal storage.

[0167] Step 4:

[0168] Extracting geographic information

[0169] The server extracts geographic information from high-resolution satellite images using image recognition artificial intelligence.

[0170] Input: High-resolution satellite image data.

[0171] How it works: The server uses image recognition AI (e.g., YOLO or Mask R-CNN) to identify geographical elements such as roads, buildings, and parks in the image and extract them as data.

[0172] Output: The extracted geographic information data is stored in a database on the server.

[0173] Step 5:

[0174] Map data generation

[0175] The server automatically generates map data based on the extracted geographic information.

[0176] Input: Geographical information data stored in a database.

[0177] How it works: The server uses GIS software (e.g. QGIS or ArcGIS) to draw a map from the information in the database and export it in GIS format.

[0178] Example prompt: "Generate up-to-date map data in GIS format based on the extracted geographic information."

[0179] Output: The generated map data is saved in the server's internal storage.

[0180] Step 6:

[0181] Provision and viewing of map data

[0182] The server transmits the generated map data to the terminal in response to a user request.

[0183] Input: User request data and generated map data.

[0184] Operation: The server retrieves the generated map data and sends the corresponding data to the user's device.

[0185] Output: Map data is sent to the device.

[0186] The device displays the received map data so that the user can view it. The device displays the map data using the Google Maps API or Leaflet, and the user can scroll and zoom.

[0187] Output: Users can also download map data as needed.

[0188] (Application example 1)

[0189] 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."

[0190] Navigation for autonomous vehicles requires highly accurate and real-time updates of map data. However, current systems do not update map data frequently or accurately enough, which may result in reduced safety and efficiency during driving. In addition, there is a lack of systems that can quickly and automatically acquire and provide the latest geographic information. To solve this issue, it is necessary to provide autonomous vehicles with the latest high-resolution map data to improve navigation accuracy and safety.

[0191] 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.

[0192] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, and means for navigating an autonomous vehicle using the provided map data, thereby making it possible to provide the latest high-resolution map data to autonomous vehicles in real time.

[0193] "Satellite imagery" refers to images taken from satellites to capture the Earth's surface and atmosphere.

[0194] "High resolution" is the process of increasing the detail of an image and converting it into a clearer image.

[0195] "Generative artificial intelligence" is a technology that uses AI technology to generate data and improve data resolution.

[0196] "Image recognition artificial intelligence" is a technology that uses AI technology to extract and recognize specific information from images.

[0197] "Geographic information" refers to information about a specific area on the earth's surface, such as information about topography, buildings, roads, etc.

[0198] "Map data" refers to data necessary to aggregate geographic information and display it as a map.

[0199] "Auto-generation" is the process by which a system automatically generates data or information.

[0200] "Communication means" refers to the technologies and tools used to send and receive data and information.

[0201] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without a human driver.

[0202] "Navigation" is a function that provides the optimal route to reach a destination.

[0203] The present invention is a system for providing highly accurate map data for autonomous vehicles. The system acquires satellite images, enhances their resolution, extracts geographic information, and then generates and provides map data suitable for the navigation system of the autonomous vehicle. Specific embodiments for implementing the invention are described below.

[0204] System configuration

[0205] server

[0206] The server has the following main functions:

[0207] 1. Obtaining satellite imagery - The server uses the API of an external satellite data provider to obtain satellite imagery for the specified area and date and time. For example, an API request is sent to obtain the latest satellite imagery of Tokyo.

[0208] 2. High-resolution imagery - The acquired satellite images are then made high-resolution using AI. For example, a 300 dpi image is converted to 600 dpi to enhance the details.

[0209] 3. Extraction of geographic information - Geographic information is extracted from the high-resolution images using image recognition AI. The server stores this geographic information in a database.

[0210] 4. Map Data Generation - Automatically generate map data based on the extracted geographic information. Export and save the map data in standard GIS formats.

[0211] 5. Communication - Sending the provided map data to the autonomous vehicle's navigation system.

[0212] Terminal

[0213] The user's device will be the interface of the autonomous vehicle equipped with the navigation system. Specifically, it will have the following functions:

[0214] 1. User Interface - Provides real-time map data to users on the autonomous vehicle's navigation system.

[0215] 2. View map data - Display the provided map data in real time, set destinations and get route guidance.

[0216] 3. Navigation - Using the latest map data to provide optimal routes and support autonomous driving.

[0217] User

[0218] The user is a person riding in an autonomous vehicle. The user performs the following actions:

[0219] 1. Map Data Request - Request the latest map data through the navigation system.

[0220] 2. Use of map data - Use the navigation function of the autonomous vehicle to head to your destination.

[0221] Specific examples

[0222] For example, if a user uses an autonomous vehicle that uses the latest map data for Tokyo, the process would be as follows:

[0223] 1. The user requests the latest map data for Tokyo via the navigation system.

[0224] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[0225] 3. Extract geographic information using image recognition AI and store it in a database.

[0226] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[0227] 5. The server provides the generated map data to the navigation system of the autonomous vehicle, and the user uses it to head to their destination.

[0228] Prompt Sentence Examples

[0229] The following is an example of a prompt to be input to a generative AI model used to enhance satellite imagery and extract geographic information:

[0230] Obtain high-resolution satellite imagery for a specified area and date / time. Extract detailed geographic information from the imagery and generate map data based on it. Example: Tokyo, 2023-10-10, map data including roads, buildings, and terrain information.

[0231] The present invention makes it possible to provide automated driving vehicles with real-time, highly accurate map data, thereby realizing safe and efficient driving.

[0232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0233] Step 1:

[0234] The server receives a request for map data from the user, including the requested region and date and time, and uses this information to send a request to the API of an external satellite data provider.

[0235] Step 2:

[0236] The server obtains satellite images for the specified area and date and time from the satellite data provider. The input is the satellite image obtained from the API response. The server stores this image in memory.

[0237] Step 3:

[0238] The server inputs the acquired satellite image into a generative AI model and performs high-resolution image generation. Specifically, low-resolution satellite images are input into the generative AI, and a high-resolution, detailed image is output. This process improves the image resolution.

[0239] Step 4:

[0240] The server inputs high-resolution satellite images into an image recognition AI (artificial intelligence) to extract geographic information, which analyzes information such as roads, buildings, and terrain pixel by pixel and outputs structured data for storage in a database.

[0241] Step 5:

[0242] The server automatically generates map data based on the extracted geographic information. Specifically, it organizes the geographic information in the database in GIS format and generates map data. This result is then output in a standard map file format (e.g., GeoJSON).

[0243] Step 6:

[0244] The server transmits the generated map data to the navigation system of the autonomous vehicle. The input is the generated map data, and the output is a communication packet containing the data. In this step, the data is transmitted using a communication means.

[0245] Step 7:

[0246] The terminal displays the received map data in real time on the navigation system of the autonomous vehicle. The input is map data received from the communication means, and the output is the latest map information displayed on the display inside the vehicle. The user uses this map data to set destinations and get route guidance.

[0247] Step 8:

[0248] The autonomous vehicle navigates using the map data provided by the device. Specifically, it references the latest high-resolution map data to support safe and efficient driving. The input is the latest map data, and the output is the autonomous vehicle's driving route.

[0249] This series of processes enables the provision of real-time, highly accurate map data, improving navigation for autonomous vehicles.

[0250] 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.

[0251] The present invention combines a system that acquires satellite images, enhances their resolution, extracts geographic information, and then automatically generates map data, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0252] System configuration

[0253] This system consists of the following main components:

[0254] 1. Server

[0255] Acquiring satellite images

[0256] High-resolution satellite images

[0257] Extracting geographic information

[0258] Map data generation

[0259] Saving map data

[0260] Processing user requests

[0261] Recognizing user emotions with an emotion engine

[0262] Map data provided

[0263] 2. Terminal

[0264] User Interface

[0265] Emotion input means (e.g., voice recognition, facial expression recognition)

[0266] Viewing map data

[0267] Download map data

[0268] 3. Users

[0269] Map data request

[0270] Emotion data input (voice and facial expressions)

[0271] Use of map data

[0272] Program processing

[0273] Acquiring satellite images

[0274] The server obtains satellite images of a specified area. To do this, the server uses the API of an external satellite data provider to request the required area and date and time. For example, the server downloads satellite images of the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[0275] High-resolution satellite images

[0276] The acquired satellite images are then converted to high resolution using generation AI. The server inputs the low-resolution image data into the generation AI to generate a detailed image. For example, a 300 dpi image can be converted to 600 dpi, and detailed information can be added.

[0277] Extracting geographic information

[0278] Image recognition AI is used to extract geographic information from high-resolution images. The server recognizes information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel.

[0279] Map data generation

[0280] Map data is automatically generated based on the extracted geographic information. The server generates map data in standard GIS format based on the information stored in the database. For example, roads, buildings, parks, etc. are plotted on the map.

[0281] Recognizing user emotions with an emotion engine

[0282] When a user browses map data, the emotion engine recognizes the user's emotions. Specifically, the device acquires voice data and facial expression data and sends it to the server. The emotion engine on the server analyzes this data and identifies the user's emotions. For example, emotions are recognized from the tone and pronunciation of the user's voice commands.

[0283] Providing map data and responding to emotions

[0284] The server changes the content and display of the map data it provides based on the user's emotions. For example, if the user expresses impatience, the server will provide route guidance as quickly as possible. The server then returns the generated map data to the user's device, where the user can view and download it.

[0285] Specific examples

[0286] For example, if a user needs to get to a destination in Tokyo in a hurry, the process would be as follows:

[0287] 1. The user requests the latest map data for Tokyo via the device's web browser.

[0288] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[0289] 3. Extract geographic information using image recognition AI and store it in a database.

[0290] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[0291] 5. The user speaks to the device, saying "I'm in a hurry."

[0292] 6. The server's emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[0293] 7. The server generates and provides map data for quick route guidance based on the user's emotions.

[0294] 8. The user displays and uses the latest map data returned on their device and heads to their destination.

[0295] In this way, the system of the present invention recognizes the user's emotions and provides map data in response to them, thereby realizing prompt and appropriate support that meets the user's needs.

[0296] The processing flow will be explained below.

[0297] Step 1:

[0298] The server obtains satellite imagery for a specified area. The server sends a request to the API of an external satellite data provider, specifying the required dataset for the required area and date and time. For example, to download satellite imagery for the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[0299] Step 2:

[0300] The server saves the downloaded satellite images in the internal storage. The acquired satellite images remain in low resolution, but are passed on to the next process for higher resolution.

[0301] Step 3:

[0302] The server inputs acquired satellite images into the generative AI model. The server converts the image data into an input format for the generative AI and performs appropriate preprocessing (e.g., shaping pixel information).

[0303] Step 4:

[0304] The server runs a generative AI to convert low-resolution images to high-resolution ones. The generative AI improves the image resolution and adds details. For example, it converts a 300dpi image to 600dpi, making the details of buildings and roads clearer.

[0305] Step 5:

[0306] The server saves the high-resolution image to its internal storage, ready to be passed on to the next processing step.

[0307] Step 6:

[0308] The server inputs the high-resolution image into the image recognition AI, which then converts the image data to fit the input format of the image recognition AI.

[0309] Step 7:

[0310] The server runs image recognition AI to identify and extract geographic information such as terrain, buildings, and roads. The image recognition AI uses pattern recognition technology to identify various geographic elements pixel by pixel and extract information, such as the width of roads, the height of buildings, and the flow of rivers.

[0311] Step 8:

[0312] The server saves the extracted geographic information in a database. The extracted information is stored in the database as structured data (e.g., GeoJSON format).

[0313] Step 9:

[0314] The server generates map data based on the geographic information stored in the database. The map data generation engine uses the stored information to visualize each geographic element and generate map data. For example, it creates a map by integrating elements such as roads, buildings, and parks.

[0315] Step 10:

[0316] The server exports the generated map data to a GIS format (e.g. Shapefile), saves the exported map data in a file format, and prepares it for distribution to users.

[0317] Step 11:

[0318] The user sends a request to the server using a device (e.g., PC or smartphone). The user accesses the API endpoint for map data using the device's web browser and requests the latest map data for the relevant area (e.g., "latest map of Tokyo").

[0319] Step 12:

[0320] The device acquires the user's emotional data. The device records the user's voice and captures facial expression data with a camera. For example, the user may say, "I'm in a hurry."

[0321] Step 13:

[0322] The device sends emotional data to the server, which then transfers recorded voice data and captured facial expression data to the server.

[0323] Step 14:

[0324] The server's emotion engine analyzes the emotion data and recognizes the user's emotion. The emotion engine analyzes voice tone and facial expression changes to identify the user's emotion. For example, it can recognize impatience from voice data.

[0325] Step 15:

[0326] The server adjusts the content and display of map data provided according to the user's emotions. Based on the recognized emotions (e.g., impatience), map data is generated that quickly highlights the shortest route to the destination.

[0327] Step 16:

[0328] The server returns the generated map data to the user's device, and the map data is provided to the user in an appropriate format (e.g., GIS format, image format).

[0329] Step 17:

[0330] The user can view and use the latest map data returned on their device. The user can check the map data and download it as needed or integrate it into their own application. For example, if the user is in a hurry, they can quickly check the shortest route to their destination.

[0331] Through these processing steps, the system of the present invention can provide the latest, high-quality map data that takes into account the user's feelings and quickly respond to the user's needs.

[0332] Example 2

[0333] 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."

[0334] Conventional map generation systems lack the functionality to provide optimal map data according to the user's emotions and circumstances, which can cause inconvenience to users. Furthermore, the low accuracy of the automatic generation and high-resolution map data can result in problems in which users' needs are not fully met. There is a demand for a system that can solve these issues and provide users with appropriate and prompt map data.

[0335] 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.

[0336] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, emotion recognition means for identifying a user's emotion, means for adjusting the map data based on the identified emotion, and communication means for providing the generated map data, thereby making it possible to provide customized map data that corresponds to the user's emotion and situation.

[0337] "Means for obtaining satellite imagery" means means for obtaining satellite imagery for a specified area and date and time using the API of a satellite data provider.

[0338] "Generative artificial intelligence means" refers to artificial intelligence technology for converting low-resolution satellite images into high-resolution images.

[0339] "Image recognition artificial intelligence means" is an artificial intelligence technology for extracting geographic information from high-resolution images.

[0340] The "means for automatically generating map data" is a means for generating map data in a standard geographic information system format based on the extracted geographic information.

[0341] "Emotion recognition means" is a technology for identifying emotions by analyzing the user's voice and facial expression data.

[0342] The "means for adjusting map data" is a means for changing the content or display of map data based on the identified emotion of the user.

[0343] "Communication means" refers to a communication technology for providing the generated map data to the user's terminal.

[0344] In this invention, a geographic information system is constructed using a system in which servers, terminals, and users each have specific roles. To implement this system, it is necessary to utilize multiple artificial intelligence technologies and communication methods. Below, we will explain how to specifically implement this invention.

[0345] Server Features

[0346] The server has the following main functions:

[0347] 1. Acquisition of satellite images: The server uses the API of a satellite data provider to acquire satellite images for the area and date and time specified by the user. Specifically, APIs such as DigitalGlobe and GeoEye can be used.

[0348] 2. High-resolution imagery: The server inputs the acquired low-resolution satellite images into a generative artificial intelligence (generative AI) model to increase the resolution. This generative AI uses technologies such as GANs (Generative Adversarial Networks).

[0349] 3. Extraction of geographic information: The server extracts geographic information from the high-resolution satellite images using image recognition AI (e.g., object detection models using TensorFlow or PyTorch). The geographic information is then stored in a database.

[0350] 4. Map data generation: The server automatically generates map data based on the extracted geographic information. This generation process uses GIS software such as QGIS or ArcGIS.

[0351] 5. Emotion Recognition: The server has an emotion recognition means (e.g., IBM Watson Tone Analyzer or Microsoft Azure Emotion API) to analyze the user's voice and facial expression data to identify emotions.

[0352] 6. Adjusting map data: The server adjusts the display and content of the map data based on the identified user's emotions.

[0353] 7. Communication: The server has a communication means to provide the generated map data to the user's terminal, and sends and receives data as appropriate.

[0354] Device Features

[0355] 1. User Interface: The device provides an interface for users to request and view map data. This is done through a web browser or dedicated application.

[0356] 2. Emotion input means: The device has a means to acquire the user's emotion data through voice recognition and facial expression recognition and send it to the server. This includes hardware such as a microphone and camera.

[0357] 3. Displaying and downloading map data: The terminal has the means to display map data provided by the server and to download it as necessary.

[0358] User Roles

[0359] 1. Request: The user requests map data for a specific region and date and time through the device interface.

[0360] 2. Emotional data input: The user inputs emotional data using voice and facial expressions and sends it to the server via the terminal.

[0361] 3. Use of map data: Users can view the provided map data and download and use it as needed.

[0362] Specific operation example

[0363] For example, if a user needs to get to a destination in Tokyo in a hurry, the process would be as follows:

[0364] 1. The user requests the latest map data for Tokyo via the device's web browser.

[0365] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[0366] 3. Extract geographic information using image recognition AI and store it in a database.

[0367] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[0368] 5. The user speaks to the device, saying "I'm in a hurry."

[0369] 6. The server's emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[0370] 7. The server generates and provides map data for quick route guidance based on the user's emotions.

[0371] 8. The user displays and uses the latest map data returned on their device and heads to their destination.

[0372] Prompt Sentence Examples

[0373] Below are some example prompts to be input to the generative AI model:

[0374] Prompt 1:

[0375] "Please increase the resolution of this satellite image. Convert it from 300 dpi to 600 dpi and add more detailed information."

[0376] Prompt Statement 2:

[0377] "Extract geographic information from this high-resolution imagery. Identify roads, buildings, and terrain information pixel by pixel and store it in a database."

[0378] Prompt statement 3:

[0379] "Analyze user emotions. Recognize user emotions from voice data and identify impatience and anxiety."

[0380] This system utilizes a variety of artificial intelligence technologies and communication methods to provide fast, high-quality map data that meets user needs.

[0381] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0382] Step 1:

[0383] 1. The server receives a request from the user for the specified location and date and time.

[0384] Input: Location and date / time request data from the user (e.g., Tokyo on October 1, 2023).

[0385] Behavior: The server analyzes the request data and prepares it to use the API of an external satellite data provider.

[0386] Output: API request data.

[0387] Step 2:

[0388] 1. The server sends a request to an API of an external satellite data provider to obtain satellite imagery for the specified area and date and time.

[0389] Input: API request data.

[0390] Operation: The server uses the acquired API request data to call the API of the satellite data provider and download the specified satellite imagery, for example, using APIs from DigitalGlobe or GeoEye.

[0391] Output: Low-resolution satellite image data.

[0392] Step 3:

[0393] 1. The low-resolution satellite images acquired by the server are input into a generative AI model to increase their resolution.

[0394] Input: Low-resolution satellite image data.

[0395] How it works: The server uses a generative AI model (e.g., GANs) to convert low-resolution satellite imagery to high-resolution. The server sends a prompt to the generative AI model, such as "Please resize this image from 300 dpi to 600 dpi," and receives the high-resolution image.

[0396] Output: High resolution satellite image data.

[0397] Step 4:

[0398] 1. The server inputs high-resolution satellite images into image recognition AI to extract geographic information.

[0399] Input: High-resolution satellite image data.

[0400] How it works: The server uses an image recognition AI model (e.g., an object detection model using TensorFlow or PyTorch) to extract geographic information (e.g., roads, buildings, terrain, etc.) from satellite images. The server then sends a prompt to the image recognition AI saying, "Please extract geographic information from this high-resolution image," and stores the extracted geographic information in a database.

[0401] Output: Geographical information data.

[0402] Step 5:

[0403] 1. The server automatically generates map data based on the extracted geographic information.

[0404] Input: Geographical data.

[0405] How it works: The server uses GIS software (e.g., QGIS or ArcGIS) to generate map data in a standard geographic information system format from the extracted geographic information, specifically plotting roads, buildings, parks, and other geographic information on a map.

[0406] Output: Auto-generated map data.

[0407] Step 6:

[0408] 1. The device collects the user's voice and facial expression data.

[0409] Input: User's voice and facial expression data.

[0410] How it works: The device uses a microphone and camera to capture the user's voice and facial expressions and collect them as data.

[0411] Output: Audio data and facial expression data.

[0412] Step 7:

[0413] 1. The device sends the collected voice and facial expression data to the server.

[0414] Input: speech and facial expression data.

[0415] Operation: The terminal performs communication processing to send the collected data to the server.

[0416] Output: Voice data and facial expression data are sent to the server.

[0417] Step 8:

[0418] 1. The server analyzes the user's emotions using emotion recognition means.

[0419] Input: speech and facial expression data.

[0420] How it works: The server uses an emotion recognition tool (e.g., IBM Watson Tone Analyzer or Microsoft Azure Emotion API) to analyze the user's emotions from the transmitted voice and facial expression data. It then sends a prompt to the emotion recognition tool saying, "Please analyze the user's emotions from this voice data."

[0421] Output: User emotion data.

[0422] Step 9:

[0423] 1. The server adjusts the content and display of map data based on the user's emotional data.

[0424] Input: Auto-generated map data and user emotion data.

[0425] How it works: The server uses the identified emotion data to adjust the display and content of map data, for example highlighting the shortest route to provide quick route guidance to users in a hurry.

[0426] Output: Adjusted map data.

[0427] Step 10:

[0428] 1. The server sends the adjusted map data to the user's device.

[0429] Input: Adjusted map data.

[0430] Operation: The server transmits the generated and adjusted map data to the user's terminal using a communication means.

[0431] Output: Map data sent back to the user's device.

[0432] Step 11:

[0433] 1. The user can view and download the provided map data.

[0434] Input: Map data sent to the user's device.

[0435] How it works: The user can view the returned map data via their device and download it as needed.

[0436] Output: Map data available to the user.

[0437] This series of processing steps makes it possible to provide high-resolution map data that is optimized according to the user's emotions and situation.

[0438] (Application example 2)

[0439] 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."

[0440] Conventional map generation systems simply provide map data without customizing it according to the user's emotions or situation. As a result, the same map data is provided whether the user is in a hurry or has other specific emotions, resulting in an unoptimized user experience. In particular, services such as food delivery require fast and efficient route guidance that is in line with the user's emotions, so a system that solves this problem is needed.

[0441] 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.

[0442] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, communication means for providing the generated map data, an emotion engine for recognizing the user's emotions, and means for customizing the map data based on the recognized emotions, thereby enabling fast and efficient route guidance according to the user's emotions.

[0443] A "means for acquiring satellite imagery" is a device or system used to acquire satellite imagery of a particular area.

[0444] "Generative artificial intelligence means for converting acquired satellite imagery to high resolution" means a generative AI model or associated software used to convert acquired low-resolution satellite imagery to high resolution.

[0445] "Image recognition artificial intelligence means for extracting geographic information from high-resolution images" refers to an AI algorithm for recognizing and extracting geographic information such as roads and buildings from high-resolution images.

[0446] The "means for automatically generating map data based on extracted geographic information" refers to a system or tool that automatically generates map data using extracted geographic information.

[0447] The "communication means for providing the generated map data" refers to the network and communication infrastructure for providing the generated map data to the user.

[0448] An "emotion engine that recognizes user emotions" is software or hardware that analyzes voice data and facial expression data to identify the user's emotions.

[0449] A "means for customizing map data based on recognized emotions" is a system or algorithm that changes the content or display of map data in response to recognized user emotions.

[0450] The present invention is a system that combines a system that acquires satellite images, enhances their resolution, extracts geographic information, and automatically generates map data with an emotion engine that recognizes user emotions. Specific embodiments of the present invention are described below.

[0451] System configuration

[0452] This system consists of the following main components:

[0453] 1. Server

[0454] Means of acquiring satellite images

[0455] AI method for generating high-resolution satellite images

[0456] Image recognition AI method to extract geographic information from high-resolution images

[0457] A means of automatically generating map data based on extracted geographic information

[0458] Communication means for providing generated map data

[0459] Emotion engine that recognizes user emotions

[0460] A means to customize map data based on recognized emotions

[0461] 2. Terminal

[0462] User Interface

[0463] Emotion input means (voice recognition, facial expression recognition)

[0464] Viewing map data

[0465] Download map data

[0466] Delivery tracking feature

[0467] 3. Users

[0468] Map data request

[0469] Emotion data input (voice and facial expressions)

[0470] Use of map data

[0471] Program Processing and Data Flow

[0472] Acquisition of satellite images and their resolution enhancement

[0473] The server retrieves satellite images of a specific area using the API of an external satellite data provider. Since the retrieved images are often low-resolution, the server uses a generative AI model (for example, a model based on TensorFlow) to increase the resolution.

[0474] Extracting geographic information

[0475] From the generated high-resolution images, geographic information (e.g., roads and buildings) is extracted using image recognition AI tools. The high-resolution images serve as input data for AI algorithms to precisely extract detailed geographic information.

[0476] Map data generation

[0477] Based on the extracted geographic information, map data is automatically generated, which the server exports in a standard geographic information system (GIS) format and stores in a database.

[0478] The role of the emotional engine

[0479] When a user requests map data, the device sends emotional data to the server through voice and facial expressions. The server's emotion engine analyzes this data and recognizes, for example, the emotion of being in a hurry. Based on the recognition results, the server optimizes and provides the map data.

[0480] Specific examples

[0481] For example, in a food delivery scenario, if the user is in a hurry, the following process would occur:

[0482] 1. A user requests the latest route data for a specific area via a smartphone application.

[0483] 2. The server obtains the latest satellite images of the area from a satellite data provider and uses generative AI to enhance the resolution.

[0484] 3. Geographic information is extracted from the high-resolution images and map data is generated.

[0485] 4. The user utters "I'm in a hurry" via voice input.

[0486] 5. The emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[0487] 6. The server generates and provides map data for quick delivery route guidance based on the user's emotions.

[0488] 7. The user checks the map data provided on their smartphone and the meal is delivered via the optimal route.

[0489] Examples of prompt statements

[0490] "I'm in a hurry, I want my food to arrive quickly!"

[0491] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0492] Step 1:

[0493] A user requests the latest route data for a specific area via a terminal. The request includes area information and date and time information. The server receives this request and obtains satellite images of the requested area using the API of an external satellite data provider. The input is the requested area and date and time information, and the output is the obtained low-resolution satellite image.

[0494] Step 2:

[0495] The server uses a generative AI model (such as a model based on TensorFlow) to convert the acquired low-resolution satellite image into a high-resolution image. The generative AI model inputs the low-resolution image and outputs a high-resolution image. As part of data processing, the generative AI performs image completion, and the output is a high-resolution image.

[0496] Step 3:

[0497] The server uses image recognition AI tools to extract geographic information from high-resolution images. Image recognition AI takes high-resolution images as input and outputs geographic information (e.g., coordinates of roads and buildings). Feature extraction algorithms are used to process the data in this step.

[0498] Step 4:

[0499] The server automatically generates map data based on the extracted geographic information. The extracted geographic information is used as input and map data in GIS format is output. Geographic Information System (GIS) tools are used to calculate this data.

[0500] Step 5:

[0501] At the same time, the device collects the user's emotional data (voice and facial expressions) and sends it to the server. The emotional data is input through the device's built-in voice and facial expression sensors. The output is the user's voice and facial expression data.

[0502] Step 6:

[0503] The server's emotion engine analyzes the user's emotion data and recognizes specific emotions (e.g., impatience). The input is the user's emotion data, and the output is the analyzed emotion result. A machine learning model is used to calculate this data.

[0504] Step 7:

[0505] The server customizes the map data based on the recognized emotion. The map data with optimal route guidance according to the specific emotion is generated. The output is customized map data. A customization algorithm is used for data processing.

[0506] Step 8:

[0507] The generated customized map data is sent from the server to the terminal, which then provides the user with quick route guidance. The input is the customized map data, and the output is the map information displayed to the user.

[0508] Step 9:

[0509] The user checks the map data provided on the terminal and receives delivery according to the instructed route. The input is customized map data, and the output is behavior based on the optimal route.

[0510] Specific prompt examples

[0511] "I'm in a hurry, I want my food to arrive quickly!"

[0512] 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.

[0513] 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.

[0514] 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.

[0515] [Second embodiment]

[0516] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0517] 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.

[0518] 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).

[0519] 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.

[0520] 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.

[0521] 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).

[0522] 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.

[0523] 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.

[0524] 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.

[0525] 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.

[0526] 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.

[0527] 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."

[0528] The present invention provides a system for automatically generating map data after acquiring satellite images and increasing their resolution and extracting geographic information. Specific embodiments for carrying out the present invention will now be described.

[0529] System configuration

[0530] This system consists of the following main components:

[0531] 1. Server

[0532] Acquiring satellite images

[0533] High-resolution satellite images

[0534] Extracting geographic information

[0535] Map data generation

[0536] Saving map data

[0537] Processing user requests

[0538] Map data provided

[0539] 2. Terminal

[0540] User Interface

[0541] Viewing map data

[0542] Download map data

[0543] 3. Users

[0544] Map data request

[0545] Use of map data

[0546] Program processing

[0547] Acquiring satellite images

[0548] The server retrieves satellite imagery for a specified area. To do this, the server uses the API of an external satellite data provider to request the required area and date and time. For example, the server may download satellite imagery for an entire city taken on a specific date.

[0549] High-resolution satellite images

[0550] The acquired satellite images are then converted to high resolution using generation AI. The server inputs low-resolution image data into the generation AI to generate a detailed image. For example, an image taken at 300 dpi can be converted to 600 dpi, and detailed information can be added.

[0551] Extracting geographic information

[0552] Image recognition AI is used to extract geographic information from high-resolution images. The server recognizes information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel.

[0553] Map data generation

[0554] Map data is automatically generated based on the extracted geographic information. The server generates map data in standard GIS format based on the information stored in the database. For example, roads, buildings, parks, etc. are plotted on the map.

[0555] Processing user requests and providing map data

[0556] The user sends a request for map data to the server via their device. The server responds to the user's request by searching for and providing the latest map data for the relevant area. The user can then view the map data on their device and download it as needed.

[0557] Specific examples

[0558] For example, if a user wants to obtain the latest map data for Tokyo, the process would be as follows:

[0559] 1. The user requests the latest map data for Tokyo via the device's web browser.

[0560] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[0561] 3. Extract geographic information using image recognition AI and store it in a database.

[0562] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[0563] 5. The server provides the generated map data to the user's device, where the user can view and download it.

[0564] This invention significantly reduces the cost and time required for map creation and provides up-to-date map information, making it a useful tool in fields that require accurate map data, such as autonomous driving and logistics.

[0565] The processing flow will be explained below.

[0566] Step 1:

[0567] The server obtains satellite imagery for a specified area. The server sends a request to an external satellite data provider's API, specifying the required region and date and time dataset. For example, the server downloads satellite imagery for the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[0568] Step 2:

[0569] The server saves the downloaded satellite images in the internal storage. The acquired satellite images remain in low resolution, but are passed on to the next process for higher resolution.

[0570] Step 3:

[0571] The server inputs acquired satellite images into the generative AI model. The server converts the image data into an input format for the generative AI (for example, converting it into a pixel array).

[0572] Step 4:

[0573] The server runs a generation AI to convert low-resolution images to high-resolution ones. The generation AI increases the image resolution and adds detailed information. For example, converting a 300 dpi image to 600 dpi allows the details of buildings and roads to be distinguished.

[0574] Step 5:

[0575] The server saves the high-resolution image to its internal storage, ready to be passed on to the next processing step.

[0576] Step 6:

[0577] The server inputs the high-resolution image into the image recognition AI. The server then converts the image data into an input format for the image recognition AI.

[0578] Step 7:

[0579] The server runs image recognition AI to identify and extract geographic information such as terrain, buildings, and roads. The image recognition AI uses pattern recognition technology to extract various geographic elements pixel by pixel. For example, it identifies the width of roads, the height of buildings, and the flow of rivers.

[0580] Step 8:

[0581] The server saves the extracted geographic information in a database. The extracted information is stored in the database as structured data (e.g., GeoJSON format).

[0582] Step 9:

[0583] The server generates map data based on the geographic information stored in the database. The map data generation engine uses the stored information to visualize each geographic element and generate map data. For example, roads, buildings, parks, etc. are plotted on the map.

[0584] Step 10:

[0585] The server exports the generated map data to a GIS format (e.g. Shapefile), saves the exported map data in a file format, and prepares it for distribution to users.

[0586] Step 11:

[0587] A user sends a request to the server using a device (such as a PC or smartphone). The user accesses the map data API endpoint using a web browser and requests the latest map data for the relevant area (e.g., "latest map of Tokyo").

[0588] Step 12:

[0589] The server receives the user's request and searches for map data for the corresponding area. The server retrieves the latest map data for the specified area from the database.

[0590] Step 13:

[0591] The server returns the acquired map data to the user's device, and the map data is provided to the user in an appropriate format (e.g., GIS format, image format).

[0592] Step 14:

[0593] The user can view and use the latest map data returned on their device. The user can check the map data and download it or integrate it into their own application if necessary.

[0594] Through these processing steps, the system of the present invention can efficiently provide users with the most up-to-date, high-quality map data.

[0595] Example 1

[0596] 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."

[0597] Conventional map data generation systems have had problems with the time and effort required for processes such as acquiring satellite images, increasing the resolution of the images, extracting geographic information, and generating map data. Furthermore, performing these tasks with high accuracy requires advanced specialized knowledge and technology, resulting in increased costs and reduced efficiency. Providing the latest map information quickly and accurately has proven particularly challenging, making highly reliable map data essential in fields such as autonomous driving and logistics.

[0598] 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.

[0599] In this invention, the server includes means for acquiring satellite images of a specified area, means for increasing the resolution of the acquired satellite images using generation AI, means for extracting geographic information from the increased resolution satellite images using image recognition AI, means for automatically generating map data in a geographic information system format based on the extracted geographic information, and communication means for providing the generated map data. This makes it possible to generate and provide the latest map data more quickly and accurately than conventional systems.

[0600] "Means for acquiring satellite images of a designated area" refers to devices or software that acquire satellite images of a designated area through the API of an external satellite data provider, etc.

[0601] "Means for increasing resolution using generative AI" refers to devices or software that increase the resolution of low-resolution satellite images using a generative AI model (e.g., an image generation AI model).

[0602] "Means for extracting geographic information using image recognition artificial intelligence" refers to devices or software that use image recognition technology to identify geographic elements such as roads, buildings, and parks from high-resolution satellite images and extract them as data.

[0603] "Means for automatically generating map data in geographic information system format" refers to devices or software that generate map data in standard GIS (geographic information system) format based on extracted geographic information.

[0604] The "communication means for providing the generated map data" refers to a network communication device or software for providing the generated map data to the user's terminal.

[0605] "Remote sensing technology" is a technology for collecting information on the ground from remote locations such as satellites and aircraft.

[0606] A "user interface" is an interface through which a user interacts with a system, including a web browser or application form.

[0607] MODE FOR CARRYING OUT THE INVENTION

[0608] The present invention provides a system for automatically generating map data after acquiring satellite images and increasing their resolution and extracting geographic information. Specific embodiments for carrying out the present invention will now be described.

[0609] System configuration

[0610] This system consists of the following main components:

[0611] 1. Server:

[0612] Acquiring satellite images

[0613] High-resolution satellite images

[0614] Extracting geographic information

[0615] Map data generation

[0616] Saving map data

[0617] Processing user requests

[0618] Map data provided

[0619] 2. Terminal:

[0620] User Interface

[0621] Viewing map data

[0622] Download map data

[0623] 3. User:

[0624] Map data request

[0625] Use of map data

[0626] Acquiring satellite images

[0627] The server obtains satellite images of a specific area. In this process, the server uses the API of an external satellite data provider (for example, an external resource provision API) to make a request specifying the desired area and date and time. By using remote sensing technology, the server can obtain the latest and most detailed satellite images. For example, the server downloads satellite images of an entire city taken on a specific date.

[0628] High-resolution satellite images

[0629] The acquired low-resolution satellite images are then converted to high resolution using artificial intelligence. The server inputs a prompt to the image generation AI model (e.g., Stable Diffusion or DALL-E) to convert the low-resolution image to high resolution, and the process is carried out. As a concrete example, an image taken at 300 dpi is converted to 600 dpi, and detailed information is added. An example of a prompt is as follows:

[0630] "Please increase the resolution of a 300 dpi satellite image of Tokyo to 600 dpi."

[0631] Extracting geographic information

[0632] Image recognition AI is used to extract geographic information from high-resolution satellite images. The server uses image recognition AI (for example, YOLO or Mask R-CNN) to identify information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel. This makes it possible to obtain detailed geographic information.

[0633] Map data generation

[0634] The server automatically generates map data based on the extracted geographic information. This process outputs the data in a standard Geographic Information System (GIS) format. The server uses GIS software (e.g., QGIS or ArcGIS) to draw a map based on the information stored in the database and export it in GIS format. For example, it plots roads, buildings, parks, etc. on the map. An example of a prompt is:

[0635] "Generate the latest map data in GIS format based on the extracted geographic information."

[0636] Provision and viewing of map data

[0637] The map data generated by the server is provided in response to user requests. When a user requests map data via a device, the server searches for the latest map data for the relevant area and sends it to the device. The device then displays the received map data so that the user can view it. Furthermore, the user can download map data as needed. In this case, the user interface on the device is implemented using tools such as Google Maps API and Leaflet.

[0638] This invention will significantly reduce the cost and time required for map creation and provide constantly up-to-date map information, making it a useful tool in fields that require accurate map data, such as autonomous driving and logistics.

[0639] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0640] Program processing flow

[0641] Step 1:

[0642] Submitting a User Request

[0643] A user requests the latest map data for a specific area via a terminal.

[0644] Input: The area name and request content specified by the user, for example, "latest map data for Tokyo."

[0645] How it works: A user enters a request into the search field in their device's web browser and clicks the submit button.

[0646] Output: Request data is sent from the terminal to the server.

[0647] Step 2:

[0648] Acquiring satellite images

[0649] The server receives the user's request and retrieves satellite images of the specified area.

[0650] Input: Request data from the user (e.g., "latest satellite image of Tokyo").

[0651] How it works: The server accesses the API of an external satellite data provider and requests satellite imagery for a specified region and date and time.

[0652] Output: The acquired low-resolution satellite image data is stored in the server's internal storage.

[0653] Step 3:

[0654] High-resolution satellite images

[0655] The low-resolution satellite images acquired by the server are converted to high resolution using artificial intelligence.

[0656] Input: Acquired low-resolution satellite image data.

[0657] How it works: The server inputs a prompt to an image generation AI model (e.g., Stable Diffusion or DALL-E) and performs the process of converting low-resolution images to high-resolution ones.

[0658] Example prompt: "Please increase the resolution of a 300 dpi satellite image of Tokyo to 600 dpi."

[0659] Output: The generated high-resolution satellite image data is stored in the server's internal storage.

[0660] Step 4:

[0661] Extracting geographic information

[0662] The server extracts geographic information from high-resolution satellite images using image recognition artificial intelligence.

[0663] Input: High-resolution satellite image data.

[0664] How it works: The server uses image recognition AI (e.g., YOLO or Mask R-CNN) to identify geographical elements such as roads, buildings, and parks in the image and extract them as data.

[0665] Output: The extracted geographic information data is stored in a database on the server.

[0666] Step 5:

[0667] Map data generation

[0668] The server automatically generates map data based on the extracted geographic information.

[0669] Input: Geographical information data stored in a database.

[0670] How it works: The server uses GIS software (e.g. QGIS or ArcGIS) to draw a map from the information in the database and export it in GIS format.

[0671] Example prompt: "Generate up-to-date map data in GIS format based on the extracted geographic information."

[0672] Output: The generated map data is saved in the server's internal storage.

[0673] Step 6:

[0674] Provision and viewing of map data

[0675] The server transmits the generated map data to the terminal in response to a user request.

[0676] Input: User request data and generated map data.

[0677] Operation: The server retrieves the generated map data and sends the corresponding data to the user's device.

[0678] Output: Map data is sent to the device.

[0679] The device displays the received map data so that the user can view it. The device displays the map data using the Google Maps API or Leaflet, and the user can scroll and zoom.

[0680] Output: Users can also download map data as needed.

[0681] (Application example 1)

[0682] 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."

[0683] Navigation for autonomous vehicles requires highly accurate and real-time updates of map data. However, current systems do not update map data frequently or accurately enough, which may result in reduced safety and efficiency during driving. In addition, there is a lack of systems that can quickly and automatically acquire and provide the latest geographic information. To solve this issue, it is necessary to provide autonomous vehicles with the latest high-resolution map data to improve navigation accuracy and safety.

[0684] 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.

[0685] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, and means for navigating an autonomous vehicle using the provided map data, thereby making it possible to provide the latest high-resolution map data to autonomous vehicles in real time.

[0686] "Satellite imagery" refers to images taken from satellites to capture the Earth's surface and atmosphere.

[0687] "High resolution" is the process of increasing the detail of an image and converting it into a clearer image.

[0688] "Generative artificial intelligence" is a technology that uses AI technology to generate data and improve data resolution.

[0689] "Image recognition artificial intelligence" is a technology that uses AI technology to extract and recognize specific information from images.

[0690] "Geographic information" refers to information about a specific area on the earth's surface, such as information about topography, buildings, roads, etc.

[0691] "Map data" refers to data necessary to aggregate geographic information and display it as a map.

[0692] "Auto-generation" is the process by which a system automatically generates data or information.

[0693] "Communication means" refers to the technologies and tools used to send and receive data and information.

[0694] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without a human driver.

[0695] "Navigation" is a function that provides the optimal route to reach a destination.

[0696] The present invention is a system for providing highly accurate map data for autonomous vehicles. The system acquires satellite images, enhances their resolution, extracts geographic information, and then generates and provides map data suitable for the navigation system of the autonomous vehicle. Specific embodiments for implementing the invention are described below.

[0697] System configuration

[0698] server

[0699] The server has the following main functions:

[0700] 1. Obtaining satellite imagery - The server uses the API of an external satellite data provider to obtain satellite imagery for the specified area and date and time. For example, an API request is sent to obtain the latest satellite imagery of Tokyo.

[0701] 2. High-resolution imagery - The acquired satellite images are then made high-resolution using AI. For example, a 300 dpi image is converted to 600 dpi to enhance the details.

[0702] 3. Extraction of geographic information - Geographic information is extracted from the high-resolution images using image recognition AI. The server stores this geographic information in a database.

[0703] 4. Map Data Generation - Automatically generate map data based on the extracted geographic information. Export and save the map data in standard GIS formats.

[0704] 5. Communication - Sending the provided map data to the autonomous vehicle's navigation system.

[0705] Terminal

[0706] The user's device will be the interface of the autonomous vehicle equipped with the navigation system. Specifically, it will have the following functions:

[0707] 1. User Interface - Provides real-time map data to users on the autonomous vehicle's navigation system.

[0708] 2. View map data - Display the provided map data in real time, set destinations and get route guidance.

[0709] 3. Navigation - Using the latest map data to provide optimal routes and support autonomous driving.

[0710] User

[0711] The user is a person riding in an autonomous vehicle. The user performs the following actions:

[0712] 1. Map Data Request - Request the latest map data through the navigation system.

[0713] 2. Use of map data - Use the navigation function of the autonomous vehicle to head to your destination.

[0714] Specific examples

[0715] For example, if a user uses an autonomous vehicle that uses the latest map data for Tokyo, the process would be as follows:

[0716] 1. The user requests the latest map data for Tokyo via the navigation system.

[0717] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[0718] 3. Extract geographic information using image recognition AI and store it in a database.

[0719] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[0720] 5. The server provides the generated map data to the navigation system of the autonomous vehicle, and the user uses it to head to their destination.

[0721] Prompt Sentence Examples

[0722] The following is an example of a prompt to be input to a generative AI model used to enhance satellite imagery and extract geographic information:

[0723] Obtain high-resolution satellite imagery for a specified area and date / time. Extract detailed geographic information from the imagery and generate map data based on it. Example: Tokyo, 2023-10-10, map data including roads, buildings, and terrain information.

[0724] The present invention makes it possible to provide automated driving vehicles with real-time, highly accurate map data, thereby realizing safe and efficient driving.

[0725] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0726] Step 1:

[0727] The server receives a request for map data from the user, including the requested region and date and time, and uses this information to send a request to the API of an external satellite data provider.

[0728] Step 2:

[0729] The server obtains satellite images for the specified area and date and time from the satellite data provider. The input is the satellite image obtained from the API response. The server stores this image in memory.

[0730] Step 3:

[0731] The server inputs the acquired satellite image into a generative AI model and performs high-resolution image generation. Specifically, low-resolution satellite images are input into the generative AI, and a high-resolution, detailed image is output. This process improves the image resolution.

[0732] Step 4:

[0733] The server inputs high-resolution satellite images into an image recognition AI (artificial intelligence) to extract geographic information, which analyzes information such as roads, buildings, and terrain pixel by pixel and outputs structured data for storage in a database.

[0734] Step 5:

[0735] The server automatically generates map data based on the extracted geographic information. Specifically, it organizes the geographic information in the database in GIS format and generates map data. This result is then output in a standard map file format (e.g., GeoJSON).

[0736] Step 6:

[0737] The server transmits the generated map data to the navigation system of the autonomous vehicle. The input is the generated map data, and the output is a communication packet containing the data. In this step, the data is transmitted using a communication means.

[0738] Step 7:

[0739] The terminal displays the received map data in real time on the navigation system of the autonomous vehicle. The input is map data received from the communication means, and the output is the latest map information displayed on the display inside the vehicle. The user uses this map data to set destinations and get route guidance.

[0740] Step 8:

[0741] The autonomous vehicle navigates using the map data provided by the device. Specifically, it references the latest high-resolution map data to support safe and efficient driving. The input is the latest map data, and the output is the autonomous vehicle's driving route.

[0742] This series of processes enables the provision of real-time, highly accurate map data, improving navigation for autonomous vehicles.

[0743] 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.

[0744] The present invention combines a system that acquires satellite images, enhances their resolution, extracts geographic information, and then automatically generates map data, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0745] System configuration

[0746] This system consists of the following main components:

[0747] 1. Server

[0748] Acquiring satellite images

[0749] High-resolution satellite images

[0750] Extracting geographic information

[0751] Map data generation

[0752] Saving map data

[0753] Processing user requests

[0754] Recognizing user emotions with an emotion engine

[0755] Map data provided

[0756] 2. Terminal

[0757] User Interface

[0758] Emotion input means (e.g., voice recognition, facial expression recognition)

[0759] Viewing map data

[0760] Download map data

[0761] 3. Users

[0762] Map data request

[0763] Emotion data input (voice and facial expressions)

[0764] Use of map data

[0765] Program processing

[0766] Acquiring satellite images

[0767] The server obtains satellite images of a specified area. To do this, the server uses the API of an external satellite data provider to request the required area and date and time. For example, the server downloads satellite images of the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[0768] High-resolution satellite images

[0769] The acquired satellite images are then converted to high resolution using generation AI. The server inputs the low-resolution image data into the generation AI to generate a detailed image. For example, a 300 dpi image can be converted to 600 dpi, and detailed information can be added.

[0770] Extracting geographic information

[0771] Image recognition AI is used to extract geographic information from high-resolution images. The server recognizes information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel.

[0772] Map data generation

[0773] Map data is automatically generated based on the extracted geographic information. The server generates map data in standard GIS format based on the information stored in the database. For example, roads, buildings, parks, etc. are plotted on the map.

[0774] Recognizing user emotions with an emotion engine

[0775] When a user browses map data, the emotion engine recognizes the user's emotions. Specifically, the device acquires voice data and facial expression data and sends it to the server. The emotion engine on the server analyzes this data and identifies the user's emotions. For example, emotions are recognized from the tone and pronunciation of the user's voice commands.

[0776] Providing map data and responding to emotions

[0777] The server changes the content and display of the map data it provides based on the user's emotions. For example, if the user expresses impatience, the server will provide route guidance as quickly as possible. The server then returns the generated map data to the user's device, where the user can view and download it.

[0778] Specific examples

[0779] For example, if a user needs to get to a destination in Tokyo in a hurry, the process would be as follows:

[0780] 1. The user requests the latest map data for Tokyo via the device's web browser.

[0781] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[0782] 3. Extract geographic information using image recognition AI and store it in a database.

[0783] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[0784] 5. The user speaks to the device, saying "I'm in a hurry."

[0785] 6. The server's emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[0786] 7. The server generates and provides map data for quick route guidance based on the user's emotions.

[0787] 8. The user displays and uses the latest map data returned on their device and heads to their destination.

[0788] In this way, the system of the present invention recognizes the user's emotions and provides map data in response to them, thereby realizing prompt and appropriate support that meets the user's needs.

[0789] The processing flow will be explained below.

[0790] Step 1:

[0791] The server obtains satellite imagery for a specified area. The server sends a request to the API of an external satellite data provider, specifying the required dataset for the required area and date and time. For example, to download satellite imagery for the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[0792] Step 2:

[0793] The server saves the downloaded satellite images in the internal storage. The acquired satellite images remain in low resolution, but are passed on to the next process for higher resolution.

[0794] Step 3:

[0795] The server inputs acquired satellite images into the generative AI model. The server converts the image data into an input format for the generative AI and performs appropriate preprocessing (e.g., shaping pixel information).

[0796] Step 4:

[0797] The server runs a generative AI to convert low-resolution images to high-resolution ones. The generative AI improves the image resolution and adds details. For example, it converts a 300dpi image to 600dpi, making the details of buildings and roads clearer.

[0798] Step 5:

[0799] The server saves the high-resolution image to its internal storage, ready to be passed on to the next processing step.

[0800] Step 6:

[0801] The server inputs the high-resolution image into the image recognition AI, which then converts the image data to fit the input format of the image recognition AI.

[0802] Step 7:

[0803] The server runs image recognition AI to identify and extract geographic information such as terrain, buildings, and roads. The image recognition AI uses pattern recognition technology to identify various geographic elements pixel by pixel and extract information, such as the width of roads, the height of buildings, and the flow of rivers.

[0804] Step 8:

[0805] The server saves the extracted geographic information in a database. The extracted information is stored in the database as structured data (e.g., GeoJSON format).

[0806] Step 9:

[0807] The server generates map data based on the geographic information stored in the database. The map data generation engine uses the stored information to visualize each geographic element and generate map data. For example, it creates a map by integrating elements such as roads, buildings, and parks.

[0808] Step 10:

[0809] The server exports the generated map data to a GIS format (e.g. Shapefile), saves the exported map data in a file format, and prepares it for distribution to users.

[0810] Step 11:

[0811] The user sends a request to the server using a device (e.g., PC or smartphone). The user accesses the API endpoint for map data using the device's web browser and requests the latest map data for the relevant area (e.g., "latest map of Tokyo").

[0812] Step 12:

[0813] The device acquires the user's emotional data. The device records the user's voice and captures facial expression data with a camera. For example, the user may say, "I'm in a hurry."

[0814] Step 13:

[0815] The device sends emotional data to the server, which then transfers recorded voice data and captured facial expression data to the server.

[0816] Step 14:

[0817] The server's emotion engine analyzes the emotion data and recognizes the user's emotion. The emotion engine analyzes voice tone and facial expression changes to identify the user's emotion. For example, it can recognize impatience from voice data.

[0818] Step 15:

[0819] The server adjusts the content and display of map data provided according to the user's emotions. Based on the recognized emotions (e.g., impatience), map data is generated that quickly highlights the shortest route to the destination.

[0820] Step 16:

[0821] The server returns the generated map data to the user's device, and the map data is provided to the user in an appropriate format (e.g., GIS format, image format).

[0822] Step 17:

[0823] The user can view and use the latest map data returned on their device. The user can check the map data and download it as needed or integrate it into their own application. For example, if the user is in a hurry, they can quickly check the shortest route to their destination.

[0824] Through these processing steps, the system of the present invention can provide the latest, high-quality map data that takes into account the user's feelings and quickly respond to the user's needs.

[0825] Example 2

[0826] 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."

[0827] Conventional map generation systems lack the functionality to provide optimal map data according to the user's emotions and circumstances, which can cause inconvenience to users. Furthermore, the low accuracy of the automatic generation and high-resolution map data can result in problems in which users' needs are not fully met. There is a demand for a system that can solve these issues and provide users with appropriate and prompt map data.

[0828] 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.

[0829] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, emotion recognition means for identifying a user's emotion, means for adjusting the map data based on the identified emotion, and communication means for providing the generated map data, thereby making it possible to provide customized map data that corresponds to the user's emotion and situation.

[0830] "Means for obtaining satellite imagery" means means for obtaining satellite imagery for a specified area and date and time using the API of a satellite data provider.

[0831] "Generative artificial intelligence means" refers to artificial intelligence technology for converting low-resolution satellite images into high-resolution images.

[0832] "Image recognition artificial intelligence means" is an artificial intelligence technology for extracting geographic information from high-resolution images.

[0833] The "means for automatically generating map data" is a means for generating map data in a standard geographic information system format based on the extracted geographic information.

[0834] "Emotion recognition means" is a technology for identifying emotions by analyzing the user's voice and facial expression data.

[0835] The "means for adjusting map data" is a means for changing the content or display of map data based on the identified emotion of the user.

[0836] "Communication means" refers to a communication technology for providing the generated map data to the user's terminal.

[0837] In this invention, a geographic information system is constructed using a system in which servers, terminals, and users each have specific roles. To implement this system, it is necessary to utilize multiple artificial intelligence technologies and communication methods. Below, we will explain how to specifically implement this invention.

[0838] Server Features

[0839] The server has the following main functions:

[0840] 1. Acquisition of satellite images: The server uses the API of a satellite data provider to acquire satellite images for the area and date and time specified by the user. Specifically, APIs such as DigitalGlobe and GeoEye can be used.

[0841] 2. High-resolution imagery: The server inputs the acquired low-resolution satellite images into a generative artificial intelligence (generative AI) model to increase the resolution. This generative AI uses technologies such as GANs (Generative Adversarial Networks).

[0842] 3. Extraction of geographic information: The server extracts geographic information from the high-resolution satellite images using image recognition AI (e.g., object detection models using TensorFlow or PyTorch). The geographic information is then stored in a database.

[0843] 4. Map data generation: The server automatically generates map data based on the extracted geographic information. This generation process uses GIS software such as QGIS or ArcGIS.

[0844] 5. Emotion Recognition: The server has an emotion recognition means (e.g., IBM Watson Tone Analyzer or Microsoft Azure Emotion API) to analyze the user's voice and facial expression data to identify emotions.

[0845] 6. Adjusting map data: The server adjusts the display and content of the map data based on the identified user's emotions.

[0846] 7. Communication: The server has a communication means to provide the generated map data to the user's terminal, and sends and receives data as appropriate.

[0847] Device Features

[0848] 1. User Interface: The device provides an interface for users to request and view map data. This is done through a web browser or dedicated application.

[0849] 2. Emotion input means: The device has a means to acquire the user's emotion data through voice recognition and facial expression recognition and send it to the server. This includes hardware such as a microphone and camera.

[0850] 3. Displaying and downloading map data: The terminal has the means to display map data provided by the server and to download it as necessary.

[0851] User Roles

[0852] 1. Request: The user requests map data for a specific region and date and time through the device interface.

[0853] 2. Emotional data input: The user inputs emotional data using voice and facial expressions and sends it to the server via the terminal.

[0854] 3. Use of map data: Users can view the provided map data and download and use it as needed.

[0855] Specific operation example

[0856] For example, if a user needs to get to a destination in Tokyo in a hurry, the process would be as follows:

[0857] 1. The user requests the latest map data for Tokyo via the device's web browser.

[0858] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[0859] 3. Extract geographic information using image recognition AI and store it in a database.

[0860] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[0861] 5. The user speaks to the device, saying "I'm in a hurry."

[0862] 6. The server's emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[0863] 7. The server generates and provides map data for quick route guidance based on the user's emotions.

[0864] 8. The user displays and uses the latest map data returned on their device and heads to their destination.

[0865] Prompt Sentence Examples

[0866] Below are some example prompts to be input to the generative AI model:

[0867] Prompt 1:

[0868] "Please increase the resolution of this satellite image. Convert it from 300 dpi to 600 dpi and add more detailed information."

[0869] Prompt Statement 2:

[0870] "Extract geographic information from this high-resolution imagery. Identify roads, buildings, and terrain information pixel by pixel and store it in a database."

[0871] Prompt statement 3:

[0872] "Analyze user emotions. Recognize user emotions from voice data and identify impatience and anxiety."

[0873] This system utilizes a variety of artificial intelligence technologies and communication methods to provide fast, high-quality map data that meets user needs.

[0874] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0875] Step 1:

[0876] 1. The server receives a request from the user for the specified location and date and time.

[0877] Input: Location and date / time request data from the user (e.g., Tokyo on October 1, 2023).

[0878] Behavior: The server analyzes the request data and prepares it to use the API of an external satellite data provider.

[0879] Output: API request data.

[0880] Step 2:

[0881] 1. The server sends a request to an API of an external satellite data provider to obtain satellite imagery for the specified area and date and time.

[0882] Input: API request data.

[0883] Operation: The server uses the acquired API request data to call the API of the satellite data provider and download the specified satellite imagery, for example, using APIs from DigitalGlobe or GeoEye.

[0884] Output: Low-resolution satellite image data.

[0885] Step 3:

[0886] 1. The low-resolution satellite images acquired by the server are input into a generative AI model to increase their resolution.

[0887] Input: Low-resolution satellite image data.

[0888] How it works: The server uses a generative AI model (e.g., GANs) to convert low-resolution satellite imagery to high-resolution. The server sends a prompt to the generative AI model, such as "Please resize this image from 300 dpi to 600 dpi," and receives the high-resolution image.

[0889] Output: High resolution satellite image data.

[0890] Step 4:

[0891] 1. The server inputs high-resolution satellite images into image recognition AI to extract geographic information.

[0892] Input: High-resolution satellite image data.

[0893] How it works: The server uses an image recognition AI model (e.g., an object detection model using TensorFlow or PyTorch) to extract geographic information (e.g., roads, buildings, terrain, etc.) from satellite images. The server then sends a prompt to the image recognition AI saying, "Please extract geographic information from this high-resolution image," and stores the extracted geographic information in a database.

[0894] Output: Geographical information data.

[0895] Step 5:

[0896] 1. The server automatically generates map data based on the extracted geographic information.

[0897] Input: Geographical data.

[0898] How it works: The server uses GIS software (e.g., QGIS or ArcGIS) to generate map data in a standard geographic information system format from the extracted geographic information, specifically plotting roads, buildings, parks, and other geographic information on a map.

[0899] Output: Auto-generated map data.

[0900] Step 6:

[0901] 1. The device collects the user's voice and facial expression data.

[0902] Input: User's voice and facial expression data.

[0903] How it works: The device uses a microphone and camera to capture the user's voice and facial expressions and collect them as data.

[0904] Output: Audio data and facial expression data.

[0905] Step 7:

[0906] 1. The device sends the collected voice and facial expression data to the server.

[0907] Input: speech and facial expression data.

[0908] Operation: The terminal performs communication processing to send the collected data to the server.

[0909] Output: Voice data and facial expression data are sent to the server.

[0910] Step 8:

[0911] 1. The server analyzes the user's emotions using emotion recognition means.

[0912] Input: speech and facial expression data.

[0913] How it works: The server uses an emotion recognition tool (e.g., IBM Watson Tone Analyzer or Microsoft Azure Emotion API) to analyze the user's emotions from the transmitted voice and facial expression data. It then sends a prompt to the emotion recognition tool saying, "Please analyze the user's emotions from this voice data."

[0914] Output: User emotion data.

[0915] Step 9:

[0916] 1. The server adjusts the content and display of map data based on the user's emotional data.

[0917] Input: Auto-generated map data and user emotion data.

[0918] How it works: The server uses the identified emotion data to adjust the display and content of map data, for example highlighting the shortest route to provide quick route guidance to users in a hurry.

[0919] Output: Adjusted map data.

[0920] Step 10:

[0921] 1. The server sends the adjusted map data to the user's device.

[0922] Input: Adjusted map data.

[0923] Operation: The server transmits the generated and adjusted map data to the user's terminal using a communication means.

[0924] Output: Map data sent back to the user's device.

[0925] Step 11:

[0926] 1. The user can view and download the provided map data.

[0927] Input: Map data sent to the user's device.

[0928] How it works: The user can view the returned map data via their device and download it as needed.

[0929] Output: Map data available to the user.

[0930] This series of processing steps makes it possible to provide high-resolution map data that is optimized according to the user's emotions and situation.

[0931] (Application example 2)

[0932] 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."

[0933] Conventional map generation systems simply provide map data without customizing it according to the user's emotions or situation. As a result, the same map data is provided whether the user is in a hurry or has other specific emotions, resulting in an unoptimized user experience. In particular, services such as food delivery require fast and efficient route guidance that is in line with the user's emotions, so a system that solves this problem is needed.

[0934] 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.

[0935] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, communication means for providing the generated map data, an emotion engine for recognizing the user's emotions, and means for customizing the map data based on the recognized emotions, thereby enabling fast and efficient route guidance according to the user's emotions.

[0936] A "means for acquiring satellite imagery" is a device or system used to acquire satellite imagery of a particular area.

[0937] "Generative artificial intelligence means for converting acquired satellite imagery to high resolution" means a generative AI model or associated software used to convert acquired low-resolution satellite imagery to high resolution.

[0938] "Image recognition artificial intelligence means for extracting geographic information from high-resolution images" refers to an AI algorithm for recognizing and extracting geographic information such as roads and buildings from high-resolution images.

[0939] The "means for automatically generating map data based on extracted geographic information" refers to a system or tool that automatically generates map data using extracted geographic information.

[0940] The "communication means for providing the generated map data" refers to the network and communication infrastructure for providing the generated map data to the user.

[0941] An "emotion engine that recognizes user emotions" is software or hardware that analyzes voice data and facial expression data to identify the user's emotions.

[0942] A "means for customizing map data based on recognized emotions" is a system or algorithm that changes the content or display of map data in response to recognized user emotions.

[0943] The present invention is a system that combines a system that acquires satellite images, enhances their resolution, extracts geographic information, and automatically generates map data with an emotion engine that recognizes user emotions. Specific embodiments of the present invention are described below.

[0944] System configuration

[0945] This system consists of the following main components:

[0946] 1. Server

[0947] Means of acquiring satellite images

[0948] AI method for generating high-resolution satellite images

[0949] Image recognition AI method to extract geographic information from high-resolution images

[0950] A means of automatically generating map data based on extracted geographic information

[0951] Communication means for providing generated map data

[0952] Emotion engine that recognizes user emotions

[0953] A means to customize map data based on recognized emotions

[0954] 2. Terminal

[0955] User Interface

[0956] Emotion input means (voice recognition, facial expression recognition)

[0957] Viewing map data

[0958] Download map data

[0959] Delivery tracking feature

[0960] 3. Users

[0961] Map data request

[0962] Emotion data input (voice and facial expressions)

[0963] Use of map data

[0964] Program Processing and Data Flow

[0965] Acquisition of satellite images and their resolution enhancement

[0966] The server retrieves satellite images of a specific area using the API of an external satellite data provider. Since the retrieved images are often low-resolution, the server uses a generative AI model (for example, a model based on TensorFlow) to increase the resolution.

[0967] Extracting geographic information

[0968] From the generated high-resolution images, geographic information (e.g., roads and buildings) is extracted using image recognition AI tools. The high-resolution images serve as input data for AI algorithms to precisely extract detailed geographic information.

[0969] Map data generation

[0970] Based on the extracted geographic information, map data is automatically generated, which the server exports in a standard geographic information system (GIS) format and stores in a database.

[0971] The role of the emotional engine

[0972] When a user requests map data, the device sends emotional data to the server through voice and facial expressions. The server's emotion engine analyzes this data and recognizes, for example, the emotion of being in a hurry. Based on the recognition results, the server optimizes and provides the map data.

[0973] Specific examples

[0974] For example, in a food delivery scenario, if the user is in a hurry, the following process would occur:

[0975] 1. A user requests the latest route data for a specific area via a smartphone application.

[0976] 2. The server obtains the latest satellite images of the area from a satellite data provider and uses generative AI to enhance the resolution.

[0977] 3. Geographic information is extracted from the high-resolution images and map data is generated.

[0978] 4. The user utters "I'm in a hurry" via voice input.

[0979] 5. The emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[0980] 6. The server generates and provides map data for quick delivery route guidance based on the user's emotions.

[0981] 7. The user checks the map data provided on their smartphone and the meal is delivered via the optimal route.

[0982] Examples of prompt statements

[0983] "I'm in a hurry, I want my food to arrive quickly!"

[0984] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0985] Step 1:

[0986] A user requests the latest route data for a specific area via a terminal. The request includes area information and date and time information. The server receives this request and obtains satellite images of the requested area using the API of an external satellite data provider. The input is the requested area and date and time information, and the output is the obtained low-resolution satellite image.

[0987] Step 2:

[0988] The server uses a generative AI model (such as a model based on TensorFlow) to convert the acquired low-resolution satellite image into a high-resolution image. The generative AI model inputs the low-resolution image and outputs a high-resolution image. As part of data processing, the generative AI performs image completion, and the output is a high-resolution image.

[0989] Step 3:

[0990] The server uses image recognition AI tools to extract geographic information from high-resolution images. Image recognition AI takes high-resolution images as input and outputs geographic information (e.g., coordinates of roads and buildings). Feature extraction algorithms are used to process the data in this step.

[0991] Step 4:

[0992] The server automatically generates map data based on the extracted geographic information. The extracted geographic information is used as input and map data in GIS format is output. Geographic Information System (GIS) tools are used to calculate this data.

[0993] Step 5:

[0994] At the same time, the device collects the user's emotional data (voice and facial expressions) and sends it to the server. The emotional data is input through the device's built-in voice and facial expression sensors. The output is the user's voice and facial expression data.

[0995] Step 6:

[0996] The server's emotion engine analyzes the user's emotion data and recognizes specific emotions (e.g., impatience). The input is the user's emotion data, and the output is the analyzed emotion result. A machine learning model is used to calculate this data.

[0997] Step 7:

[0998] The server customizes the map data based on the recognized emotion. The map data with optimal route guidance according to the specific emotion is generated. The output is customized map data. A customization algorithm is used for data processing.

[0999] Step 8:

[1000] The generated customized map data is sent from the server to the terminal, which then provides the user with quick route guidance. The input is the customized map data, and the output is the map information displayed to the user.

[1001] Step 9:

[1002] The user checks the map data provided on the terminal and receives delivery according to the instructed route. The input is customized map data, and the output is behavior based on the optimal route.

[1003] Specific prompt examples

[1004] "I'm in a hurry, I want my food to arrive quickly!"

[1005] 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.

[1006] 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.

[1007] 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.

[1008] [Third embodiment]

[1009] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1010] 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.

[1011] 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).

[1012] 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.

[1013] 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.

[1014] 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).

[1015] 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.

[1016] 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.

[1017] 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.

[1018] 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.

[1019] 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.

[1020] 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."

[1021] The present invention provides a system for automatically generating map data after acquiring satellite images and increasing their resolution and extracting geographic information. Specific embodiments for carrying out the present invention will now be described.

[1022] System configuration

[1023] This system consists of the following main components:

[1024] 1. Server

[1025] Acquiring satellite images

[1026] High-resolution satellite images

[1027] Extracting geographic information

[1028] Map data generation

[1029] Saving map data

[1030] Processing user requests

[1031] Map data provided

[1032] 2. Terminal

[1033] User Interface

[1034] Viewing map data

[1035] Download map data

[1036] 3. Users

[1037] Map data request

[1038] Use of map data

[1039] Program processing

[1040] Acquiring satellite images

[1041] The server retrieves satellite imagery for a specified area. To do this, the server uses the API of an external satellite data provider to request the required area and date and time. For example, the server may download satellite imagery for an entire city taken on a specific date.

[1042] High-resolution satellite images

[1043] The acquired satellite images are then converted to high resolution using generation AI. The server inputs low-resolution image data into the generation AI to generate a detailed image. For example, an image taken at 300 dpi can be converted to 600 dpi, and detailed information can be added.

[1044] Extracting geographic information

[1045] Image recognition AI is used to extract geographic information from high-resolution images. The server recognizes information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel.

[1046] Map data generation

[1047] Map data is automatically generated based on the extracted geographic information. The server generates map data in standard GIS format based on the information stored in the database. For example, roads, buildings, parks, etc. are plotted on the map.

[1048] Processing user requests and providing map data

[1049] The user sends a request for map data to the server via their device. The server responds to the user's request by searching for and providing the latest map data for the relevant area. The user can then view the map data on their device and download it as needed.

[1050] Specific examples

[1051] For example, if a user wants to obtain the latest map data for Tokyo, the process would be as follows:

[1052] 1. The user requests the latest map data for Tokyo via the device's web browser.

[1053] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[1054] 3. Extract geographic information using image recognition AI and store it in a database.

[1055] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[1056] 5. The server provides the generated map data to the user's device, where the user can view and download it.

[1057] This invention significantly reduces the cost and time required for map creation and provides up-to-date map information, making it a useful tool in fields that require accurate map data, such as autonomous driving and logistics.

[1058] The processing flow will be explained below.

[1059] Step 1:

[1060] The server obtains satellite imagery for a specified area. The server sends a request to an external satellite data provider's API, specifying the required region and date and time dataset. For example, the server downloads satellite imagery for the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[1061] Step 2:

[1062] The server saves the downloaded satellite images in the internal storage. The acquired satellite images remain in low resolution, but are passed on to the next process for higher resolution.

[1063] Step 3:

[1064] The server inputs acquired satellite images into the generative AI model. The server converts the image data into an input format for the generative AI (for example, converting it into a pixel array).

[1065] Step 4:

[1066] The server runs a generation AI to convert low-resolution images to high-resolution ones. The generation AI increases the image resolution and adds detailed information. For example, converting a 300 dpi image to 600 dpi allows the details of buildings and roads to be distinguished.

[1067] Step 5:

[1068] The server saves the high-resolution image to its internal storage, ready to be passed on to the next processing step.

[1069] Step 6:

[1070] The server inputs the high-resolution image into the image recognition AI. The server then converts the image data into an input format for the image recognition AI.

[1071] Step 7:

[1072] The server runs image recognition AI to identify and extract geographic information such as terrain, buildings, and roads. The image recognition AI uses pattern recognition technology to extract various geographic elements pixel by pixel. For example, it identifies the width of roads, the height of buildings, and the flow of rivers.

[1073] Step 8:

[1074] The server saves the extracted geographic information in a database. The extracted information is stored in the database as structured data (e.g., GeoJSON format).

[1075] Step 9:

[1076] The server generates map data based on the geographic information stored in the database. The map data generation engine uses the stored information to visualize each geographic element and generate map data. For example, roads, buildings, parks, etc. are plotted on the map.

[1077] Step 10:

[1078] The server exports the generated map data to a GIS format (e.g. Shapefile), saves the exported map data in a file format, and prepares it for distribution to users.

[1079] Step 11:

[1080] A user sends a request to the server using a device (such as a PC or smartphone). The user accesses the map data API endpoint using a web browser and requests the latest map data for the relevant area (e.g., "latest map of Tokyo").

[1081] Step 12:

[1082] The server receives the user's request and searches for map data for the corresponding area. The server retrieves the latest map data for the specified area from the database.

[1083] Step 13:

[1084] The server returns the acquired map data to the user's device, and the map data is provided to the user in an appropriate format (e.g., GIS format, image format).

[1085] Step 14:

[1086] The user can view and use the latest map data returned on their device. The user can check the map data and download it or integrate it into their own application if necessary.

[1087] Through these processing steps, the system of the present invention can efficiently provide users with the most up-to-date, high-quality map data.

[1088] Example 1

[1089] 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."

[1090] Conventional map data generation systems have had problems with the time and effort required for processes such as acquiring satellite images, increasing the resolution of the images, extracting geographic information, and generating map data. Furthermore, performing these tasks with high accuracy requires advanced specialized knowledge and technology, resulting in increased costs and reduced efficiency. Providing the latest map information quickly and accurately has proven particularly challenging, making highly reliable map data essential in fields such as autonomous driving and logistics.

[1091] 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.

[1092] In this invention, the server includes means for acquiring satellite images of a specified area, means for increasing the resolution of the acquired satellite images using generation AI, means for extracting geographic information from the increased resolution satellite images using image recognition AI, means for automatically generating map data in a geographic information system format based on the extracted geographic information, and communication means for providing the generated map data. This makes it possible to generate and provide the latest map data more quickly and accurately than conventional systems.

[1093] "Means for acquiring satellite images of a designated area" refers to devices or software that acquire satellite images of a designated area through the API of an external satellite data provider, etc.

[1094] "Means for increasing resolution using generative AI" refers to devices or software that increase the resolution of low-resolution satellite images using a generative AI model (e.g., an image generation AI model).

[1095] "Means for extracting geographic information using image recognition artificial intelligence" refers to devices or software that use image recognition technology to identify geographic elements such as roads, buildings, and parks from high-resolution satellite images and extract them as data.

[1096] "Means for automatically generating map data in geographic information system format" refers to devices or software that generate map data in standard GIS (geographic information system) format based on extracted geographic information.

[1097] The "communication means for providing the generated map data" refers to a network communication device or software for providing the generated map data to the user's terminal.

[1098] "Remote sensing technology" is a technology for collecting information on the ground from remote locations such as satellites and aircraft.

[1099] A "user interface" is an interface through which a user interacts with a system, including a web browser or application form.

[1100] MODE FOR CARRYING OUT THE INVENTION

[1101] The present invention provides a system for automatically generating map data after acquiring satellite images and increasing their resolution and extracting geographic information. Specific embodiments for carrying out the present invention will now be described.

[1102] System configuration

[1103] This system consists of the following main components:

[1104] 1. Server:

[1105] Acquiring satellite images

[1106] High-resolution satellite images

[1107] Extracting geographic information

[1108] Map data generation

[1109] Saving map data

[1110] Processing user requests

[1111] Map data provided

[1112] 2. Terminal:

[1113] User Interface

[1114] Viewing map data

[1115] Download map data

[1116] 3. User:

[1117] Map data request

[1118] Use of map data

[1119] Acquiring satellite images

[1120] The server obtains satellite images of a specific area. In this process, the server uses the API of an external satellite data provider (for example, an external resource provision API) to make a request specifying the desired area and date and time. By using remote sensing technology, the server can obtain the latest and most detailed satellite images. For example, the server downloads satellite images of an entire city taken on a specific date.

[1121] High-resolution satellite images

[1122] The acquired low-resolution satellite images are then converted to high resolution using artificial intelligence. The server inputs a prompt to the image generation AI model (e.g., Stable Diffusion or DALL-E) to convert the low-resolution image to high resolution, and the process is carried out. As a concrete example, an image taken at 300 dpi is converted to 600 dpi, and detailed information is added. An example of a prompt is as follows:

[1123] "Please increase the resolution of a 300 dpi satellite image of Tokyo to 600 dpi."

[1124] Extracting geographic information

[1125] Image recognition AI is used to extract geographic information from high-resolution satellite images. The server uses image recognition AI (for example, YOLO or Mask R-CNN) to identify information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel. This makes it possible to obtain detailed geographic information.

[1126] Map data generation

[1127] The server automatically generates map data based on the extracted geographic information. This process outputs the data in a standard Geographic Information System (GIS) format. The server uses GIS software (e.g., QGIS or ArcGIS) to draw a map based on the information stored in the database and export it in GIS format. For example, it plots roads, buildings, parks, etc. on the map. An example of a prompt is:

[1128] "Generate the latest map data in GIS format based on the extracted geographic information."

[1129] Provision and viewing of map data

[1130] The map data generated by the server is provided in response to user requests. When a user requests map data via a device, the server searches for the latest map data for the relevant area and sends it to the device. The device then displays the received map data so that the user can view it. Furthermore, the user can download map data as needed. In this case, the user interface on the device is implemented using tools such as Google Maps API and Leaflet.

[1131] This invention will significantly reduce the cost and time required for map creation and provide constantly up-to-date map information, making it a useful tool in fields that require accurate map data, such as autonomous driving and logistics.

[1132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1133] Program processing flow

[1134] Step 1:

[1135] Submitting a User Request

[1136] A user requests the latest map data for a specific area via a terminal.

[1137] Input: The area name and request content specified by the user, for example, "latest map data for Tokyo."

[1138] How it works: A user enters a request into the search field in their device's web browser and clicks the submit button.

[1139] Output: Request data is sent from the terminal to the server.

[1140] Step 2:

[1141] Acquiring satellite images

[1142] The server receives the user's request and retrieves satellite images of the specified area.

[1143] Input: Request data from the user (e.g., "latest satellite image of Tokyo").

[1144] How it works: The server accesses the API of an external satellite data provider and requests satellite imagery for a specified region and date and time.

[1145] Output: The acquired low-resolution satellite image data is stored in the server's internal storage.

[1146] Step 3:

[1147] High-resolution satellite images

[1148] The low-resolution satellite images acquired by the server are converted to high resolution using artificial intelligence.

[1149] Input: Acquired low-resolution satellite image data.

[1150] How it works: The server inputs a prompt to an image generation AI model (e.g., Stable Diffusion or DALL-E) and performs the process of converting low-resolution images to high-resolution ones.

[1151] Example prompt: "Please increase the resolution of a 300 dpi satellite image of Tokyo to 600 dpi."

[1152] Output: The generated high-resolution satellite image data is stored in the server's internal storage.

[1153] Step 4:

[1154] Extracting geographic information

[1155] The server extracts geographic information from high-resolution satellite images using image recognition artificial intelligence.

[1156] Input: High-resolution satellite image data.

[1157] How it works: The server uses image recognition AI (e.g., YOLO or Mask R-CNN) to identify geographical elements such as roads, buildings, and parks in the image and extract them as data.

[1158] Output: The extracted geographic information data is stored in a database on the server.

[1159] Step 5:

[1160] Map data generation

[1161] The server automatically generates map data based on the extracted geographic information.

[1162] Input: Geographical information data stored in a database.

[1163] How it works: The server uses GIS software (e.g. QGIS or ArcGIS) to draw a map from the information in the database and export it in GIS format.

[1164] Example prompt: "Generate up-to-date map data in GIS format based on the extracted geographic information."

[1165] Output: The generated map data is saved in the server's internal storage.

[1166] Step 6:

[1167] Provision and viewing of map data

[1168] The server transmits the generated map data to the terminal in response to a user request.

[1169] Input: User request data and generated map data.

[1170] Operation: The server retrieves the generated map data and sends the corresponding data to the user's device.

[1171] Output: Map data is sent to the device.

[1172] The device displays the received map data so that the user can view it. The device displays the map data using the Google Maps API or Leaflet, and the user can scroll and zoom.

[1173] Output: Users can also download map data as needed.

[1174] (Application example 1)

[1175] 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."

[1176] Navigation for autonomous vehicles requires highly accurate and real-time updates of map data. However, current systems do not update map data frequently or accurately enough, which may result in reduced safety and efficiency during driving. In addition, there is a lack of systems that can quickly and automatically acquire and provide the latest geographic information. To solve this issue, it is necessary to provide autonomous vehicles with the latest high-resolution map data to improve navigation accuracy and safety.

[1177] 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.

[1178] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, and means for navigating an autonomous vehicle using the provided map data, thereby making it possible to provide the latest high-resolution map data to autonomous vehicles in real time.

[1179] "Satellite imagery" refers to images taken from satellites to capture the Earth's surface and atmosphere.

[1180] "High resolution" is the process of increasing the detail of an image and converting it into a clearer image.

[1181] "Generative artificial intelligence" is a technology that uses AI technology to generate data and improve data resolution.

[1182] "Image recognition artificial intelligence" is a technology that uses AI technology to extract and recognize specific information from images.

[1183] "Geographic information" refers to information about a specific area on the earth's surface, such as information about topography, buildings, roads, etc.

[1184] "Map data" refers to data necessary to aggregate geographic information and display it as a map.

[1185] "Auto-generation" is the process by which a system automatically generates data or information.

[1186] "Communication means" refers to the technologies and tools used to send and receive data and information.

[1187] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without a human driver.

[1188] "Navigation" is a function that provides the optimal route to reach a destination.

[1189] The present invention is a system for providing highly accurate map data for autonomous vehicles. The system acquires satellite images, enhances their resolution, extracts geographic information, and then generates and provides map data suitable for the navigation system of the autonomous vehicle. Specific embodiments for implementing the invention are described below.

[1190] System configuration

[1191] server

[1192] The server has the following main functions:

[1193] 1. Obtaining satellite imagery - The server uses the API of an external satellite data provider to obtain satellite imagery for the specified area and date and time. For example, an API request is sent to obtain the latest satellite imagery of Tokyo.

[1194] 2. High-resolution imagery - The acquired satellite images are then made high-resolution using AI. For example, a 300 dpi image is converted to 600 dpi to enhance the details.

[1195] 3. Extraction of geographic information - Geographic information is extracted from the high-resolution images using image recognition AI. The server stores this geographic information in a database.

[1196] 4. Map Data Generation - Automatically generate map data based on the extracted geographic information. Export and save the map data in standard GIS formats.

[1197] 5. Communication - Sending the provided map data to the autonomous vehicle's navigation system.

[1198] Terminal

[1199] The user's device will be the interface of the autonomous vehicle equipped with the navigation system. Specifically, it will have the following functions:

[1200] 1. User Interface - Provides real-time map data to users on the autonomous vehicle's navigation system.

[1201] 2. View map data - Display the provided map data in real time, set destinations and get route guidance.

[1202] 3. Navigation - Using the latest map data to provide optimal routes and support autonomous driving.

[1203] User

[1204] The user is a person riding in an autonomous vehicle. The user performs the following actions:

[1205] 1. Map Data Request - Request the latest map data through the navigation system.

[1206] 2. Use of map data - Use the navigation function of the autonomous vehicle to head to your destination.

[1207] Specific examples

[1208] For example, if a user uses an autonomous vehicle that uses the latest map data for Tokyo, the process would be as follows:

[1209] 1. The user requests the latest map data for Tokyo via the navigation system.

[1210] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[1211] 3. Extract geographic information using image recognition AI and store it in a database.

[1212] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[1213] 5. The server provides the generated map data to the navigation system of the autonomous vehicle, and the user uses it to head to their destination.

[1214] Prompt Sentence Examples

[1215] The following is an example of a prompt to be input to a generative AI model used to enhance satellite imagery and extract geographic information:

[1216] Obtain high-resolution satellite imagery for a specified area and date / time. Extract detailed geographic information from the imagery and generate map data based on it. Example: Tokyo, 2023-10-10, map data including roads, buildings, and terrain information.

[1217] The present invention makes it possible to provide automated driving vehicles with real-time, highly accurate map data, thereby realizing safe and efficient driving.

[1218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1219] Step 1:

[1220] The server receives a request for map data from the user, including the requested region and date and time, and uses this information to send a request to the API of an external satellite data provider.

[1221] Step 2:

[1222] The server obtains satellite images for the specified area and date and time from the satellite data provider. The input is the satellite image obtained from the API response. The server stores this image in memory.

[1223] Step 3:

[1224] The server inputs the acquired satellite image into a generative AI model and performs high-resolution image generation. Specifically, low-resolution satellite images are input into the generative AI, and a high-resolution, detailed image is output. This process improves the image resolution.

[1225] Step 4:

[1226] The server inputs high-resolution satellite images into an image recognition AI (artificial intelligence) to extract geographic information, which analyzes information such as roads, buildings, and terrain pixel by pixel and outputs structured data for storage in a database.

[1227] Step 5:

[1228] The server automatically generates map data based on the extracted geographic information. Specifically, it organizes the geographic information in the database in GIS format and generates map data. This result is then output in a standard map file format (e.g., GeoJSON).

[1229] Step 6:

[1230] The server transmits the generated map data to the navigation system of the autonomous vehicle. The input is the generated map data, and the output is a communication packet containing the data. In this step, the data is transmitted using a communication means.

[1231] Step 7:

[1232] The terminal displays the received map data in real time on the navigation system of the autonomous vehicle. The input is map data received from the communication means, and the output is the latest map information displayed on the display inside the vehicle. The user uses this map data to set destinations and get route guidance.

[1233] Step 8:

[1234] The autonomous vehicle navigates using the map data provided by the device. Specifically, it references the latest high-resolution map data to support safe and efficient driving. The input is the latest map data, and the output is the autonomous vehicle's driving route.

[1235] This series of processes enables the provision of real-time, highly accurate map data, improving navigation for autonomous vehicles.

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

[1237] The present invention combines a system that acquires satellite images, enhances their resolution, extracts geographic information, and then automatically generates map data, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1238] System configuration

[1239] This system consists of the following main components:

[1240] 1. Server

[1241] Acquiring satellite images

[1242] High-resolution satellite images

[1243] Extracting geographic information

[1244] Map data generation

[1245] Saving map data

[1246] Processing user requests

[1247] Recognizing user emotions with an emotion engine

[1248] Map data provided

[1249] 2. Terminal

[1250] User Interface

[1251] Emotion input means (e.g., voice recognition, facial expression recognition)

[1252] Viewing map data

[1253] Download map data

[1254] 3. Users

[1255] Map data request

[1256] Emotion data input (voice and facial expressions)

[1257] Use of map data

[1258] Program processing

[1259] Acquiring satellite images

[1260] The server obtains satellite images of a specified area. To do this, the server uses the API of an external satellite data provider to request the required area and date and time. For example, the server downloads satellite images of the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[1261] High-resolution satellite images

[1262] The acquired satellite images are then converted to high resolution using generation AI. The server inputs the low-resolution image data into the generation AI to generate a detailed image. For example, a 300 dpi image can be converted to 600 dpi, and detailed information can be added.

[1263] Extracting geographic information

[1264] Image recognition AI is used to extract geographic information from high-resolution images. The server recognizes information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel.

[1265] Map data generation

[1266] Map data is automatically generated based on the extracted geographic information. The server generates map data in standard GIS format based on the information stored in the database. For example, roads, buildings, parks, etc. are plotted on the map.

[1267] Recognizing user emotions with an emotion engine

[1268] When a user browses map data, the emotion engine recognizes the user's emotions. Specifically, the device acquires voice data and facial expression data and sends it to the server. The emotion engine on the server analyzes this data and identifies the user's emotions. For example, emotions are recognized from the tone and pronunciation of the user's voice commands.

[1269] Providing map data and responding to emotions

[1270] The server changes the content and display of the map data it provides based on the user's emotions. For example, if the user expresses impatience, the server will provide route guidance as quickly as possible. The server then returns the generated map data to the user's device, where the user can view and download it.

[1271] Specific examples

[1272] For example, if a user needs to get to a destination in Tokyo in a hurry, the process would be as follows:

[1273] 1. The user requests the latest map data for Tokyo via the device's web browser.

[1274] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[1275] 3. Extract geographic information using image recognition AI and store it in a database.

[1276] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[1277] 5. The user speaks to the device, saying "I'm in a hurry."

[1278] 6. The server's emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[1279] 7. The server generates and provides map data for quick route guidance based on the user's emotions.

[1280] 8. The user displays and uses the latest map data returned on their device and heads to their destination.

[1281] In this way, the system of the present invention recognizes the user's emotions and provides map data in response to them, thereby realizing prompt and appropriate support that meets the user's needs.

[1282] The processing flow will be explained below.

[1283] Step 1:

[1284] The server obtains satellite imagery for a specified area. The server sends a request to the API of an external satellite data provider, specifying the required dataset for the required area and date and time. For example, to download satellite imagery for the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[1285] Step 2:

[1286] The server saves the downloaded satellite images in the internal storage. The acquired satellite images remain in low resolution, but are passed on to the next process for higher resolution.

[1287] Step 3:

[1288] The server inputs acquired satellite images into the generative AI model. The server converts the image data into an input format for the generative AI and performs appropriate preprocessing (e.g., shaping pixel information).

[1289] Step 4:

[1290] The server runs a generative AI to convert low-resolution images to high-resolution ones. The generative AI improves the image resolution and adds details. For example, it converts a 300dpi image to 600dpi, making the details of buildings and roads clearer.

[1291] Step 5:

[1292] The server saves the high-resolution image to its internal storage, ready to be passed on to the next processing step.

[1293] Step 6:

[1294] The server inputs the high-resolution image into the image recognition AI, which then converts the image data to fit the input format of the image recognition AI.

[1295] Step 7:

[1296] The server runs image recognition AI to identify and extract geographic information such as terrain, buildings, and roads. The image recognition AI uses pattern recognition technology to identify various geographic elements pixel by pixel and extract information, such as the width of roads, the height of buildings, and the flow of rivers.

[1297] Step 8:

[1298] The server saves the extracted geographic information in a database. The extracted information is stored in the database as structured data (e.g., GeoJSON format).

[1299] Step 9:

[1300] The server generates map data based on the geographic information stored in the database. The map data generation engine uses the stored information to visualize each geographic element and generate map data. For example, it creates a map by integrating elements such as roads, buildings, and parks.

[1301] Step 10:

[1302] The server exports the generated map data to a GIS format (e.g. Shapefile), saves the exported map data in a file format, and prepares it for distribution to users.

[1303] Step 11:

[1304] The user sends a request to the server using a device (e.g., PC or smartphone). The user accesses the API endpoint for map data using the device's web browser and requests the latest map data for the relevant area (e.g., "latest map of Tokyo").

[1305] Step 12:

[1306] The device acquires the user's emotional data. The device records the user's voice and captures facial expression data with a camera. For example, the user may say, "I'm in a hurry."

[1307] Step 13:

[1308] The device sends emotional data to the server, which then transfers recorded voice data and captured facial expression data to the server.

[1309] Step 14:

[1310] The server's emotion engine analyzes the emotion data and recognizes the user's emotion. The emotion engine analyzes voice tone and facial expression changes to identify the user's emotion. For example, it can recognize impatience from voice data.

[1311] Step 15:

[1312] The server adjusts the content and display of map data provided according to the user's emotions. Based on the recognized emotions (e.g., impatience), map data is generated that quickly highlights the shortest route to the destination.

[1313] Step 16:

[1314] The server returns the generated map data to the user's device, and the map data is provided to the user in an appropriate format (e.g., GIS format, image format).

[1315] Step 17:

[1316] The user can view and use the latest map data returned on their device. The user can check the map data and download it as needed or integrate it into their own application. For example, if the user is in a hurry, they can quickly check the shortest route to their destination.

[1317] Through these processing steps, the system of the present invention can provide the latest, high-quality map data that takes into account the user's feelings and quickly respond to the user's needs.

[1318] Example 2

[1319] 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."

[1320] Conventional map generation systems lack the functionality to provide optimal map data according to the user's emotions and circumstances, which can cause inconvenience to users. Furthermore, the low accuracy of the automatic generation and high-resolution map data can result in problems in which users' needs are not fully met. There is a demand for a system that can solve these issues and provide users with appropriate and prompt map data.

[1321] 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.

[1322] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, emotion recognition means for identifying a user's emotion, means for adjusting the map data based on the identified emotion, and communication means for providing the generated map data, thereby making it possible to provide customized map data that corresponds to the user's emotion and situation.

[1323] "Means for obtaining satellite imagery" means means for obtaining satellite imagery for a specified area and date and time using the API of a satellite data provider.

[1324] "Generative artificial intelligence means" refers to artificial intelligence technology for converting low-resolution satellite images into high-resolution images.

[1325] "Image recognition artificial intelligence means" is an artificial intelligence technology for extracting geographic information from high-resolution images.

[1326] The "means for automatically generating map data" is a means for generating map data in a standard geographic information system format based on the extracted geographic information.

[1327] "Emotion recognition means" is a technology for identifying emotions by analyzing the user's voice and facial expression data.

[1328] The "means for adjusting map data" is a means for changing the content or display of map data based on the identified emotion of the user.

[1329] "Communication means" refers to a communication technology for providing the generated map data to the user's terminal.

[1330] In this invention, a geographic information system is constructed using a system in which servers, terminals, and users each have specific roles. To implement this system, it is necessary to utilize multiple artificial intelligence technologies and communication methods. Below, we will explain how to specifically implement this invention.

[1331] Server Features

[1332] The server has the following main functions:

[1333] 1. Acquisition of satellite images: The server uses the API of a satellite data provider to acquire satellite images for the area and date and time specified by the user. Specifically, APIs such as DigitalGlobe and GeoEye can be used.

[1334] 2. High-resolution imagery: The server inputs the acquired low-resolution satellite images into a generative artificial intelligence (generative AI) model to increase the resolution. This generative AI uses technologies such as GANs (Generative Adversarial Networks).

[1335] 3. Extraction of geographic information: The server extracts geographic information from the high-resolution satellite images using image recognition AI (e.g., object detection models using TensorFlow or PyTorch). The geographic information is then stored in a database.

[1336] 4. Map data generation: The server automatically generates map data based on the extracted geographic information. This generation process uses GIS software such as QGIS or ArcGIS.

[1337] 5. Emotion Recognition: The server has an emotion recognition means (e.g., IBM Watson Tone Analyzer or Microsoft Azure Emotion API) to analyze the user's voice and facial expression data to identify emotions.

[1338] 6. Adjusting map data: The server adjusts the display and content of the map data based on the identified user's emotions.

[1339] 7. Communication: The server has a communication means to provide the generated map data to the user's terminal, and sends and receives data as appropriate.

[1340] Device Features

[1341] 1. User Interface: The device provides an interface for users to request and view map data. This is done through a web browser or dedicated application.

[1342] 2. Emotion input means: The device has a means to acquire the user's emotion data through voice recognition and facial expression recognition and send it to the server. This includes hardware such as a microphone and camera.

[1343] 3. Displaying and downloading map data: The terminal has the means to display map data provided by the server and to download it as necessary.

[1344] User Roles

[1345] 1. Request: The user requests map data for a specific region and date and time through the device interface.

[1346] 2. Emotional data input: The user inputs emotional data using voice and facial expressions and sends it to the server via the terminal.

[1347] 3. Use of map data: Users can view the provided map data and download and use it as needed.

[1348] Specific operation example

[1349] For example, if a user needs to get to a destination in Tokyo in a hurry, the process would be as follows:

[1350] 1. The user requests the latest map data for Tokyo via the device's web browser.

[1351] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[1352] 3. Extract geographic information using image recognition AI and store it in a database.

[1353] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[1354] 5. The user speaks to the device, saying "I'm in a hurry."

[1355] 6. The server's emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[1356] 7. The server generates and provides map data for quick route guidance based on the user's emotions.

[1357] 8. The user displays and uses the latest map data returned on their device and heads to their destination.

[1358] Prompt Sentence Examples

[1359] Below are some example prompts to be input to the generative AI model:

[1360] Prompt 1:

[1361] "Please increase the resolution of this satellite image. Convert it from 300 dpi to 600 dpi and add more detailed information."

[1362] Prompt Statement 2:

[1363] "Extract geographic information from this high-resolution imagery. Identify roads, buildings, and terrain information pixel by pixel and store it in a database."

[1364] Prompt statement 3:

[1365] "Analyze user emotions. Recognize user emotions from voice data and identify impatience and anxiety."

[1366] This system utilizes a variety of artificial intelligence technologies and communication methods to provide fast, high-quality map data that meets user needs.

[1367] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1368] Step 1:

[1369] 1. The server receives a request from the user for the specified location and date and time.

[1370] Input: Location and date / time request data from the user (e.g., Tokyo on October 1, 2023).

[1371] Behavior: The server analyzes the request data and prepares it to use the API of an external satellite data provider.

[1372] Output: API request data.

[1373] Step 2:

[1374] 1. The server sends a request to an API of an external satellite data provider to obtain satellite imagery for the specified area and date and time.

[1375] Input: API request data.

[1376] Operation: The server uses the acquired API request data to call the API of the satellite data provider and download the specified satellite imagery, for example, using APIs from DigitalGlobe or GeoEye.

[1377] Output: Low-resolution satellite image data.

[1378] Step 3:

[1379] 1. The low-resolution satellite images acquired by the server are input into a generative AI model to increase their resolution.

[1380] Input: Low-resolution satellite image data.

[1381] How it works: The server uses a generative AI model (e.g., GANs) to convert low-resolution satellite imagery to high-resolution. The server sends a prompt to the generative AI model, such as "Please resize this image from 300 dpi to 600 dpi," and receives the high-resolution image.

[1382] Output: High resolution satellite image data.

[1383] Step 4:

[1384] 1. The server inputs high-resolution satellite images into image recognition AI to extract geographic information.

[1385] Input: High-resolution satellite image data.

[1386] How it works: The server uses an image recognition AI model (e.g., an object detection model using TensorFlow or PyTorch) to extract geographic information (e.g., roads, buildings, terrain, etc.) from satellite images. The server then sends a prompt to the image recognition AI saying, "Please extract geographic information from this high-resolution image," and stores the extracted geographic information in a database.

[1387] Output: Geographical information data.

[1388] Step 5:

[1389] 1. The server automatically generates map data based on the extracted geographic information.

[1390] Input: Geographical data.

[1391] How it works: The server uses GIS software (e.g., QGIS or ArcGIS) to generate map data in a standard geographic information system format from the extracted geographic information, specifically plotting roads, buildings, parks, and other geographic information on a map.

[1392] Output: Auto-generated map data.

[1393] Step 6:

[1394] 1. The device collects the user's voice and facial expression data.

[1395] Input: User's voice and facial expression data.

[1396] How it works: The device uses a microphone and camera to capture the user's voice and facial expressions and collect them as data.

[1397] Output: Audio data and facial expression data.

[1398] Step 7:

[1399] 1. The device sends the collected voice and facial expression data to the server.

[1400] Input: speech and facial expression data.

[1401] Operation: The terminal performs communication processing to send the collected data to the server.

[1402] Output: Voice data and facial expression data are sent to the server.

[1403] Step 8:

[1404] 1. The server analyzes the user's emotions using emotion recognition means.

[1405] Input: speech and facial expression data.

[1406] How it works: The server uses an emotion recognition tool (e.g., IBM Watson Tone Analyzer or Microsoft Azure Emotion API) to analyze the user's emotions from the transmitted voice and facial expression data. It then sends a prompt to the emotion recognition tool saying, "Please analyze the user's emotions from this voice data."

[1407] Output: User emotion data.

[1408] Step 9:

[1409] 1. The server adjusts the content and display of map data based on the user's emotional data.

[1410] Input: Auto-generated map data and user emotion data.

[1411] How it works: The server uses the identified emotion data to adjust the display and content of map data, for example highlighting the shortest route to provide quick route guidance to users in a hurry.

[1412] Output: Adjusted map data.

[1413] Step 10:

[1414] 1. The server sends the adjusted map data to the user's device.

[1415] Input: Adjusted map data.

[1416] Operation: The server transmits the generated and adjusted map data to the user's terminal using a communication means.

[1417] Output: Map data sent back to the user's device.

[1418] Step 11:

[1419] 1. The user can view and download the provided map data.

[1420] Input: Map data sent to the user's device.

[1421] How it works: The user can view the returned map data via their device and download it as needed.

[1422] Output: Map data available to the user.

[1423] This series of processing steps makes it possible to provide high-resolution map data that is optimized according to the user's emotions and situation.

[1424] (Application example 2)

[1425] 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."

[1426] Conventional map generation systems simply provide map data without customizing it according to the user's emotions or situation. As a result, the same map data is provided whether the user is in a hurry or has other specific emotions, resulting in an unoptimized user experience. In particular, services such as food delivery require fast and efficient route guidance that is in line with the user's emotions, so a system that solves this problem is needed.

[1427] 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.

[1428] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, communication means for providing the generated map data, an emotion engine for recognizing the user's emotions, and means for customizing the map data based on the recognized emotions, thereby enabling fast and efficient route guidance according to the user's emotions.

[1429] A "means for acquiring satellite imagery" is a device or system used to acquire satellite imagery of a particular area.

[1430] "Generative artificial intelligence means for converting acquired satellite imagery to high resolution" means a generative AI model or associated software used to convert acquired low-resolution satellite imagery to high resolution.

[1431] "Image recognition artificial intelligence means for extracting geographic information from high-resolution images" refers to an AI algorithm for recognizing and extracting geographic information such as roads and buildings from high-resolution images.

[1432] The "means for automatically generating map data based on extracted geographic information" refers to a system or tool that automatically generates map data using extracted geographic information.

[1433] The "communication means for providing the generated map data" refers to the network and communication infrastructure for providing the generated map data to the user.

[1434] An "emotion engine that recognizes user emotions" is software or hardware that analyzes voice data and facial expression data to identify the user's emotions.

[1435] A "means for customizing map data based on recognized emotions" is a system or algorithm that changes the content or display of map data in response to recognized user emotions.

[1436] The present invention is a system that combines a system that acquires satellite images, enhances their resolution, extracts geographic information, and automatically generates map data with an emotion engine that recognizes user emotions. Specific embodiments of the present invention are described below.

[1437] System configuration

[1438] This system consists of the following main components:

[1439] 1. Server

[1440] Means of acquiring satellite images

[1441] AI method for generating high-resolution satellite images

[1442] Image recognition AI method to extract geographic information from high-resolution images

[1443] A means of automatically generating map data based on extracted geographic information

[1444] Communication means for providing generated map data

[1445] Emotion engine that recognizes user emotions

[1446] A means to customize map data based on recognized emotions

[1447] 2. Terminal

[1448] User Interface

[1449] Emotion input means (voice recognition, facial expression recognition)

[1450] Viewing map data

[1451] Download map data

[1452] Delivery tracking feature

[1453] 3. Users

[1454] Map data request

[1455] Emotion data input (voice and facial expressions)

[1456] Use of map data

[1457] Program Processing and Data Flow

[1458] Acquisition of satellite images and their resolution enhancement

[1459] The server retrieves satellite images of a specific area using the API of an external satellite data provider. Since the retrieved images are often low-resolution, the server uses a generative AI model (for example, a model based on TensorFlow) to increase the resolution.

[1460] Extracting geographic information

[1461] From the generated high-resolution images, geographic information (e.g., roads and buildings) is extracted using image recognition AI tools. The high-resolution images serve as input data for AI algorithms to precisely extract detailed geographic information.

[1462] Map data generation

[1463] Based on the extracted geographic information, map data is automatically generated, which the server exports in a standard geographic information system (GIS) format and stores in a database.

[1464] The role of the emotional engine

[1465] When a user requests map data, the device sends emotional data to the server through voice and facial expressions. The server's emotion engine analyzes this data and recognizes, for example, the emotion of being in a hurry. Based on the recognition results, the server optimizes and provides the map data.

[1466] Specific examples

[1467] For example, in a food delivery scenario, if the user is in a hurry, the following process would occur:

[1468] 1. A user requests the latest route data for a specific area via a smartphone application.

[1469] 2. The server obtains the latest satellite images of the area from a satellite data provider and uses generative AI to enhance the resolution.

[1470] 3. Geographic information is extracted from the high-resolution images and map data is generated.

[1471] 4. The user utters "I'm in a hurry" via voice input.

[1472] 5. The emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[1473] 6. The server generates and provides map data for quick delivery route guidance based on the user's emotions.

[1474] 7. The user checks the map data provided on their smartphone and the meal is delivered via the optimal route.

[1475] Examples of prompt statements

[1476] "I'm in a hurry, I want my food to arrive quickly!"

[1477] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1478] Step 1:

[1479] A user requests the latest route data for a specific area via a terminal. The request includes area information and date and time information. The server receives this request and obtains satellite images of the requested area using the API of an external satellite data provider. The input is the requested area and date and time information, and the output is the obtained low-resolution satellite image.

[1480] Step 2:

[1481] The server uses a generative AI model (such as a model based on TensorFlow) to convert the acquired low-resolution satellite image into a high-resolution image. The generative AI model inputs the low-resolution image and outputs a high-resolution image. As part of data processing, the generative AI performs image completion, and the output is a high-resolution image.

[1482] Step 3:

[1483] The server uses image recognition AI tools to extract geographic information from high-resolution images. Image recognition AI takes high-resolution images as input and outputs geographic information (e.g., coordinates of roads and buildings). Feature extraction algorithms are used to process the data in this step.

[1484] Step 4:

[1485] The server automatically generates map data based on the extracted geographic information. The extracted geographic information is used as input and map data in GIS format is output. Geographic Information System (GIS) tools are used to calculate this data.

[1486] Step 5:

[1487] At the same time, the device collects the user's emotional data (voice and facial expressions) and sends it to the server. The emotional data is input through the device's built-in voice and facial expression sensors. The output is the user's voice and facial expression data.

[1488] Step 6:

[1489] The server's emotion engine analyzes the user's emotion data and recognizes specific emotions (e.g., impatience). The input is the user's emotion data, and the output is the analyzed emotion result. A machine learning model is used to calculate this data.

[1490] Step 7:

[1491] The server customizes the map data based on the recognized emotion. The map data with optimal route guidance according to the specific emotion is generated. The output is customized map data. A customization algorithm is used for data processing.

[1492] Step 8:

[1493] The generated customized map data is sent from the server to the terminal, which then provides the user with quick route guidance. The input is the customized map data, and the output is the map information displayed to the user.

[1494] Step 9:

[1495] The user checks the map data provided on the terminal and receives delivery according to the instructed route. The input is customized map data, and the output is behavior based on the optimal route.

[1496] Specific prompt examples

[1497] "I'm in a hurry, I want my food to arrive quickly!"

[1498] 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.

[1499] 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.

[1500] 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.

[1501] [Fourth embodiment]

[1502] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1503] 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.

[1504] 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).

[1505] 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.

[1506] 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.

[1507] 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).

[1508] 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.

[1509] 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.

[1510] 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.

[1511] 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.

[1512] 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.

[1513] 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.

[1514] 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."

[1515] The present invention provides a system for automatically generating map data after acquiring satellite images and increasing their resolution and extracting geographic information. Specific embodiments for carrying out the present invention will now be described.

[1516] System configuration

[1517] This system consists of the following main components:

[1518] 1. Server

[1519] Acquiring satellite images

[1520] High-resolution satellite images

[1521] Extracting geographic information

[1522] Map data generation

[1523] Saving map data

[1524] Processing user requests

[1525] Map data provided

[1526] 2. Terminal

[1527] User Interface

[1528] Viewing map data

[1529] Download map data

[1530] 3. Users

[1531] Map data request

[1532] Use of map data

[1533] Program processing

[1534] Acquiring satellite images

[1535] The server retrieves satellite imagery for a specified area. To do this, the server uses the API of an external satellite data provider to request the required area and date and time. For example, the server may download satellite imagery for an entire city taken on a specific date.

[1536] High-resolution satellite images

[1537] The acquired satellite images are then converted to high resolution using generation AI. The server inputs low-resolution image data into the generation AI to generate a detailed image. For example, an image taken at 300 dpi can be converted to 600 dpi, and detailed information can be added.

[1538] Extracting geographic information

[1539] Image recognition AI is used to extract geographic information from high-resolution images. The server recognizes information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel.

[1540] Map data generation

[1541] Map data is automatically generated based on the extracted geographic information. The server generates map data in standard GIS format based on the information stored in the database. For example, roads, buildings, parks, etc. are plotted on the map.

[1542] Processing user requests and providing map data

[1543] The user sends a request for map data to the server via their device. The server responds to the user's request by searching for and providing the latest map data for the relevant area. The user can then view the map data on their device and download it as needed.

[1544] Specific examples

[1545] For example, if a user wants to obtain the latest map data for Tokyo, the process would be as follows:

[1546] 1. The user requests the latest map data for Tokyo via the device's web browser.

[1547] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[1548] 3. Extract geographic information using image recognition AI and store it in a database.

[1549] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[1550] 5. The server provides the generated map data to the user's device, where the user can view and download it.

[1551] This invention significantly reduces the cost and time required for map creation and provides up-to-date map information, making it a useful tool in fields that require accurate map data, such as autonomous driving and logistics.

[1552] The processing flow will be explained below.

[1553] Step 1:

[1554] The server obtains satellite imagery for a specified area. The server sends a request to an external satellite data provider's API, specifying the required region and date and time dataset. For example, the server downloads satellite imagery for the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[1555] Step 2:

[1556] The server saves the downloaded satellite images in the internal storage. The acquired satellite images remain in low resolution, but are passed on to the next process for higher resolution.

[1557] Step 3:

[1558] The server inputs acquired satellite images into the generative AI model. The server converts the image data into an input format for the generative AI (for example, converting it into a pixel array).

[1559] Step 4:

[1560] The server runs a generation AI to convert low-resolution images to high-resolution ones. The generation AI increases the image resolution and adds detailed information. For example, converting a 300 dpi image to 600 dpi allows the details of buildings and roads to be distinguished.

[1561] Step 5:

[1562] The server saves the high-resolution image to its internal storage, ready to be passed on to the next processing step.

[1563] Step 6:

[1564] The server inputs the high-resolution image into the image recognition AI. The server then converts the image data into an input format for the image recognition AI.

[1565] Step 7:

[1566] The server runs image recognition AI to identify and extract geographic information such as terrain, buildings, and roads. The image recognition AI uses pattern recognition technology to extract various geographic elements pixel by pixel. For example, it identifies the width of roads, the height of buildings, and the flow of rivers.

[1567] Step 8:

[1568] The server saves the extracted geographic information in a database. The extracted information is stored in the database as structured data (e.g., GeoJSON format).

[1569] Step 9:

[1570] The server generates map data based on the geographic information stored in the database. The map data generation engine uses the stored information to visualize each geographic element and generate map data. For example, roads, buildings, parks, etc. are plotted on the map.

[1571] Step 10:

[1572] The server exports the generated map data to a GIS format (e.g. Shapefile), saves the exported map data in a file format, and prepares it for distribution to users.

[1573] Step 11:

[1574] A user sends a request to the server using a device (such as a PC or smartphone). The user accesses the map data API endpoint using a web browser and requests the latest map data for the relevant area (e.g., "latest map of Tokyo").

[1575] Step 12:

[1576] The server receives the user's request and searches for map data for the corresponding area. The server retrieves the latest map data for the specified area from the database.

[1577] Step 13:

[1578] The server returns the acquired map data to the user's device, and the map data is provided to the user in an appropriate format (e.g., GIS format, image format).

[1579] Step 14:

[1580] The user can view and use the latest map data returned on their device. The user can check the map data and download it or integrate it into their own application if necessary.

[1581] Through these processing steps, the system of the present invention can efficiently provide users with the most up-to-date, high-quality map data.

[1582] Example 1

[1583] 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."

[1584] Conventional map data generation systems have had problems with the time and effort required for processes such as acquiring satellite images, increasing the resolution of the images, extracting geographic information, and generating map data. Furthermore, performing these tasks with high accuracy requires advanced specialized knowledge and technology, resulting in increased costs and reduced efficiency. Providing the latest map information quickly and accurately has proven particularly challenging, making highly reliable map data essential in fields such as autonomous driving and logistics.

[1585] 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.

[1586] In this invention, the server includes means for acquiring satellite images of a specified area, means for increasing the resolution of the acquired satellite images using generation AI, means for extracting geographic information from the increased resolution satellite images using image recognition AI, means for automatically generating map data in a geographic information system format based on the extracted geographic information, and communication means for providing the generated map data. This makes it possible to generate and provide the latest map data more quickly and accurately than conventional systems.

[1587] "Means for acquiring satellite images of a designated area" refers to devices or software that acquire satellite images of a designated area through the API of an external satellite data provider, etc.

[1588] "Means for increasing resolution using generative AI" refers to devices or software that increase the resolution of low-resolution satellite images using a generative AI model (e.g., an image generation AI model).

[1589] "Means for extracting geographic information using image recognition artificial intelligence" refers to devices or software that use image recognition technology to identify geographic elements such as roads, buildings, and parks from high-resolution satellite images and extract them as data.

[1590] "Means for automatically generating map data in geographic information system format" refers to devices or software that generate map data in standard GIS (geographic information system) format based on extracted geographic information.

[1591] The "communication means for providing the generated map data" refers to a network communication device or software for providing the generated map data to the user's terminal.

[1592] "Remote sensing technology" is a technology for collecting information on the ground from remote locations such as satellites and aircraft.

[1593] A "user interface" is an interface through which a user interacts with a system, including a web browser or application form.

[1594] MODE FOR CARRYING OUT THE INVENTION

[1595] The present invention provides a system for automatically generating map data after acquiring satellite images and increasing their resolution and extracting geographic information. Specific embodiments for carrying out the present invention will now be described.

[1596] System configuration

[1597] This system consists of the following main components:

[1598] 1. Server:

[1599] Acquiring satellite images

[1600] High-resolution satellite images

[1601] Extracting geographic information

[1602] Map data generation

[1603] Saving map data

[1604] Processing user requests

[1605] Map data provided

[1606] 2. Terminal:

[1607] User Interface

[1608] Viewing map data

[1609] Download map data

[1610] 3. User:

[1611] Map data request

[1612] Use of map data

[1613] Acquiring satellite images

[1614] The server obtains satellite images of a specific area. In this process, the server uses the API of an external satellite data provider (for example, an external resource provision API) to make a request specifying the desired area and date and time. By using remote sensing technology, the server can obtain the latest and most detailed satellite images. For example, the server downloads satellite images of an entire city taken on a specific date.

[1615] High-resolution satellite images

[1616] The acquired low-resolution satellite images are then converted to high resolution using artificial intelligence. The server inputs a prompt to the image generation AI model (e.g., Stable Diffusion or DALL-E) to convert the low-resolution image to high resolution, and the process is carried out. As a concrete example, an image taken at 300 dpi is converted to 600 dpi, and detailed information is added. An example of a prompt is as follows:

[1617] "Please increase the resolution of a 300 dpi satellite image of Tokyo to 600 dpi."

[1618] Extracting geographic information

[1619] Image recognition AI is used to extract geographic information from high-resolution satellite images. The server uses image recognition AI (for example, YOLO or Mask R-CNN) to identify information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel. This makes it possible to obtain detailed geographic information.

[1620] Map data generation

[1621] The server automatically generates map data based on the extracted geographic information. This process outputs the data in a standard Geographic Information System (GIS) format. The server uses GIS software (e.g., QGIS or ArcGIS) to draw a map based on the information stored in the database and export it in GIS format. For example, it plots roads, buildings, parks, etc. on the map. An example of a prompt is:

[1622] "Generate the latest map data in GIS format based on the extracted geographic information."

[1623] Provision and viewing of map data

[1624] The map data generated by the server is provided in response to user requests. When a user requests map data via a device, the server searches for the latest map data for the relevant area and sends it to the device. The device then displays the received map data so that the user can view it. Furthermore, the user can download map data as needed. In this case, the user interface on the device is implemented using tools such as Google Maps API and Leaflet.

[1625] This invention will significantly reduce the cost and time required for map creation and provide constantly up-to-date map information, making it a useful tool in fields that require accurate map data, such as autonomous driving and logistics.

[1626] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1627] Program processing flow

[1628] Step 1:

[1629] Submitting a User Request

[1630] A user requests the latest map data for a specific area via a terminal.

[1631] Input: The area name and request content specified by the user, for example, "latest map data for Tokyo."

[1632] How it works: A user enters a request into the search field in their device's web browser and clicks the submit button.

[1633] Output: Request data is sent from the terminal to the server.

[1634] Step 2:

[1635] Acquiring satellite images

[1636] The server receives the user's request and retrieves satellite images of the specified area.

[1637] Input: Request data from the user (e.g., "latest satellite image of Tokyo").

[1638] How it works: The server accesses the API of an external satellite data provider and requests satellite imagery for a specified region and date and time.

[1639] Output: The acquired low-resolution satellite image data is stored in the server's internal storage.

[1640] Step 3:

[1641] High-resolution satellite images

[1642] The low-resolution satellite images acquired by the server are converted to high resolution using artificial intelligence.

[1643] Input: Acquired low-resolution satellite image data.

[1644] How it works: The server inputs a prompt to an image generation AI model (e.g., Stable Diffusion or DALL-E) and performs the process of converting low-resolution images to high-resolution ones.

[1645] Example prompt: "Please increase the resolution of a 300 dpi satellite image of Tokyo to 600 dpi."

[1646] Output: The generated high-resolution satellite image data is stored in the server's internal storage.

[1647] Step 4:

[1648] Extracting geographic information

[1649] The server extracts geographic information from high-resolution satellite images using image recognition artificial intelligence.

[1650] Input: High-resolution satellite image data.

[1651] How it works: The server uses image recognition AI (e.g., YOLO or Mask R-CNN) to identify geographical elements such as roads, buildings, and parks in the image and extract them as data.

[1652] Output: The extracted geographic information data is stored in a database on the server.

[1653] Step 5:

[1654] Map data generation

[1655] The server automatically generates map data based on the extracted geographic information.

[1656] Input: Geographical information data stored in a database.

[1657] How it works: The server uses GIS software (e.g. QGIS or ArcGIS) to draw a map from the information in the database and export it in GIS format.

[1658] Example prompt: "Generate up-to-date map data in GIS format based on the extracted geographic information."

[1659] Output: The generated map data is saved in the server's internal storage.

[1660] Step 6:

[1661] Provision and viewing of map data

[1662] The server transmits the generated map data to the terminal in response to a user request.

[1663] Input: User request data and generated map data.

[1664] Operation: The server retrieves the generated map data and sends the corresponding data to the user's device.

[1665] Output: Map data is sent to the device.

[1666] The device displays the received map data so that the user can view it. The device displays the map data using the Google Maps API or Leaflet, and the user can scroll and zoom.

[1667] Output: Users can also download map data as needed.

[1668] (Application example 1)

[1669] 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."

[1670] Navigation for autonomous vehicles requires highly accurate and real-time updates of map data. However, current systems do not update map data frequently or accurately enough, which may result in reduced safety and efficiency during driving. In addition, there is a lack of systems that can quickly and automatically acquire and provide the latest geographic information. To solve this issue, it is necessary to provide autonomous vehicles with the latest high-resolution map data to improve navigation accuracy and safety.

[1671] 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.

[1672] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, and means for navigating an autonomous vehicle using the provided map data, thereby making it possible to provide the latest high-resolution map data to autonomous vehicles in real time.

[1673] "Satellite imagery" refers to images taken from satellites to capture the Earth's surface and atmosphere.

[1674] "High resolution" is the process of increasing the detail of an image and converting it into a clearer image.

[1675] "Generative artificial intelligence" is a technology that uses AI technology to generate data and improve data resolution.

[1676] "Image recognition artificial intelligence" is a technology that uses AI technology to extract and recognize specific information from images.

[1677] "Geographic information" refers to information about a specific area on the earth's surface, such as information about topography, buildings, roads, etc.

[1678] "Map data" refers to data necessary to aggregate geographic information and display it as a map.

[1679] "Auto-generation" is the process by which a system automatically generates data or information.

[1680] "Communication means" refers to the technologies and tools used to send and receive data and information.

[1681] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without a human driver.

[1682] "Navigation" is a function that provides the optimal route to reach a destination.

[1683] The present invention is a system for providing highly accurate map data for autonomous vehicles. The system acquires satellite images, enhances their resolution, extracts geographic information, and then generates and provides map data suitable for the navigation system of the autonomous vehicle. Specific embodiments for implementing the invention are described below.

[1684] System configuration

[1685] server

[1686] The server has the following main functions:

[1687] 1. Obtaining satellite imagery - The server uses the API of an external satellite data provider to obtain satellite imagery for the specified area and date and time. For example, an API request is sent to obtain the latest satellite imagery of Tokyo.

[1688] 2. High-resolution imagery - The acquired satellite images are then made high-resolution using AI. For example, a 300 dpi image is converted to 600 dpi to enhance the details.

[1689] 3. Extraction of geographic information - Geographic information is extracted from the high-resolution images using image recognition AI. The server stores this geographic information in a database.

[1690] 4. Map Data Generation - Automatically generate map data based on the extracted geographic information. Export and save the map data in standard GIS formats.

[1691] 5. Communication - Sending the provided map data to the autonomous vehicle's navigation system.

[1692] Terminal

[1693] The user's device will be the interface of the autonomous vehicle equipped with the navigation system. Specifically, it will have the following functions:

[1694] 1. User Interface - Provides real-time map data to users on the autonomous vehicle's navigation system.

[1695] 2. View map data - Display the provided map data in real time, set destinations and get route guidance.

[1696] 3. Navigation - Using the latest map data to provide optimal routes and support autonomous driving.

[1697] User

[1698] The user is a person riding in an autonomous vehicle. The user performs the following actions:

[1699] 1. Map Data Request - Request the latest map data through the navigation system.

[1700] 2. Use of map data - Use the navigation function of the autonomous vehicle to head to your destination.

[1701] Specific examples

[1702] For example, if a user uses an autonomous vehicle that uses the latest map data for Tokyo, the process would be as follows:

[1703] 1. The user requests the latest map data for Tokyo via the navigation system.

[1704] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[1705] 3. Extract geographic information using image recognition AI and store it in a database.

[1706] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[1707] 5. The server provides the generated map data to the navigation system of the autonomous vehicle, and the user uses it to head to their destination.

[1708] Prompt Sentence Examples

[1709] The following is an example of a prompt to be input to a generative AI model used to enhance satellite imagery and extract geographic information:

[1710] Obtain high-resolution satellite imagery for a specified area and date / time. Extract detailed geographic information from the imagery and generate map data based on it. Example: Tokyo, 2023-10-10, map data including roads, buildings, and terrain information.

[1711] The present invention makes it possible to provide automated driving vehicles with real-time, highly accurate map data, thereby realizing safe and efficient driving.

[1712] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1713] Step 1:

[1714] The server receives a request for map data from the user, including the requested region and date and time, and uses this information to send a request to the API of an external satellite data provider.

[1715] Step 2:

[1716] The server obtains satellite images for the specified area and date and time from the satellite data provider. The input is the satellite image obtained from the API response. The server stores this image in memory.

[1717] Step 3:

[1718] The server inputs the acquired satellite image into a generative AI model and performs high-resolution image generation. Specifically, low-resolution satellite images are input into the generative AI, and a high-resolution, detailed image is output. This process improves the image resolution.

[1719] Step 4:

[1720] The server inputs high-resolution satellite images into an image recognition AI (artificial intelligence) to extract geographic information, which analyzes information such as roads, buildings, and terrain pixel by pixel and outputs structured data for storage in a database.

[1721] Step 5:

[1722] The server automatically generates map data based on the extracted geographic information. Specifically, it organizes the geographic information in the database in GIS format and generates map data. This result is then output in a standard map file format (e.g., GeoJSON).

[1723] Step 6:

[1724] The server transmits the generated map data to the navigation system of the autonomous vehicle. The input is the generated map data, and the output is a communication packet containing the data. In this step, the data is transmitted using a communication means.

[1725] Step 7:

[1726] The terminal displays the received map data in real time on the navigation system of the autonomous vehicle. The input is map data received from the communication means, and the output is the latest map information displayed on the display inside the vehicle. The user uses this map data to set destinations and get route guidance.

[1727] Step 8:

[1728] The autonomous vehicle navigates using the map data provided by the device. Specifically, it references the latest high-resolution map data to support safe and efficient driving. The input is the latest map data, and the output is the autonomous vehicle's driving route.

[1729] This series of processes enables the provision of real-time, highly accurate map data, improving navigation for autonomous vehicles.

[1730] 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.

[1731] The present invention combines a system that acquires satellite images, enhances their resolution, extracts geographic information, and then automatically generates map data, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1732] System configuration

[1733] This system consists of the following main components:

[1734] 1. Server

[1735] Acquiring satellite images

[1736] High-resolution satellite images

[1737] Extracting geographic information

[1738] Map data generation

[1739] Saving map data

[1740] Processing user requests

[1741] Recognizing user emotions with an emotion engine

[1742] Map data provided

[1743] 2. Terminal

[1744] User Interface

[1745] Emotion input means (e.g., voice recognition, facial expression recognition)

[1746] Viewing map data

[1747] Download map data

[1748] 3. Users

[1749] Map data request

[1750] Emotion data input (voice and facial expressions)

[1751] Use of map data

[1752] Program processing

[1753] Acquiring satellite images

[1754] The server obtains satellite images of a specified area. To do this, the server uses the API of an external satellite data provider to request the required area and date and time. For example, the server downloads satellite images of the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[1755] High-resolution satellite images

[1756] The acquired satellite images are then converted to high resolution using generation AI. The server inputs the low-resolution image data into the generation AI to generate a detailed image. For example, a 300 dpi image can be converted to 600 dpi, and detailed information can be added.

[1757] Extracting geographic information

[1758] Image recognition AI is used to extract geographic information from high-resolution images. The server recognizes information such as the terrain, buildings, and roads in the image and stores it in a database. For example, information such as road width and building height is extracted pixel by pixel.

[1759] Map data generation

[1760] Map data is automatically generated based on the extracted geographic information. The server generates map data in standard GIS format based on the information stored in the database. For example, roads, buildings, parks, etc. are plotted on the map.

[1761] Recognizing user emotions with an emotion engine

[1762] When a user browses map data, the emotion engine recognizes the user's emotions. Specifically, the device acquires voice data and facial expression data and sends it to the server. The emotion engine on the server analyzes this data and identifies the user's emotions. For example, emotions are recognized from the tone and pronunciation of the user's voice commands.

[1763] Providing map data and responding to emotions

[1764] The server changes the content and display of the map data it provides based on the user's emotions. For example, if the user expresses impatience, the server will provide route guidance as quickly as possible. The server then returns the generated map data to the user's device, where the user can view and download it.

[1765] Specific examples

[1766] For example, if a user needs to get to a destination in Tokyo in a hurry, the process would be as follows:

[1767] 1. The user requests the latest map data for Tokyo via the device's web browser.

[1768] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[1769] 3. Extract geographic information using image recognition AI and store it in a database.

[1770] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[1771] 5. The user speaks to the device, saying "I'm in a hurry."

[1772] 6. The server's emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[1773] 7. The server generates and provides map data for quick route guidance based on the user's emotions.

[1774] 8. The user displays and uses the latest map data returned on their device and heads to their destination.

[1775] In this way, the system of the present invention recognizes the user's emotions and provides map data in response to them, thereby realizing prompt and appropriate support that meets the user's needs.

[1776] The processing flow will be explained below.

[1777] Step 1:

[1778] The server obtains satellite imagery for a specified area. The server sends a request to the API of an external satellite data provider, specifying the required dataset for the required area and date and time. For example, to download satellite imagery for the entire Tokyo metropolitan area for a specific date (October 1, 2023).

[1779] Step 2:

[1780] The server saves the downloaded satellite images in the internal storage. The acquired satellite images remain in low resolution, but are passed on to the next process for higher resolution.

[1781] Step 3:

[1782] The server inputs acquired satellite images into the generative AI model. The server converts the image data into an input format for the generative AI and performs appropriate preprocessing (e.g., shaping pixel information).

[1783] Step 4:

[1784] The server runs a generative AI to convert low-resolution images to high-resolution ones. The generative AI improves the image resolution and adds details. For example, it converts a 300dpi image to 600dpi, making the details of buildings and roads clearer.

[1785] Step 5:

[1786] The server saves the high-resolution image to its internal storage, ready to be passed on to the next processing step.

[1787] Step 6:

[1788] The server inputs the high-resolution image into the image recognition AI, which then converts the image data to fit the input format of the image recognition AI.

[1789] Step 7:

[1790] The server runs image recognition AI to identify and extract geographic information such as terrain, buildings, and roads. The image recognition AI uses pattern recognition technology to identify various geographic elements pixel by pixel and extract information, such as the width of roads, the height of buildings, and the flow of rivers.

[1791] Step 8:

[1792] The server saves the extracted geographic information in a database. The extracted information is stored in the database as structured data (e.g., GeoJSON format).

[1793] Step 9:

[1794] The server generates map data based on the geographic information stored in the database. The map data generation engine uses the stored information to visualize each geographic element and generate map data. For example, it creates a map by integrating elements such as roads, buildings, and parks.

[1795] Step 10:

[1796] The server exports the generated map data to a GIS format (e.g. Shapefile), saves the exported map data in a file format, and prepares it for distribution to users.

[1797] Step 11:

[1798] The user sends a request to the server using a device (e.g., PC or smartphone). The user accesses the API endpoint for map data using the device's web browser and requests the latest map data for the relevant area (e.g., "latest map of Tokyo").

[1799] Step 12:

[1800] The device acquires the user's emotional data. The device records the user's voice and captures facial expression data with a camera. For example, the user may say, "I'm in a hurry."

[1801] Step 13:

[1802] The device sends emotional data to the server, which then transfers recorded voice data and captured facial expression data to the server.

[1803] Step 14:

[1804] The server's emotion engine analyzes the emotion data and recognizes the user's emotion. The emotion engine analyzes voice tone and facial expression changes to identify the user's emotion. For example, it can recognize impatience from voice data.

[1805] Step 15:

[1806] The server adjusts the content and display of map data provided according to the user's emotions. Based on the recognized emotions (e.g., impatience), map data is generated that quickly highlights the shortest route to the destination.

[1807] Step 16:

[1808] The server returns the generated map data to the user's device, and the map data is provided to the user in an appropriate format (e.g., GIS format, image format).

[1809] Step 17:

[1810] The user can view and use the latest map data returned on their device. The user can check the map data and download it as needed or integrate it into their own application. For example, if the user is in a hurry, they can quickly check the shortest route to their destination.

[1811] Through these processing steps, the system of the present invention can provide the latest, high-quality map data that takes into account the user's feelings and quickly respond to the user's needs.

[1812] Example 2

[1813] 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."

[1814] Conventional map generation systems lack the functionality to provide optimal map data according to the user's emotions and circumstances, which can cause inconvenience to users. Furthermore, the low accuracy of the automatic generation and high-resolution map data can result in problems in which users' needs are not fully met. There is a demand for a system that can solve these issues and provide users with appropriate and prompt map data.

[1815] 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.

[1816] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, emotion recognition means for identifying a user's emotion, means for adjusting the map data based on the identified emotion, and communication means for providing the generated map data, thereby making it possible to provide customized map data that corresponds to the user's emotion and situation.

[1817] "Means for obtaining satellite imagery" means means for obtaining satellite imagery for a specified area and date and time using the API of a satellite data provider.

[1818] "Generative artificial intelligence means" refers to artificial intelligence technology for converting low-resolution satellite images into high-resolution images.

[1819] "Image recognition artificial intelligence means" is an artificial intelligence technology for extracting geographic information from high-resolution images.

[1820] The "means for automatically generating map data" is a means for generating map data in a standard geographic information system format based on the extracted geographic information.

[1821] "Emotion recognition means" is a technology for identifying emotions by analyzing the user's voice and facial expression data.

[1822] The "means for adjusting map data" is a means for changing the content or display of map data based on the identified emotion of the user.

[1823] "Communication means" refers to a communication technology for providing the generated map data to the user's terminal.

[1824] In this invention, a geographic information system is constructed using a system in which servers, terminals, and users each have specific roles. To implement this system, it is necessary to utilize multiple artificial intelligence technologies and communication methods. Below, we will explain how to specifically implement this invention.

[1825] Server Features

[1826] The server has the following main functions:

[1827] 1. Acquisition of satellite images: The server uses the API of a satellite data provider to acquire satellite images for the area and date and time specified by the user. Specifically, APIs such as DigitalGlobe and GeoEye can be used.

[1828] 2. High-resolution imagery: The server inputs the acquired low-resolution satellite images into a generative artificial intelligence (generative AI) model to increase the resolution. This generative AI uses technologies such as GANs (Generative Adversarial Networks).

[1829] 3. Extraction of geographic information: The server extracts geographic information from the high-resolution satellite images using image recognition AI (e.g., object detection models using TensorFlow or PyTorch). The geographic information is then stored in a database.

[1830] 4. Map data generation: The server automatically generates map data based on the extracted geographic information. This generation process uses GIS software such as QGIS or ArcGIS.

[1831] 5. Emotion Recognition: The server has an emotion recognition means (e.g., IBM Watson Tone Analyzer or Microsoft Azure Emotion API) to analyze the user's voice and facial expression data to identify emotions.

[1832] 6. Adjusting map data: The server adjusts the display and content of the map data based on the identified user's emotions.

[1833] 7. Communication: The server has a communication means to provide the generated map data to the user's terminal, and sends and receives data as appropriate.

[1834] Device Features

[1835] 1. User Interface: The device provides an interface for users to request and view map data. This is done through a web browser or dedicated application.

[1836] 2. Emotion input means: The device has a means to acquire the user's emotion data through voice recognition and facial expression recognition and send it to the server. This includes hardware such as a microphone and camera.

[1837] 3. Displaying and downloading map data: The terminal has the means to display map data provided by the server and to download it as necessary.

[1838] User Roles

[1839] 1. Request: The user requests map data for a specific region and date and time through the device interface.

[1840] 2. Emotional data input: The user inputs emotional data using voice and facial expressions and sends it to the server via the terminal.

[1841] 3. Use of map data: Users can view the provided map data and download and use it as needed.

[1842] Specific operation example

[1843] For example, if a user needs to get to a destination in Tokyo in a hurry, the process would be as follows:

[1844] 1. The user requests the latest map data for Tokyo via the device's web browser.

[1845] 2. The server obtains the latest satellite images of Tokyo from a satellite data provider and uses generative AI to enhance the resolution.

[1846] 3. Extract geographic information using image recognition AI and store it in a database.

[1847] 4. The server generates the latest map data for Tokyo based on the extracted information and exports it in GIS format.

[1848] 5. The user speaks to the device, saying "I'm in a hurry."

[1849] 6. The server's emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[1850] 7. The server generates and provides map data for quick route guidance based on the user's emotions.

[1851] 8. The user displays and uses the latest map data returned on their device and heads to their destination.

[1852] Prompt Sentence Examples

[1853] Below are some example prompts to be input to the generative AI model:

[1854] Prompt 1:

[1855] "Please increase the resolution of this satellite image. Convert it from 300 dpi to 600 dpi and add more detailed information."

[1856] Prompt Statement 2:

[1857] "Extract geographic information from this high-resolution imagery. Identify roads, buildings, and terrain information pixel by pixel and store it in a database."

[1858] Prompt statement 3:

[1859] "Analyze user emotions. Recognize user emotions from voice data and identify impatience and anxiety."

[1860] This system utilizes a variety of artificial intelligence technologies and communication methods to provide fast, high-quality map data that meets user needs.

[1861] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1862] Step 1:

[1863] 1. The server receives a request from the user for the specified location and date and time.

[1864] Input: Location and date / time request data from the user (e.g., Tokyo on October 1, 2023).

[1865] Behavior: The server analyzes the request data and prepares it to use the API of an external satellite data provider.

[1866] Output: API request data.

[1867] Step 2:

[1868] 1. The server sends a request to an API of an external satellite data provider to obtain satellite imagery for the specified area and date and time.

[1869] Input: API request data.

[1870] Operation: The server uses the acquired API request data to call the API of the satellite data provider and download the specified satellite imagery, for example, using APIs from DigitalGlobe or GeoEye.

[1871] Output: Low-resolution satellite image data.

[1872] Step 3:

[1873] 1. The low-resolution satellite images acquired by the server are input into a generative AI model to increase their resolution.

[1874] Input: Low-resolution satellite image data.

[1875] How it works: The server uses a generative AI model (e.g., GANs) to convert low-resolution satellite imagery to high-resolution. The server sends a prompt to the generative AI model, such as "Please resize this image from 300 dpi to 600 dpi," and receives the high-resolution image.

[1876] Output: High resolution satellite image data.

[1877] Step 4:

[1878] 1. The server inputs high-resolution satellite images into image recognition AI to extract geographic information.

[1879] Input: High-resolution satellite image data.

[1880] How it works: The server uses an image recognition AI model (e.g., an object detection model using TensorFlow or PyTorch) to extract geographic information (e.g., roads, buildings, terrain, etc.) from satellite images. The server then sends a prompt to the image recognition AI saying, "Please extract geographic information from this high-resolution image," and stores the extracted geographic information in a database.

[1881] Output: Geographical information data.

[1882] Step 5:

[1883] 1. The server automatically generates map data based on the extracted geographic information.

[1884] Input: Geographical data.

[1885] How it works: The server uses GIS software (e.g., QGIS or ArcGIS) to generate map data in a standard geographic information system format from the extracted geographic information, specifically plotting roads, buildings, parks, and other geographic information on a map.

[1886] Output: Auto-generated map data.

[1887] Step 6:

[1888] 1. The device collects the user's voice and facial expression data.

[1889] Input: User's voice and facial expression data.

[1890] How it works: The device uses a microphone and camera to capture the user's voice and facial expressions and collect them as data.

[1891] Output: Audio data and facial expression data.

[1892] Step 7:

[1893] 1. The device sends the collected voice and facial expression data to the server.

[1894] Input: speech and facial expression data.

[1895] Operation: The terminal performs communication processing to send the collected data to the server.

[1896] Output: Voice data and facial expression data are sent to the server.

[1897] Step 8:

[1898] 1. The server analyzes the user's emotions using emotion recognition means.

[1899] Input: speech and facial expression data.

[1900] How it works: The server uses an emotion recognition tool (e.g., IBM Watson Tone Analyzer or Microsoft Azure Emotion API) to analyze the user's emotions from the transmitted voice and facial expression data. It then sends a prompt to the emotion recognition tool saying, "Please analyze the user's emotions from this voice data."

[1901] Output: User emotion data.

[1902] Step 9:

[1903] 1. The server adjusts the content and display of map data based on the user's emotional data.

[1904] Input: Auto-generated map data and user emotion data.

[1905] How it works: The server uses the identified emotion data to adjust the display and content of map data, for example highlighting the shortest route to provide quick route guidance to users in a hurry.

[1906] Output: Adjusted map data.

[1907] Step 10:

[1908] 1. The server sends the adjusted map data to the user's device.

[1909] Input: Adjusted map data.

[1910] Operation: The server transmits the generated and adjusted map data to the user's terminal using a communication means.

[1911] Output: Map data sent back to the user's device.

[1912] Step 11:

[1913] 1. The user can view and download the provided map data.

[1914] Input: Map data sent to the user's device.

[1915] How it works: The user can view the returned map data via their device and download it as needed.

[1916] Output: Map data available to the user.

[1917] This series of processing steps makes it possible to provide high-resolution map data that is optimized according to the user's emotions and situation.

[1918] (Application example 2)

[1919] 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."

[1920] Conventional map generation systems simply provide map data without customizing it according to the user's emotions or situation. As a result, the same map data is provided whether the user is in a hurry or has other specific emotions, resulting in an unoptimized user experience. In particular, services such as food delivery require fast and efficient route guidance that is in line with the user's emotions, so a system that solves this problem is needed.

[1921] 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.

[1922] In this invention, the server includes means for acquiring satellite images, artificial intelligence generation means for increasing the resolution of the acquired satellite images, artificial intelligence image recognition means for extracting geographic information from the increased resolution images, means for automatically generating map data based on the extracted geographic information, communication means for providing the generated map data, an emotion engine for recognizing the user's emotions, and means for customizing the map data based on the recognized emotions, thereby enabling fast and efficient route guidance according to the user's emotions.

[1923] A "means for acquiring satellite imagery" is a device or system used to acquire satellite imagery of a particular area.

[1924] "Generative artificial intelligence means for converting acquired satellite imagery to high resolution" means a generative AI model or associated software used to convert acquired low-resolution satellite imagery to high resolution.

[1925] "Image recognition artificial intelligence means for extracting geographic information from high-resolution images" refers to an AI algorithm for recognizing and extracting geographic information such as roads and buildings from high-resolution images.

[1926] The "means for automatically generating map data based on extracted geographic information" refers to a system or tool that automatically generates map data using extracted geographic information.

[1927] The "communication means for providing the generated map data" refers to the network and communication infrastructure for providing the generated map data to the user.

[1928] An "emotion engine that recognizes user emotions" is software or hardware that analyzes voice data and facial expression data to identify the user's emotions.

[1929] A "means for customizing map data based on recognized emotions" is a system or algorithm that changes the content or display of map data in response to recognized user emotions.

[1930] The present invention is a system that combines a system that acquires satellite images, enhances their resolution, extracts geographic information, and automatically generates map data with an emotion engine that recognizes user emotions. Specific embodiments of the present invention are described below.

[1931] System configuration

[1932] This system consists of the following main components:

[1933] 1. Server

[1934] Means of acquiring satellite images

[1935] AI method for generating high-resolution satellite images

[1936] Image recognition AI method to extract geographic information from high-resolution images

[1937] A means of automatically generating map data based on extracted geographic information

[1938] Communication means for providing generated map data

[1939] Emotion engine that recognizes user emotions

[1940] A means to customize map data based on recognized emotions

[1941] 2. Terminal

[1942] User Interface

[1943] Emotion input means (voice recognition, facial expression recognition)

[1944] Viewing map data

[1945] Download map data

[1946] Delivery tracking feature

[1947] 3. Users

[1948] Map data request

[1949] Emotion data input (voice and facial expressions)

[1950] Use of map data

[1951] Program Processing and Data Flow

[1952] Acquisition of satellite images and their resolution enhancement

[1953] The server retrieves satellite images of a specific area using the API of an external satellite data provider. Since the retrieved images are often low-resolution, the server uses a generative AI model (for example, a model based on TensorFlow) to increase the resolution.

[1954] Extracting geographic information

[1955] From the generated high-resolution images, geographic information (e.g., roads and buildings) is extracted using image recognition AI tools. The high-resolution images serve as input data for AI algorithms to precisely extract detailed geographic information.

[1956] Map data generation

[1957] Based on the extracted geographic information, map data is automatically generated, which the server exports in a standard geographic information system (GIS) format and stores in a database.

[1958] The role of the emotional engine

[1959] When a user requests map data, the device sends emotional data to the server through voice and facial expressions. The server's emotion engine analyzes this data and recognizes, for example, the emotion of being in a hurry. Based on the recognition results, the server optimizes and provides the map data.

[1960] Specific examples

[1961] For example, in a food delivery scenario, if the user is in a hurry, the following process would occur:

[1962] 1. A user requests the latest route data for a specific area via a smartphone application.

[1963] 2. The server obtains the latest satellite images of the area from a satellite data provider and uses generative AI to enhance the resolution.

[1964] 3. Geographic information is extracted from the high-resolution images and map data is generated.

[1965] 4. The user utters "I'm in a hurry" via voice input.

[1966] 5. The emotion engine analyzes the user's voice data and recognizes the emotion of impatience.

[1967] 6. The server generates and provides map data for quick delivery route guidance based on the user's emotions.

[1968] 7. The user checks the map data provided on their smartphone and the meal is delivered via the optimal route.

[1969] Examples of prompt statements

[1970] "I'm in a hurry, I want my food to arrive quickly!"

[1971] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1972] Step 1:

[1973] A user requests the latest route data for a specific area via a terminal. The request includes area information and date and time information. The server receives this request and obtains satellite images of the requested area using the API of an external satellite data provider. The input is the requested area and date and time information, and the output is the obtained low-resolution satellite image.

[1974] Step 2:

[1975] The server uses a generative AI model (such as a model based on TensorFlow) to convert the acquired low-resolution satellite image into a high-resolution image. The generative AI model inputs the low-resolution image and outputs a high-resolution image. As part of data processing, the generative AI performs image completion, and the output is a high-resolution image.

[1976] Step 3:

[1977] The server uses image recognition AI tools to extract geographic information from high-resolution images. Image recognition AI takes high-resolution images as input and outputs geographic information (e.g., coordinates of roads and buildings). Feature extraction algorithms are used to process the data in this step.

[1978] Step 4:

[1979] The server automatically generates map data based on the extracted geographic information. The extracted geographic information is used as input and map data in GIS format is output. Geographic Information System (GIS) tools are used to calculate this data.

[1980] Step 5:

[1981] At the same time, the device collects the user's emotional data (voice and facial expressions) and sends it to the server. The emotional data is input through the device's built-in voice and facial expression sensors. The output is the user's voice and facial expression data.

[1982] Step 6:

[1983] The server's emotion engine analyzes the user's emotion data and recognizes specific emotions (e.g., impatience). The input is the user's emotion data, and the output is the analyzed emotion result. A machine learning model is used to calculate this data.

[1984] Step 7:

[1985] The server customizes the map data based on the recognized emotion. The map data with optimal route guidance according to the specific emotion is generated. The output is customized map data. A customization algorithm is used for data processing.

[1986] Step 8:

[1987] The generated customized map data is sent from the server to the terminal, which then provides the user with quick route guidance. The input is the customized map data, and the output is the map information displayed to the user.

[1988] Step 9:

[1989] The user checks the map data provided on the terminal and receives delivery according to the instructed route. The input is customized map data, and the output is behavior based on the optimal route.

[1990] Specific prompt examples

[1991] "I'm in a hurry, I want my food to arrive quickly!"

[1992] 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.

[1993] 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.

[1994] 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 robot 414.

[1995] 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.

[1996] FIG. 9 illustrates 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 behaviors 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.

[1997] 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.

[1998] 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).

[1999] 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.

[2000] 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."

[2001] 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.

[2002] 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).

[2003] 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.

[2004] 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.

[2005] 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.

[2006] 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.

[2007] 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.

[2008] 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.

[2009] 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.

[2010] 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.

[2011] 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.

[2012] 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.

[2013] The following is further disclosed regarding the above embodiment.

[2014] (Claim 1)

[2015] a means for acquiring satellite imagery;

[2016] A generating artificial intelligence means for increasing the resolution of the acquired satellite image;

[2017] an image recognition artificial intelligence means for extracting geographic information from high-resolution images;

[2018] A means for automatically generating map data based on the extracted geographic information;

[2019] a communication means for providing the generated map data;

[2020] A system including:

[2021] (Claim 2)

[2022] 10. The system of claim 1, wherein the acquired satellite imagery is obtained using remote sensing technology.

[2023] (Claim 3)

[2024] 10. The system of claim 1, wherein the generated map data is exported in a geographic information system format.

[2025] "Example 1"

[2026] (Claim 1)

[2027] a means for obtaining satellite imagery of a designated area;

[2028] A means for increasing the resolution of acquired satellite images using artificial intelligence;

[2029] A means for extracting geographic information from high-resolution satellite images using image recognition artificial intelligence;

[2030] means for automatically generating map data in a geographic information system format based on the extracted geographic information;

[2031] a communication means for providing the generated map data;

[2032] A system including:

[2033] (Claim 2)

[2034] 10. The system of claim 1, wherein the system processes satellite images acquired using remote sensing techniques.

[2035] (Claim 3)

[2036] 10. The system of claim 1, wherein the request for map data is received via a user interface.

[2037] "Application Example 1"

[2038] (Claim 1)

[2039] a means for acquiring satellite imagery;

[2040] A generating artificial intelligence means for increasing the resolution of the acquired satellite image;

[2041] an image recognition artificial intelligence means for extracting geographic information from high-resolution images;

[2042] A means for automatically generating map data based on the extracted geographic information;

[2043] a communication means for providing the generated map data;

[2044] a means for performing navigation in an autonomous vehicle using the provided map data;

[2045] A system including:

[2046] (Claim 2)

[2047] 10. The system of claim 1, wherein the acquired satellite imagery is obtained using remote sensing technology.

[2048] (Claim 3)

[2049] 10. The system of claim 1, wherein the generated map data is exported in a geographic information system format.

[2050] "Example 2: Combining Emotion Engines"

[2051] (Claim 1)

[2052] a means for acquiring satellite imagery;

[2053] A generating artificial intelligence means for increasing the resolution of the acquired satellite image;

[2054] an image recognition artificial intelligence means for extracting geographic information from high-resolution images;

[2055] A means for automatically generating map data based on the extracted geographic information;

[2056] emotion recognition means for identifying an emotion of a user;

[2057] means for adjusting map data based on the identified emotion;

[2058] a communication means for providing the generated map data;

[2059] A system including:

[2060] (Claim 2)

[2061] 10. The system of claim 1, wherein the acquired satellite imagery is obtained using remote sensing technology.

[2062] (Claim 3)

[2063] 10. The system of claim 1, wherein the generated map data is exported in a geographic information system format.

[2064] "Application example 2 when combining emotion engines"

[2065] (Claim 1)

[2066] a means for acquiring satellite imagery;

[2067] A generating artificial intelligence means for increasing the resolution of the acquired satellite image;

[2068] an image recognition artificial intelligence means for extracting geographic information from high-resolution images;

[2069] A means for automatically generating map data based on the extracted geographic information;

[2070] a communication means for providing the generated map data;

[2071] an emotion engine that recognizes the user's emotions;

[2072] means for customizing map data based on the recognized emotion;

[2073] A system including:

[2074] (Claim 2)

[2075] 10. The system of claim 1, wherein the acquired satellite imagery is obtained using remote sensing technology.

[2076] (Claim 3)

[2077] 10. The system of claim 1, wherein the generated map data is exported in a geographic information system format. [Explanation of symbols]

[2078] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for acquiring satellite imagery; A generating artificial intelligence means for increasing the resolution of the acquired satellite images; an image recognition artificial intelligence means for extracting geographic information from high-resolution images; A means for automatically generating map data based on the extracted geographic information; a communication means for providing the generated map data; A system including:

2. 10. The system of claim 1, wherein the acquired satellite imagery is acquired using remote sensing techniques.

3. 10. The system of claim 1, wherein the generated map data is exported in a geographic information system format.

Images