system

Abstract

To provide a system for efficiently providing information which a user requires.SOLUTION: A system comprises: reception means for receiving earth observation data sent from an earth observation satellite; analysis means including a generative AI for analyzing the received earth observation data; classification means for classifying the data analyzed by the analysis means to a prescribed category; integration means for integrating the classified data; generation means for generating real time information on the basis of the integrated data; and providing means for providing the generated information according to a search word.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 an explanation 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Earth observation data is vast, and it is difficult to analyze all of it in real time and provide the information required by users.

Means for Solving the Problems

[0005] The present invention receives earth observation data, analyzes, classifies, and integrates it by a generative AI to generate real-time information. Furthermore, by providing the information generated based on the search words from the user, it becomes possible to efficiently provide the information required by the user.

Brief Description of the Drawings

[0006] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] It is a sequence diagram showing the processing flow of the data processing system in Example 1 of Form Example 1 when combined with an emotion engine. [Figure 18] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] It is a sequence diagram showing the processing flow of the data processing system in Example 2 of Form Example 2 when combined with an emotion engine. [Figure 20] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] It is a sequence diagram showing the processing flow of the data processing system in Example 3 of Form Example 3 when combined with an emotion engine. [Figure 22] It is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0007] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the language used in the following description will be explained.

[0009] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)), etc.

[0010] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0011] In the following embodiments, the labeled 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), or magnetic tapes, etc.

[0012] In the following embodiments, the labeled communication I / F (Interface) 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), or Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0014] [First Embodiment]

[0015] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0016] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0019] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] One embodiment of the present invention is a system equipped with a receiving means for receiving Earth observation data transmitted from an Earth observation satellite. This receiving means is capable of receiving data from the satellite in real time.

[0029] "Example of form 2"

[0030] Next, there is an analysis method that includes a generative AI for analyzing the received Earth observation data. This generative AI uses AI technologies such as deep learning to analyze the Earth observation data. For example, it can detect climate change or natural disasters in specific regions from satellite images.

[0031] "Example of form 3"

[0032] Furthermore, there are classification means for classifying the analyzed data and integration means for integrating the classified data. Through these means, the analyzed data is appropriately classified and integrated, making it possible to efficiently provide the information that users need.

[0033] "Example of form 4"

[0034] Finally, there is a means of providing information that generates real-time data based on integrated data and provides information generated according to the user's search terms. For example, if a user searches for "Tokyo weather," the system generates information about Tokyo's weather from data analyzed, classified, and integrated by a generation AI, and provides it to the user.

[0035] The following describes the processing flow for each example of the form.

[0036] "Example of form 1"

[0037] Step 1: Receive Earth observation data transmitted from Earth observation satellites. This reception is performed in real time using specific receiving equipment.

[0038] "Example of form 2"

[0039] Step 1: The received Earth observation data is analyzed by a generating AI. This analysis is performed using AI technologies such as deep learning, making it possible to detect climate change and natural disasters in specific regions from satellite images.

[0040] "Example of form 3"

[0041] Step 1: Classify the analyzed data. This classification is performed using a specific classification algorithm.

[0042] Step 2: Integrate the classified data. This integration is performed using a specific integration algorithm, ensuring that the analyzed data is appropriately classified and integrated.

[0043] "Example of form 4"

[0044] Step 1: Generate real-time information based on integrated data. This generation is performed using a specific generation algorithm.

[0045] Step 2: Provide information generated in response to the user's search terms. For example, if a user searches for "Tokyo weather," the system generates information about Tokyo's weather from data analyzed, classified, and integrated by the generating AI, and provides it to the user.

[0046] (Example 1)

[0047] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0048] Conventional systems for receiving and analyzing Earth observation data were inefficient in real-time data reception and analysis, making it difficult to quickly provide users with the information they needed. Furthermore, the methods for storing and providing analyzed data were inadequate, resulting in delays for users accessing the data they required.

[0049] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means for analyzing the received Earth observation data, a storage means for storing the data analyzed by the analysis means, a providing means for providing the stored data, and an access means for users to access the data through the providing means. This makes it possible to receive data transmitted from Earth observation satellites in real time and to quickly store and provide the analyzed data.

[0050] "Receiving means" refers to devices or systems for receiving data transmitted from Earth observation satellites.

[0051] "Analysis means" refers to devices or systems used to analyze received Earth observation data and extract necessary information.

[0052] "Storage means" refers to devices or systems for saving analyzed data to a database or similar location.

[0053] "Means of provision" refers to devices or systems used to provide stored data to users or other systems.

[0054] "Access means" refers to devices or systems that allow users to access data stored through the provided means.

[0055] This invention relates to a system for receiving, analyzing, storing, and providing data transmitted from Earth observation satellites in real time. Specific embodiments of this system are described below.

[0056] The server uses a highly sensitive antenna and receiver to receive Earth observation data. This makes it possible to receive data transmitted from Earth observation satellites in real time. The received data is sent to the server via data receiving software.

[0057] Next, the server uses data analysis software to analyze the received data. This analysis process involves formatting, filtering, and extracting necessary information from the data. For example, in the case of weather data, information such as temperature, humidity, and wind speed is extracted.

[0058] The analyzed data is stored in a database on the server. When saving, metadata such as the data type and timestamp is also saved. This makes it easier to search for the data later.

[0059] The stored data is provided to users and other systems through the distribution channels. Users can access the API using their devices and retrieve the necessary data. For example, if a user wants to obtain weather data, they can send a request to the API and receive temperature, humidity, and wind speed data from the server.

[0060] As a concrete example, the process by which a server receives, analyzes, stores, and provides weather data transmitted from an Earth observation satellite is shown below.

[0061] Specific example: The server receives weather data transmitted from Earth observation satellites using a high-sensitivity antenna and receiver. The received data is sent to the server via data receiving software, and data analysis software extracts information such as temperature, humidity, and wind speed. The analyzed data is stored in a database, and users access the API using their terminals to retrieve the necessary data.

[0062] Example of a prompt:

[0063] Please create a program that receives weather data transmitted from Earth observation satellites in real time and extracts information such as temperature, humidity, and wind speed. The hardware to be used will be a high-sensitivity antenna and receiver, and the software will consist of data reception software and data analysis software. The analyzed data should be stored in a database and provided to users via an API.

[0064] In this way, a system can be built in which the server can efficiently receive, analyze, store, and provide data from Earth observation satellites.

[0065] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0066] Step 1: Prepare to receive data

[0067] The server installs and connects a high-sensitivity antenna and receiver to receive data from Earth observation satellites. Next, it starts the data receiving software and configures it to receive signals from the satellites. The input is the installation and connection of the high-sensitivity antenna and receiver, and the output is the startup and completion of the data receiving software configuration.

[0068] Step 2: Data Reception

[0069] The server uses a high-sensitivity antenna and receiver to receive data transmitted from Earth observation satellites in real time. The received data is transferred to the server via data receiving software. The input is data from Earth observation satellites, and the output is the received data. Specifically, the server adjusts the antenna and receiver to capture the optimal signal.

[0070] Step 3: Data Analysis

[0071] The server passes the received data to data analysis software and begins the analysis. This analysis process involves formatting, filtering, and extracting necessary information from the data. For example, in the case of weather data, information such as temperature, humidity, and wind speed is extracted. The input is the received data, and the output is the analyzed data. Specifically, the server runs the data analysis software and processes the data using various algorithms.

[0072] Step 4: Save Data

[0073] The server stores the analyzed data in a database. When saving, it also saves metadata such as the data type and timestamp. The input is the analyzed data, and the output is the data stored in the database. Specifically, the server connects to the database and inserts data using SQL queries.

[0074] Step 5: Data Provision

[0075] The server makes stored data accessible through an API to provide it to users and other systems. Users can access the API using their terminals and retrieve the necessary data. The input is the user's request, and the output is the provided data. Specifically, the server receives an API request, retrieves the necessary data from the database, and sends it back to the user.

[0076] In this way, the server will build a system that can efficiently receive, analyze, store, and provide data from Earth observation satellites.

[0077] (Application Example 1)

[0078] Next, we will describe Application Example 1 of Form 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."

[0079] There is a need for a system that can use real-time data from Earth observation satellites to quickly detect natural disasters and suspicious activity, and issue appropriate warnings to users. However, conventional systems have problems with inefficient processes from data reception to analysis and warning issuance, and lack real-time capabilities. As a result, users have been unable to receive information at the appropriate time, making it difficult to respond quickly.

[0080] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0081] In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, and a classification means for classifying the data analyzed by the analysis means. This makes it possible to efficiently receive and analyze real-time data from Earth observation satellites, quickly detect natural disasters and suspicious activities, and issue warnings to users.

[0082] "Earth observation data" refers to data transmitted from Earth observation satellites that includes information about the Earth's surface and atmosphere.

[0083] "Receiving means" refers to a device or system for receiving Earth observation data transmitted from Earth observation satellites in real time.

[0084] "Analysis means" refers to a device or system that includes a generating AI for analyzing received Earth observation data.

[0085] "Generative AI" is an artificial intelligence technology that analyzes received data to detect specific patterns or anomalies.

[0086] A "classification means" is a device or system for classifying data analyzed by an analysis means into a specific category.

[0087] An "integration means" is a device or system for integrating classified data and generating overall information.

[0088] "Generation means" refers to a device or system for generating real-time information based on integrated data.

[0089] "Means of provision" refers to a device or system for providing generated information to a user according to the search terms.

[0090] A "warning device" is a device or system for issuing a warning to a user based on generated information.

[0091] The system for carrying out this invention is for receiving and analyzing Earth observation data transmitted from Earth observation satellites in real time and issuing warnings to users. Specific embodiments of the system are described below.

[0092] System Configuration

[0093] The system consists of the following main components:

[0094] 1. Receiving means: A device or system that receives Earth observation data transmitted from Earth observation satellites in real time. Specifically, this involves using a dedicated receiving antenna or data receiving server.

[0095] 2. Analysis means: A device or system including a generative AI for analyzing received Earth observation data. The data is analyzed using generative AI models such as Python or TENSORFLOW®.

[0096] 3. Classification means: A device or system that classifies data analyzed by an analysis means into specific categories. This involves efficiently classifying data using a database management system (DBMS).

[0097] 4. Integration means: A device or system that integrates classified data and generates overall information. The integrated data is updated in real time.

[0098] 5. Generation means: A device or system that generates real-time information based on integrated data. The generated information is provided to the user.

[0099] 6. Means of delivery: A device or system that provides generated information to users according to their search terms. Information is provided through smartphone apps or web applications.

[0100] 7. Warning means: A device or system that issues a warning to the user based on the generated information. Warnings are issued using push notifications or email notifications.

[0101] Program processing

[0102] The server receives Earth observation data and analyzes it using a generative AI. The analyzed data is categorized and integrated into specific categories. Real-time information is generated based on the integrated data and provided to the user. Furthermore, warnings are issued to the user based on the generated information.

[0103] Hardware and software to be used

[0104] Hardware: Receiving antenna, data receiving server, smartphone

[0105] Software: Python, TensorFlow, Database Management Systems (DBMS), Smartphone Apps, Web Applications

[0106] Specific example

[0107] For example, data transmitted from Earth observation satellites is received, and a generative AI model is used to detect natural disasters (e.g., earthquakes, floods) and suspicious activities (e.g., forest fires, poaching). The detected information is provided to the user in real time, and warnings are issued as needed.

[0108] Examples of prompts for generative AI models

[0109] Create a Python program that analyzes real-time data from Earth observation satellites to detect natural disasters and suspicious activity. The program should retrieve data via an API and include a function to alert the user based on the analysis results.

[0110] In this way, the system can efficiently utilize Earth observation data and provide users with timely and appropriate information and warnings.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server receives Earth observation data transmitted from Earth observation satellites. Using receiving equipment, it acquires data through dedicated receiving antennas and data receiving servers. The input is data from Earth observation satellites, and the output is the received raw data.

[0114] Step 2:

[0115] The server analyzes received Earth observation data using a generating AI model. It uses Python and TensorFlow as analysis tools to detect patterns and anomalies in the data. The input is the received raw data, and the output is the analyzed data. Specific operations include data preprocessing, feature extraction, model input, and retrieval of analysis results.

[0116] Step 3:

[0117] The server classifies the analyzed data into specific categories. A database management system (DBMS) is used as the classification method to efficiently classify the analysis results. The input is the analyzed data, and the output is the classified data. Specific operations include inserting data into the database and classifying data through queries.

[0118] Step 4:

[0119] The server integrates classified data to generate overall information. Using integration methods, it updates data in real time and generates integrated information. The input is classified data, and the output is integrated data. Specific operations include data aggregation, statistical processing, and real-time updates.

[0120] Step 5:

[0121] The server generates real-time information based on integrated data. It uses generation methods to produce information for the user. The input is integrated data, and the output is generated information. Specific operations include data format conversion and information generation.

[0122] Step 6:

[0123] The server provides generated information to the user based on the search terms. It uses smartphone apps and web applications to deliver the information to the user. The input consists of the generated information and the user's search terms, while the output is the information provided to the user. Specific operations include receiving search terms, filtering information, and providing the information.

[0124] Step 7:

[0125] The server issues a warning to the user based on the generated information. It uses push notifications and email notifications as warning methods. The input is the generated information, and the output is the warning issued to the user. Specific operations include checking warning conditions, generating notifications, and sending notifications.

[0126] (Example 2)

[0127] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0128] Conventional Earth observation data analysis systems often perform preprocessing and analysis of received data manually, resulting in inefficiency. Furthermore, the classification and integration of analysis results are insufficient, making real-time information provision difficult. Additionally, information based on user search terms may not be adequately provided.

[0129] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, a preprocessing means for preprocessing the received Earth observation data, an analysis means including a generation AI for analyzing the preprocessed data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, and a providing means for providing the generated information according to search words. This enables efficient preprocessing, analysis, classification, and integration of Earth observation data, and realizes real-time information provision. It also enables the provision of appropriate information based on search words from the user.

[0130] "Receiving means" refers to devices or functions for receiving Earth observation data from satellites or other data sources.

[0131] "Preprocessing means" refers to devices or functions that perform processing to convert received Earth observation data into a format suitable for analysis.

[0132] "Analysis means" refers to devices or functions, including generative AI, for analyzing pre-processed data.

[0133] "Generative AI" is artificial intelligence that uses deep learning technology to analyze Earth observation data and detect climate change and natural disasters in specific regions.

[0134] A "classification means" is a device or function that classifies data analyzed by an analysis means into specific categories or patterns.

[0135] An "integration tool" is a device or function that integrates classified data and generates overall information.

[0136] "Generation means" refers to devices or functions that generate real-time information based on integrated data.

[0137] "Means of provision" refers to devices or functions that provide generated information according to the user's search terms.

[0138] A "search term" is a keyword or phrase that a user enters to search for specific information.

[0139] Modes for carrying out the invention

[0140] This invention relates to a system for efficiently analyzing Earth observation data and providing real-time information. Specific embodiments of this system are described below.

[0141] 1. System Configuration

[0142] This system includes the following main means:

[0143] Receiving method: Earth observation data is received from satellites and other data sources.

[0144] Preprocessing means: Convert the received Earth observation data into a format suitable for analysis.

[0145] Analysis means: Includes a generative AI for analyzing preprocessed data.

[0146] Classification method: Data analyzed by the analysis method is classified into specific categories or patterns.

[0147] Integration method: Integrates classified data to generate overall information.

[0148] Generation method: Generates real-time information based on integrated data.

[0149] Delivery method: Generated information is provided according to the user's search terms.

[0150] 2. Hardware and software to be used

[0151] Server: A server with high-performance computing capabilities is used to receive, preprocess, analyze, classify, integrate, generate, and deliver data.

[0152] Generative AI Model: We use a generative AI model based on deep learning technology to analyze Earth observation data.

[0153] Database: Use a database to store analysis results and integrated data.

[0154] User terminal: The user uses a device (PC, smartphone, etc.) to enter search terms and view the analysis results.

[0155] 3. Specific examples

[0156] If a user wants to analyze climate change in a specific region, they should follow these steps.

[0157] 1. Data reception:

[0158] The user uploads satellite image data of the region they want to analyze to the server.

[0159] The server receives the uploaded data.

[0160] 2. Data preprocessing:

[0161] The server removes noise from the received image data and adjusts the resolution.

[0162] The server imputes missing values ​​and normalizes numerical data.

[0163] 3. Application of Generative AI Models:

[0164] The server inputs the pre-processed data into the generating AI model.

[0165] Generative AI models analyze data and detect climate change patterns in specific regions.

[0166] 4. Output of analysis results:

[0167] The server outputs the analysis results obtained from the generated AI model.

[0168] The user reviews the analysis results provided by the server and takes necessary countermeasures.

[0169] Example of a prompt

[0170] "To analyze climate change in a specific region, please use the following satellite imagery data. Preprocess the data and apply a generative AI model to detect climate change patterns."

[0171] In this way, the server, terminals, and users each play their respective roles, resulting in a system that enables efficient analysis of Earth observation data and real-time information provision.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1: Data Received

[0174] The server receives Earth observation data from satellites and other data sources. Inputs include satellite image data and numerical data. Outputs are the received raw data.

[0175] Specific operation: The server receives data transmitted from the satellite and stores it in storage.

[0176] Step 2: Data Preprocessing

[0177] The server preprocesses the received Earth observation data. The input is the received raw data. The output is the preprocessed data.

[0178] Specific actions:

[0179] For image data: The server applies a noise reduction filter and adjusts the resolution.

[0180] For numerical data: The server imputes missing values ​​and normalizes the data.

[0181] Step 3: Applying the Generative AI Model

[0182] The server inputs pre-processed data into the generating AI model. The input is the pre-processed data. The output is the analysis result.

[0183] Specific operation: The server inputs pre-processed data into a generating AI model, which then analyzes the data to detect patterns in climate change and natural disasters.

[0184] Step 4: Data Classification

[0185] The server classifies the analysis results obtained from the generated AI model. The input is the analysis results. The output is the classified data.

[0186] Specific operation: The server applies an algorithm to classify the analysis results into specific categories or patterns.

[0187] Step 5: Data Integration

[0188] The server integrates the classified data. The input is the classified data. The output is the integrated data.

[0189] Specific operation: The server integrates the classified data and generates overall information.

[0190] Step 6: Information Generation

[0191] The server generates real-time information based on integrated data. The input is the integrated data. The output is the generated real-time information.

[0192] Specific operation: The server analyzes the integrated data and applies algorithms to generate real-time information.

[0193] Step 7: Information Provision

[0194] The server provides generated information according to the user's search terms. The input consists of the user's search terms and the generated real-time information. The output is the information provided to the user.

[0195] Specific operation: The server receives search terms from the user, extracts appropriate information based on those terms, and provides it to the user's terminal.

[0196] In this way, the server, terminals, and users each play their respective roles, resulting in a system that enables efficient analysis of Earth observation data and real-time information provision.

[0197] (Application Example 2)

[0198] Next, we will describe Application Example 2 of Form 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."

[0199] Conventional Earth observation data analysis systems have struggled to predict climate change and natural disasters in real time and notify users quickly. Furthermore, they lacked the functionality to provide users with the information they needed based on search terms, making information retrieval cumbersome. Therefore, in today's world, where rapid responses to climate change and natural disasters are essential, a more efficient and rapid information delivery system is needed.

[0200] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, a prediction means for receiving satellite image data and predicting the occurrence of climate change and natural disasters, and a notification means for notifying the user of the predicted information in real time. This makes it possible to quickly predict the occurrence of climate change and natural disasters and notify the user in real time.

[0201] "Earth observation data" refers to data collected to observe the conditions of the Earth's surface, atmosphere, oceans, and other natural features.

[0202] "Receiving means" refers to devices or systems for receiving Earth observation data.

[0203] "Generative AI" refers to artificial intelligence models that use artificial intelligence technologies such as deep learning to analyze data and generate new information.

[0204] "Analysis means" refers to devices or systems used to analyze received Earth observation data.

[0205] A "classification tool" is a device or system used to classify analyzed data into specific categories.

[0206] An "integration tool" is a device or system for integrating classified data and generating overall information.

[0207] "Generation means" refers to devices or systems for generating real-time information based on integrated data.

[0208] "Means of provision" refers to devices or systems that provide generated information according to the user's search terms.

[0209] "Satellite image data" refers to image data of the Earth taken from a satellite.

[0210] "Prediction tools" refer to devices and systems that analyze satellite image data to predict the occurrence of climate change and natural disasters.

[0211] A "notification means" is a device or system for notifying users of predicted information in real time.

[0212] A system for carrying out this invention includes a series of processes for receiving, analyzing, classifying, and integrating Earth observation data, generating real-time information, and providing it to the user. Specific embodiments of this system are described below.

[0213] First, the server receives image data transmitted from the satellite using a receiving means for receiving Earth observation data. This receiving means includes a communication module for acquiring data via an internet connection.

[0214] Next, the received Earth observation data is analyzed by an analysis tool that includes generative AI. This analysis tool includes generative AI models using deep learning frameworks such as TensorFlow and Keras. The generative AI model takes satellite image data as input and predicts the occurrence of climate change and natural disasters.

[0215] The analyzed data is classified into specific categories using a classification method. For example, it may be classified into data related to climate change or data related to natural disasters. This classification method is implemented using a database management system (DBMS).

[0216] The classified data is integrated by an integration mechanism to generate overall information. This integration mechanism is implemented using data integration tools or scripts.

[0217] Based on integrated data, the generation mechanism generates real-time information. This generation mechanism then generates information to be provided to the user based on the data analysis results.

[0218] The generated information is provided according to the user's search terms through a delivery method. This delivery method provides the information to the user via a web server or mobile application.

[0219] Furthermore, the system includes a prediction method that receives satellite image data and predicts the occurrence of climate change and natural disasters. This prediction method uses a generative AI model to predict the occurrence of climate change and natural disasters from satellite image data.

[0220] The predicted information will be notified to the user in real time via notification methods. These notification methods will be implemented using smartphone push notification and email notification functions.

[0221] As a concrete example, consider a scenario where a user launches an application on their smartphone, acquires the latest satellite imagery, and analyzes it. An example of a prompt to be input to the generative AI model is as follows:

[0222] "Analyze the latest satellite imagery to predict climate change and natural disaster occurrences in specific regions."

[0223] By inputting this prompt into the generating AI model, the model analyzes satellite images and outputs results that predict the occurrence of climate change and natural disasters.

[0224] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0225] Step 1:

[0226] The server receives Earth observation data. Specifically, it acquires image data transmitted from satellites via an internet connection. The input is image data from satellites, and the output is the received image data.

[0227] Step 2:

[0228] The server analyzes received Earth observation data using analytical methods, including generative AI. Specifically, it inputs image data into generative AI models using deep learning frameworks such as TensorFlow and Keras to predict the occurrence of climate change and natural disasters. The input is the received image data, and the output is the analysis results.

[0229] Step 3:

[0230] The server classifies the analyzed data using classification methods. Specifically, it uses a database management system (DBMS) to classify data into categories such as climate change data and natural disaster data. The input is the analysis results, and the output is the classified data.

[0231] Step 4:

[0232] The server integrates the classified data using integration tools. Specifically, it generates overall information using data integration tools and scripts. The input is classified data, and the output is integrated data.

[0233] Step 5:

[0234] The server generates real-time information using a generation mechanism based on integrated data. Specifically, it generates information to provide to the user based on the results of data analysis. The input is integrated data, and the output is the generated information.

[0235] Step 6:

[0236] The server provides the generated information to the user according to their search terms through various means. Specifically, it provides information to the user through a web server or mobile application. The input is the generated information and the user's search terms, and the output is the information provided to the user.

[0237] Step 7:

[0238] The server receives satellite image data and uses prediction tools to forecast the occurrence of climate change and natural disasters. Specifically, it uses a generative AI model to predict the occurrence of climate change and natural disasters from satellite image data. The input is satellite image data, and the output is the prediction result.

[0239] Step 8:

[0240] The server notifies the user in real time of the predicted information via notification methods. Specifically, this is implemented using smartphone push notification and email notification functions. The input is the prediction result, and the output is the information notified to the user.

[0241] (Example 3)

[0242] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0243] Conventional Earth observation data analysis systems often involved manual data analysis, classification, and integration, making real-time information provision difficult. Furthermore, the inability to quickly provide information based on user search terms meant users couldn't obtain the information they needed in a timely manner. This resulted in reduced user convenience and diminished the usefulness of the information.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0245] In this invention, the server includes receiving means for receiving Earth observation data, analysis means including a generative AI model for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, and provision means for providing the generated information according to search terms. This automates data analysis, classification, and integration, enabling real-time information provision. Furthermore, it allows for the rapid provision of information according to the user's search terms, improving user convenience.

[0246] "Receiving means" refers to a device or function for receiving Earth observation data.

[0247] "Analysis means" refers to a device or function that includes a generative AI model for analyzing received Earth observation data.

[0248] A "generative AI model" is an artificial intelligence model used for natural language processing and data analysis.

[0249] "Classification means" refers to a device or function that classifies data analyzed by an analysis means into a specific category.

[0250] An "integration means" is a device or function that integrates classified data into a single set of information.

[0251] "Generation means" refers to a device or function that generates real-time information based on integrated data.

[0252] "Means of provision" refers to a device or function that provides generated information according to the user's search terms.

[0253] "Search terms" are keywords or phrases that users enter when searching for information.

[0254] "Real-time information" refers to information generated based on the current situation and the latest data.

[0255] This invention is a system that efficiently analyzes Earth observation data and provides real-time information tailored to the user's search terms. This system operates through the coordinated efforts of a server, a terminal, and the user.

[0256] First, the user enters a search term using a device (for example, a smartphone or computer). For example, they might enter "Tokyo weather." The device then sends this search term to the server.

[0257] The server receives Earth observation data using a receiving means. The received data is analyzed by an analysis means. This analysis uses a generative AI model (for example, a model using natural language processing technology). Specifically, a general natural language processing model can be used as the generative AI model.

[0258] The analyzed data is classified into specific categories using classification methods. For example, weather data is classified into categories such as "temperature," "probability of precipitation," and "wind speed." Machine learning algorithms (e.g., k-means clustering) are used for this classification.

[0259] The classified data is integrated into a single information set using integration tools. Database management systems (e.g., MySQL® or Amazon Redshift) are used for this integration. This brings together related data into a single information set.

[0260] Based on integrated data, the generation mechanism generates real-time information. A generation AI model is used for this generation. For example, if a user searches for "Tokyo weather," the server generates the latest weather information for Tokyo based on the integrated weather data.

[0261] The generated information is provided to the user using the provided means. The user can check the latest weather information for Tokyo on their device screen. An example of a specific prompt message would be, "The user searched for 'Tokyo weather'. Please provide the latest weather information for Tokyo based on this search term."

[0262] This system allows users to efficiently obtain the information they need. Data analysis, classification, and integration are automated, enabling real-time information delivery. Furthermore, it can quickly provide information tailored to the user's search terms, improving user convenience. The specific processing flow in Example 3 will be explained using Figure 15.

[0263] Step 1:

[0264] The user enters a search term.

[0265] The user enters a search term using a terminal. For example, they might enter "Tokyo weather." This input data is sent from the terminal to the server. The input data is the search term, and the output is the search term sent to the server.

[0266] Step 2:

[0267] The server receives the data.

[0268] The server receives search terms sent from the terminal. Using the receiving mechanism, it also simultaneously receives Earth observation data. The input data consists of the search terms and Earth observation data, while the output is the data passed to the analysis mechanism.

[0269] Step 3:

[0270] The server analyzes the data.

[0271] The server analyzes the received Earth observation data using an analysis tool. A generative AI model is used for this analysis. Specifically, the generative AI model is prompted with a search term and extracts relevant data. The input data consists of Earth observation data and the search term, and the output is the analyzed data. As a concrete example, the server inputs the following prompt to the generative AI model: "The user searched for 'Tokyo weather.' Based on this search term, please provide the latest weather information for Tokyo."

[0272] Step 4:

[0273] The server classifies the data.

[0274] The server classifies the data analyzed by the analysis tools using classification tools. For example, it classifies weather data into categories such as "temperature," "probability of precipitation," and "wind speed." Machine learning algorithms are used for this classification. The input data is the analyzed data, and the output is the classified data. Specifically, the server stores the weather data separated into each category.

[0275] Step 5:

[0276] The server integrates the data.

[0277] The server integrates the classified data using an integration means. For example, using a database management system, the data of each category is grouped as one information set. The input data is the classified data, and the output is the integrated data. As a specific operation, the server executes the following SQL query: "SELECT FROM weather_data WHERE location = 'Tokyo';".

[0278] Step 6:

[0279] The server generates real-time information

[0280] The server generates real-time information based on the integrated data. A generation AI model is used for this generation. The input data is the integrated data, and the output is the generated real-time information. As a specific operation, the server inputs the following prompt sentence to the generation AI model: "Please generate the latest weather information in Tokyo based on the integrated weather data."

[0281] Step 7:

[0282] The server provides information to the user

[0283] The server provides the generated information to the user using a providing means. The user can check the latest weather information in Tokyo on the screen of the terminal. The input data is the generated real-time information, and the output is the information provided to the user. As a specific operation, the server sends the generated information to the terminal in HTML format and displays it on the user's browser.

[0284] (Application Example 3)

[0285] Next, Application Example 3 of Embodiment Example 3 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart device 14 is referred to as a "terminal".

[0286] Conventional information provision systems struggle to provide users with searched information in real time, and there is a particular need to efficiently analyze, classify, and integrate large amounts of complex data, such as Earth observation data. Furthermore, there is a lack of technology to quickly generate and provide appropriate information in response to user search queries. As a result, it is not possible to provide users with the information they need in a timely manner, leading to problems with the accuracy and reliability of the information.

[0287] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0288] In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, a generating AI model for generating appropriate information based on the user's search query, and a prompt input means for inputting prompt sentences to the generating AI model to generate information. This makes it possible to provide the information searched by the user in real time, improving the accuracy and reliability of the information.

[0289] "Earth observation data" refers to data collected to observe various phenomena and environmental conditions on Earth.

[0290] "Receiving means" refers to devices or systems for receiving data from an external source.

[0291] "Analysis means" refers to devices or systems used to analyze received data.

[0292] "Generative AI" refers to a system that uses artificial intelligence technology to generate data.

[0293] A "classification tool" is a device or system used to classify analyzed data into specific categories.

[0294] An "integration tool" is a device or system used to combine classified data into a single entity.

[0295] A "generation means" is a device or system for generating new information based on integrated data.

[0296] "Means of provision" refers to devices or systems used to provide generated information to users.

[0297] A "generative AI model" is a model that uses artificial intelligence technology to generate appropriate information based on a user's search query.

[0298] A "prompt input means" is a device or system for inputting prompt text into a generative AI model to generate information.

[0299] A system for carrying out this invention has the following configuration: The server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, a generating AI model for generating appropriate information based on the user's search query, and a prompt input means for inputting prompt sentences to the generating AI model to generate information.

[0300] Hardware and software to use

[0301] Hardware: Servers, user terminals (smartphones, smart glasses)

[0302] Software: Generative AI models (e.g., GPT-4 (registered trademark)), data analysis tools (e.g., Pandas), cloud databases (e.g., Firebase)

[0303] Data processing and data calculation

[0304] 1. Data collection: The server collects real-time data from the cloud database. For example, it obtains data from weather information APIs and news APIs.

[0305] 2. Data analysis: The server analyzes the collected data using Pandas and extracts the necessary information.

[0306] 3. Data classification: The server classifies the analyzed data using a generative AI model (GPT-4). For example, it classifies the data into weather information, news, traffic information, etc.

[0307] 4. Data integration: The server integrates the classified data and generates appropriate information according to the query searched by the user.

[0308] 5. Information provision: The server generates real-time information based on the integrated data and provides it to the user terminal.

[0309] Specific example

[0310] For example, when a user searches for "Weather in Tokyo" on a smartphone, the system operates as follows.

[0311] 1. Data collection: The server obtains the latest weather data of Tokyo from the weather information API.

[0312] 2. Data analysis: The server analyzes the obtained data using Pandas and extracts the necessary information.

[0313] 3. Data Classification: The server classifies the analyzed data as weather information using a generating AI model (GPT-4).

[0314] 4. Data Integration: The server integrates categorized weather information and generates relevant information in response to the user's search queries.

[0315] 5. Information Provision: The server provides the user's terminal with the latest weather information for Tokyo based on integrated data.

[0316] Example of a prompt

[0317] User's search query: "Tokyo weather"

[0318] Prompt to the generating AI model: "Please tell me the latest weather information for Tokyo."

[0319] When this prompt is input into the generating AI model, the model generates the latest weather information for Tokyo from the analyzed, classified, and integrated data and provides it to the user.

[0320] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0321] Step 1:

[0322] The server collects real-time data from a cloud database. Specifically, it retrieves data from weather information APIs and news APIs. The input for this step is data from the APIs, and the output is the retrieved raw data.

[0323] Step 2:

[0324] The server analyzes the collected data using Pandas and extracts the necessary information. Specifically, it cleans and preprocesses the data and converts it into a format that can be analyzed. The input for this step is the raw data obtained in step 1, and the output is the analyzed data.

[0325] Step 3:

[0326] The server classifies the analyzed data using a generative AI model (GPT-4). Specifically, it classifies the data into categories such as weather information, news, and traffic information. The input for this step is the data analyzed in step 2, and the output is the classified data.

[0327] Step 4:

[0328] The server integrates the classified data and generates appropriate information in response to the user's search query. Specifically, it combines related data and generates information corresponding to the user's search query. The input for this step is the data classified in step 3 and the user's search query, and the output is the integrated information.

[0329] Step 5:

[0330] The server generates real-time information based on the integrated data and provides it to the user terminal. Specifically, it inputs prompt messages into the generation AI model and provides the generated information to the user. The input for this step is the information integrated in step 4 and the prompt messages, and the output is the final information provided to the user.

[0331] Specific example

[0332] User's search query: "Tokyo weather"

[0333] Prompt to the generating AI model: "Please tell me the latest weather information for Tokyo."

[0334] When this prompt is input into the generating AI model, the model generates the latest weather information for Tokyo from the analyzed, classified, and integrated data and provides it to the user.

[0335] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0336] "Example of form 1"

[0337] In one embodiment of the present invention, the system includes receiving means for receiving Earth observation data, analysis means including a generating AI for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, and providing means for providing the generated information according to search words. Furthermore, it also includes an emotion engine for recognizing the user's emotions.

[0338] "Example of form 2"

[0339] The emotion engine recognizes emotions from, for example, the user's tone of voice, facial expressions, and text input. This emotion engine then adjusts the information generated based on the user's emotions. For example, if the user is expressing joy, the system will prioritize generating positive information to match that emotion.

[0340] "Example of form 3"

[0341] The delivery method provides users with information adjusted by an emotion engine. For example, if a user is expressing sadness, the system will respond to that emotion by providing comforting words or cheerful news. This enables the delivery of information that is tailored to the user's emotions.

[0342] The following describes the processing flow for each example of the form.

[0343] "Example of form 1"

[0344] Step 1: The system receives Earth observation data.

[0345] Step 2: Analyze the received Earth observation data using a generative AI.

[0346] Step 3: Classify the analyzed data.

[0347] Step 4: Integrate the classified data.

[0348] Step 5: Generate real-time information based on integrated data.

[0349] Step 6: Provide the generated information according to the search terms.

[0350] Step 7: Recognize the user's emotions using the emotion engine.

[0351] "Example of form 2"

[0352] Step 1: The emotion engine recognizes emotions from the user's tone of voice, facial expressions, and text input.

[0353] Step 2: The emotion engine adjusts the information generated based on the user's emotions.

[0354] Step 3: If the user is expressing feelings of joy, the system prioritizes generating positive information that aligns with those feelings.

[0355] "Example of form 3"

[0356] Step 1: Provide users with information adjusted by the emotion engine.

[0357] Step 2: If the user is showing sadness, the system responds to that emotion by providing, for example, words of comfort or happy news.

[0358] (Example 1)

[0359] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0360] Conventional Earth observation data analysis systems have difficulty providing real-time data analysis and information, and they have not been able to provide information that is tailored to the user's emotions. Therefore, in situations where rapid and appropriate information provision is required, they have been unable to provide information that meets the user's needs.

[0361] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0362] In this invention, the server includes receiving means for receiving Earth observation data, analysis means including a generative AI model for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, provision means for providing the generated information according to search words, and an emotion engine for recognizing the user's emotions. This enables real-time data analysis and information provision, and further enables the provision of appropriate information according to the user's emotions.

[0363] "Earth observation data" refers to data about the Earth's surface and atmosphere obtained from Earth observation satellites.

[0364] "Receiving means" refers to devices and software for receiving Earth observation data transmitted from Earth observation satellites in real time.

[0365] A "generative AI model" is an artificial intelligence model used to analyze received Earth observation data, performing pattern recognition and anomaly detection on the data.

[0366] "Analysis means" refers to devices and software used to analyze Earth observation data received using a generated AI model.

[0367] A "classification means" is a device or software used to classify data analyzed by an analysis means into different categories.

[0368] An "integration tool" is a device or software used to integrate classified data and combine it into a single dataset.

[0369] "Generation means" refers to devices or software for generating real-time information based on integrated data.

[0370] "Means of provision" refers to devices or software that provide generated information to users according to their search terms.

[0371] An "emotion engine" is a device or software that recognizes a user's emotions and adjusts the information it provides accordingly.

[0372] Modes for carrying out the invention

[0373] This invention is a system that receives, analyzes, classifies, integrates, generates, and provides Earth observation data transmitted from Earth observation satellites in real time. Furthermore, it also has a function to recognize the user's emotions and adjust the information provided.

[0374] Receiving means

[0375] The server receives Earth observation data transmitted from Earth observation satellites in real time. Specifically, the server acquires data using a high-performance antenna and dedicated receiving software (e.g., SatReceiver Pro). For example, the server receives 10 MB of data per second and temporarily stores it in storage.

[0376] Analysis means

[0377] The server uses a generative AI model (e.g., GPT-4) to analyze the received Earth observation data. First, the server preprocesses the data to remove noise. Then, it uses the generative AI model to perform pattern recognition and anomaly detection on the data. For example, the server analyzes satellite image data to detect signs of forest fires.

[0378] Classification means

[0379] The server classifies the analyzed data. Specifically, the server uses machine learning algorithms (e.g., Scikit-learn) to classify the data into different categories (e.g., weather data, topographic data, environmental data). For example, the server classifies weather data into subcategories such as temperature, humidity, and wind speed.

[0380] Integration means

[0381] The server integrates the classified data. For example, the server combines weather data and topographic data to predict flood risk. A database management system (e.g., PostgreSQL) is used for this integration. The server stores the integrated data as a single dataset and uses it for subsequent processing.

[0382] generation means

[0383] The server generates real-time information based on integrated data. For example, the server generates warning messages based on flood risk predictions. This generation uses natural language generation tools (e.g., OpenAI's GPT-4). The server temporarily stores the generated information in a cache and prepares it for immediate provision.

[0384] Providing means

[0385] The server provides generated information based on the search terms. The user enters the search terms using their terminal, and the server provides relevant information based on those terms. For example, if a user searches for "flood risk," the server provides the latest flood risk information. The server sends the search results to the user's terminal, and the user views them.

[0386] Emotional Engine

[0387] The server uses an emotion engine (e.g., Affectiva) to recognize the user's emotions. When a user searches for information through their device, the server analyzes the user's emotions from their facial expressions and voice, and adjusts the information provided accordingly. For example, if the user has an anxious expression, the server will provide more detailed explanations or reassuring information. The server analyzes emotion data in real time and reflects this in the information provided.

[0388] Specific examples and prompt statements

[0389] Specific example

[0390] If a user wants to know about flood risk, they can obtain the information by following these steps:

[0391] 1. The user uses their device to search for "flood risk".

[0392] 2. The server analyzes Earth observation data and predicts flood risk.

[0393] 3. The server classifies the analysis results and integrates the weather data and topographic data.

[0394] 4. The server generates flood risk alert messages based on the integrated data.

[0395] 5. The server analyzes the user's emotions and adjusts the information as needed.

[0396] 6. The server provides the final information to the user.

[0397] Example of a prompt

[0398] "Please provide the latest information on flood risk."

[0399] By inputting this prompt into the AI ​​model, the server generates and provides real-time flood risk information to the user.

[0400] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0401] Step 1:

[0402] The server receives Earth observation data transmitted from Earth observation satellites. Specifically, the server acquires data using a high-performance antenna and dedicated receiving software. The input is data from Earth observation satellites, and the output is raw data temporarily stored in storage. For example, the server receives 10 MB of data per second and temporarily stores it in storage.

[0403] Step 2:

[0404] The server uses a generative AI model to analyze the received Earth observation data. The input is the raw data received in step 1, and the output is the analyzed data. Specifically, the server first preprocesses the data to remove noise. Then, it uses the generative AI model to perform pattern recognition and anomaly detection on the data. For example, the server analyzes satellite image data to detect signs of forest fires.

[0405] Step 3:

[0406] The server classifies the analyzed data. The input is the data analyzed in step 2, and the output is the classified data. Specifically, the server uses machine learning algorithms to classify the data into different categories. For example, the server classifies weather data into subcategories such as temperature, humidity, and wind speed.

[0407] Step 4:

[0408] The server integrates the classified data. The input is the data classified in step 3, and the output is the integrated data. Specifically, the server combines weather data and topographic data to predict flood risk. A database management system is used for this integration. The server stores the integrated data as a single dataset and uses it for subsequent processing.

[0409] Step 5:

[0410] The server generates real-time information based on the integrated data. The input is the data integrated in step 4, and the output is the generated information. Specifically, the server generates warning messages based on flood risk predictions. A natural language generation tool is used for this generation. The server temporarily stores the generated information in a cache and prepares it for immediate provision.

[0411] Step 6:

[0412] The server provides generated information based on the search terms. The input is the search terms entered by the user, and the output is the information as search results. The user enters the search terms using a terminal, and the server provides relevant information based on those search terms. For example, if the user searches for "flood risk," the server will provide the latest flood risk information. The server sends the search results to the user's terminal, and the user views them.

[0413] Step 7:

[0414] The server uses an emotion engine to recognize the user's emotions. Input is the user's facial expressions and voice data, and output is the analyzed emotion data. When a user searches for information through their device, the server analyzes their emotions from their facial expressions and voice and adjusts the information provided accordingly. For example, if the user has an anxious expression, the server will provide more detailed explanations or reassuring information. The server analyzes the emotion data in real time and reflects this in the information provided.

[0415] (Application Example 1)

[0416] Next, we will describe Application Example 1 of Form 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."

[0417] In operating autonomous vehicles, it is necessary to grasp environmental information such as weather and traffic conditions in real time and select appropriate routes. However, conventional systems have had difficulty efficiently analyzing data from Earth observation satellites and providing it in real time. Furthermore, because information is not provided with consideration for user emotions, there have been challenges in ensuring sufficient safety and efficiency of operation.

[0418] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, an emotion engine for recognizing the user's emotions, and a real-time environment monitoring means for supporting the operation of an autonomous vehicle. This makes it possible to analyze data from Earth observation satellites in real time and provide appropriate operation information that takes the user's emotions into consideration.

[0419] "Earth observation data" refers to data about the state of the Earth's surface and atmosphere transmitted from Earth observation satellites.

[0420] "Receiving means" refers to devices or systems for receiving Earth observation data transmitted from Earth observation satellites.

[0421] "Analysis means" refers to devices or systems, including generating AI, for analyzing received Earth observation data.

[0422] "Generative AI" is an artificial intelligence technology that analyzes received data and generates specific information.

[0423] A "classification means" is a device or system that classifies data analyzed by an analysis means into a specific category.

[0424] An "integration tool" is a device or system that integrates classified data and combines it into a single piece of information.

[0425] A "generation means" refers to a device or system that generates real-time information based on integrated data.

[0426] "Means of provision" refers to devices or systems that provide generated information according to search terms.

[0427] An "emotion engine" is a device or system that recognizes a user's emotions and provides information that corresponds to those emotions.

[0428] A "real-time environmental monitoring means" refers to a device or system that monitors environmental information in real time in order to support the operation of autonomous vehicles.

[0429] A system for carrying out this invention includes receiving means for receiving Earth observation data, analysis means including generating AI for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, providing means for providing the generated information according to search words, an emotion engine for recognizing the user's emotions, and real-time environmental monitoring means for supporting the operation of an autonomous vehicle.

[0430] Hardware and software to use

[0431] hardware

[0432] Onboard computer for autonomous vehicles: Performs data processing and analysis.

[0433] Antenna that receives data from Earth observation satellites: Receives Earth observation data.

[0434] software

[0435] SatelliteDataReceiver: A module that receives data from Earth observation satellites.

[0436] AIAnalyzer: A generative AI model that analyzes received data.

[0437] DataClassifier: A module for classifying analytical data.

[0438] DataIntegrator: A module for integrating classification data.

[0439] RealTimeInfoGenerator: A module that generates real-time information based on integrated data.

[0440] EmotionEngine: A module that recognizes the user's emotions.

[0441] Data processing and calculations

[0442] The server first receives data from Earth observation satellites in real time. The received data is analyzed using a generative AI model. The analyzed data is classified into specific categories and then integrated into a single piece of information. Based on this integrated data, real-time operational information is generated. Furthermore, it recognizes the user's emotions and provides information tailored to those emotions.

[0443] Specific example

[0444] For example, if the weather is deteriorating, the server will notify you with "Warning: Weather is deteriorating. Please change your route." On the other hand, if the weather is good, it will notify you with "The current route is safe."

[0445] Example of a prompt

[0446] Create a program that receives data from Earth observation satellites, analyzes weather information, and provides operational information tailored to the user's emotions. If the user is feeling anxious, issue a warning; if the weather is good, notify them that it is safe.

[0447] In this way, it becomes possible to analyze data from Earth observation satellites in real time and provide appropriate operational information that takes into account the user's feelings.

[0448] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0449] Step 1:

[0450] The server receives data from Earth observation satellites. The input is Earth observation data transmitted from the satellites, and the output is the received raw data. Specifically, the server uses an antenna to catch signals from the satellites and receives the data.

[0451] Step 2:

[0452] The server analyzes received Earth observation data using a generating AI model. The input is the received raw data, and the output is the analyzed data. Specifically, the server starts the AIAnalyzer module and inputs prompts for data analysis into the generating AI model.

[0453] Step 3:

[0454] The server classifies the analyzed data into specific categories using classification tools. The input is the analyzed data, and the output is the classified data. Specifically, the server uses the DataClassifier module to classify the data into categories such as weather information, traffic information, and road conditions.

[0455] Step 4:

[0456] The server combines classified data into a single piece of information using an integration mechanism. The input is classified data, and the output is integrated data. Specifically, the server uses the DataIntegrator module to integrate data from each category and generate overall environmental information.

[0457] Step 5:

[0458] The server generates real-time operational information based on integrated data. The input is integrated data, and the output is real-time operational information. Specifically, the server uses the RealTimeInfoGenerator module to generate information such as route optimization and warnings.

[0459] Step 6:

[0460] The server analyzes the user's emotions using an emotion engine that recognizes user emotions. The input is the user's emotion data, and the output is the analyzed emotion information. Specifically, the server uses the EmotionEngine module to analyze the user's facial expressions and voice data to recognize emotions.

[0461] Step 7:

[0462] The server provides appropriate information based on generated operational information and user sentiment information. Inputs are real-time operational information and analyzed sentiment information, while output is optimal operational information provided to the user. Specifically, the server uses a delivery system to notify the user of information such as changes to the operational route or warnings.

[0463] (Example 2)

[0464] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0465] Conventional Earth observation data analysis systems often lacked sufficient preprocessing and analysis of received data, making real-time information generation difficult. Furthermore, they lacked the ability to adjust information based on user sentiment, failing to provide users with appropriate information. This resulted in decreased user satisfaction and limited the system's usefulness.

[0466] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0467] In this invention, the server includes receiving means for receiving Earth observation data, preprocessing means for preprocessing the received Earth observation data, analysis means including a generating AI for analyzing the preprocessed Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, providing means for providing the generated information according to search words, collection means for collecting the user's voice and facial expressions, analysis means for analyzing the collected voice and facial expressions, emotion recognition means for recognizing the user's emotions from the analysis results, and information generation means for generating and displaying information based on the recognized emotions. This enables highly accurate analysis of Earth observation data and the provision of information based on the user's emotions.

[0468] "Earth observation data" refers to data collected to observe the conditions of the Earth's surface, atmosphere, oceans, and other natural features.

[0469] "Receiving means" refers to devices or functions for receiving Earth observation data from an external source.

[0470] "Preprocessing means" refers to devices or functions that perform preprocessing, such as noise reduction and normalization, on received Earth observation data to make it easier to analyze.

[0471] "Generative AI" refers to algorithms and models that use artificial intelligence technologies such as deep learning to analyze data and detect specific patterns or anomalies.

[0472] "Analysis means" refers to devices or functions that analyze Earth observation data preprocessed using generative AI.

[0473] A "classification means" is a device or function that classifies data analyzed by an analysis means into a specific category or class.

[0474] An "integration tool" is a device or function that integrates classified data and processes it as a single, cohesive piece of information.

[0475] "Generation means" refers to devices or functions that generate real-time information based on integrated data.

[0476] "Means of provision" refers to devices or functions that provide generated information according to the user's search terms.

[0477] "Collection means" refers to devices or functions used to collect the user's voice and facial expressions.

[0478] "Emotion recognition means" refers to devices or functions that analyze collected audio and facial expressions to recognize the user's emotions.

[0479] "Information generation means" refers to devices or functions that generate information based on recognized emotions and display it to the user.

[0480] This invention relates to a system that analyzes Earth observation data and provides information based on the user's emotions. Specific embodiments of this system are described below.

[0481] Analysis of Earth observation data

[0482] The server has a receiving mechanism for receiving Earth observation data. This receiving mechanism uses a satellite communication module to receive data from Earth observation satellites. The received data is stored in a specified directory.

[0483] Next, the server uses preprocessing tools to preprocess the received data. Preprocessing includes reading the data using the Python Pandas library and imputing missing values. For image data, denoising is performed using OpenCV.

[0484] The preprocessed data is input into an analysis tool that includes a generative AI. The generative AI is built using deep learning frameworks such as TensorFlow and PyTorch. The server loads a TensorFlow model and inputs the preprocessed data. The model detects climate change and natural disaster occurrences in specific regions.

[0485] Data analyzed by the analysis means is classified into specific categories or classes by the classification means. The classified data is integrated by the integration means and processed as a single, cohesive piece of information. Based on the integrated data, the generation means generates real-time information. The generated information is provided by the provision means according to the user's search terms.

[0486] User emotion recognition

[0487] The device has a means of collecting the user's voice and facial expressions. Voice is collected via the microphone, and facial expressions are collected via the camera. The collected data is temporarily stored in the device's memory.

[0488] Next, the device uses analysis tools to analyze the collected audio and facial expressions. Google Cloud Speech-to-Text is used for audio analysis, and OpenCV and Dlib are used for facial expression analysis. The device calls the Google Cloud Speech-to-Text API to convert audio to text. OpenCV and Dlib are used to analyze the user's emotions from their facial expressions.

[0489] Based on the analysis results, the device recognizes the user's emotions using emotion recognition mechanisms. The emotion engine then classifies the user's emotions into categories such as "joy," "sadness," and "anger" based on the analysis results.

[0490] Based on recognized emotions, the device uses information generation mechanisms to generate and display information to the user. For example, if the user says, "I'm very happy today," the device generates positive news or encouraging messages. The generated information is displayed on the device's screen.

[0491] Specific example

[0492] The server uses image data acquired from NASA's MODIS satellite. The generating AI analyzes the image data using deep learning frameworks such as TensorFlow and PyTorch. As a result of the analysis, it detects abnormal temperature increases and flood occurrences in specific regions.

[0493] Example of a prompt:

[0494] "Analyze the latest image data acquired from NASA's MODIS satellite to detect climate change and natural disaster occurrences in specific regions."

[0495] The device collects the user's voice using a microphone and converts it to text using speech recognition software (e.g., Google Cloud Speech-to-Text). The emotion engine analyzes the user's facial expressions using libraries such as OpenCV and Dlib. If the user says, "I'm very happy today," the device displays positive news or encouraging messages.

[0496] Example of a prompt:

[0497] "Analyze the user's voice and facial expressions, and prioritize generating positive information if the user is showing positive emotions."

[0498] In this way, this system enables highly accurate analysis of Earth observation data and provides information based on the user's emotions.

[0499] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0500] Step 1:

[0501] The server receives Earth observation data. Using the receiving means, it receives data from Earth observation satellites via a satellite communication module. The received data is saved to a specified directory.

[0502] Input: Data from Earth observation satellites

[0503] Output: Saved Earth observation data file

[0504] Step 2:

[0505] The server preprocesses the received Earth observation data. Using preprocessing tools, it reads the data using the Python Pandas library and imputes missing values. For image data, it performs denoising using OpenCV.

[0506] Input: Saved Earth observation data file

[0507] Output: Preprocessed Earth observation data

[0508] Step 3:

[0509] The server inputs preprocessed data into a generative AI model for analysis. The generative AI model is built using TensorFlow or PyTorch. The server loads the TensorFlow model and inputs the preprocessed data. The model detects climate change and natural disaster occurrences in specific regions.

[0510] Input: Preprocessed Earth observation data

[0511] Output: Analysis results (detection results for climate change and natural disasters)

[0512] Step 4:

[0513] The server classifies the data analyzed by the analysis means. It uses classification means to categorize the analysis results into specific categories or classes.

[0514] Input: Analysis results

[0515] Output: Classified data

[0516] Step 5:

[0517] The server integrates the classified data. Using integration methods, it processes the classified data as a single, cohesive piece of information.

[0518] Input: Classified data

[0519] Output: Integrated data

[0520] Step 6:

[0521] The server generates real-time information based on integrated data. It uses generation means to generate information based on integrated data.

[0522] Input: Integrated data

[0523] Output: Generated real-time information

[0524] Step 7:

[0525] The server provides generated information according to the search terms. It uses a delivery method to provide information based on the user's search terms.

[0526] Input: Generated real-time information, user search terms

[0527] Output: Provided information

[0528] Step 8:

[0529] The device collects the user's voice and facial expressions. It uses a collection method to capture the user's voice and facial expressions through the microphone and camera. The collected data is temporarily stored in the device's memory.

[0530] Input: User's voice and facial expressions

[0531] Output: Collected audio and facial expression data

[0532] Step 9:

[0533] The device analyzes the collected audio and facial expressions. Using analysis tools, it employs Google Cloud Speech-to-Text for audio analysis and OpenCV and Dlib for facial expression analysis. The device calls the Google Cloud Speech-to-Text API to convert audio to text. It then uses OpenCV and Dlib to analyze the user's emotions from their facial expressions.

[0534] Input: Collected audio and facial expression data

[0535] Output: Analysis results (text conversion results of speech, facial expression analysis results)

[0536] Step 10:

[0537] The device recognizes the user's emotions from the analysis results. Using emotion recognition technology, it classifies the user's emotions based on the analysis results.

[0538] Input: Analysis results

[0539] Output: Recognized emotions

[0540] Step 11:

[0541] The device generates and displays information based on recognized emotions. Using information generation methods, it generates information tailored to the user's emotions and displays it on the device's screen. For example, if the user says, "I'm very happy today," it generates positive news or encouraging messages.

[0542] Input: Recognized emotions

[0543] Output: Generated information, displayed information

[0544] (Application Example 2)

[0545] Next, we will describe Application Example 2 of Form 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."

[0546] While conventional Earth observation data analysis systems have shown some effectiveness in detecting climate change and natural disasters, they lack the ability to adjust information based on user emotions, making it difficult to provide appropriate warnings and advice to users. Furthermore, providing information without considering user emotions can potentially increase user anxiety.

[0547] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0548] In this invention, the server includes receiving means for receiving Earth observation data, analysis means including generating AI for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, emotion recognition means for recognizing the user's emotions, adjustment means for adjusting the information generated based on the emotions recognized by the emotion recognition means, and providing means for providing the generated information according to search words. This makes it possible to provide appropriate warnings and advice according to the user's emotions.

[0549] "Earth observation data" refers to data recorded using satellites and other observation instruments to document the state of the Earth's surface, atmosphere, oceans, and other features.

[0550] "Receiving means" refers to devices or systems for receiving Earth observation data.

[0551] "Analysis means" refers to devices or systems, including generating AI, for analyzing received Earth observation data.

[0552] "Generative AI" is artificial intelligence that uses AI technologies such as deep learning to analyze data and generate new information.

[0553] A "classification means" is a device or system that classifies data analyzed by an analysis means into a specific category.

[0554] An "integration tool" is a device or system that combines classified data into a single integrated dataset.

[0555] A "generation means" refers to a device or system that generates real-time information based on integrated data.

[0556] "Emotion recognition means" refers to devices or systems that recognize emotions from a user's voice tone, facial expressions, or text input.

[0557] "Adjustment means" refers to devices or systems that adjust information generated based on emotions recognized by emotion recognition means.

[0558] "Means of provision" refers to devices or systems that provide generated information to users according to their search terms.

[0559] A system for carrying out this invention includes a series of processes for receiving and analyzing Earth observation data and tailoring and providing information based on the user's emotions. Specific embodiments thereof are described below.

[0560] Hardware and software to be used

[0561] Hardware: Smartphone

[0562] Software: TensorFlow, OpenCV, Transformers (Hugging Face)

[0563] System Configuration

[0564] 1. Receiving method:

[0565] The server receives Earth observation data from satellites and other observation instruments. This includes satellite imagery and weather data.

[0566] 2. Analysis method:

[0567] The server uses generative AI to analyze the received Earth observation data. This generative AI is a deep learning model using TensorFlow and predicts the risks of climate change and natural disasters.

[0568] 3. Classification means:

[0569] The server categorizes the analyzed data into specific categories, such as climate change, floods, and earthquakes.

[0570] 4. Integration methods:

[0571] The server combines the classified data into a single, integrated dataset. This centralizes information from multiple data sources.

[0572] 5. Generation means:

[0573] The server generates real-time information based on integrated data, including information on current weather conditions and natural disaster risks.

[0574] 6. Emotion recognition means:

[0575] The device (smartphone) recognizes emotions from the user's voice tone, facial expressions, and text input. This uses an emotion recognition model based on Transformers.

[0576] 7. Adjustment means:

[0577] The server adjusts the information generated based on the emotions recognized by the emotion recognition system. For example, if a user is showing signs of anxiety, it prioritizes providing information that provides a sense of security.

[0578] 8. Means of provision:

[0579] The device provides the user with generated information based on their search terms. This allows the user to quickly obtain the information they need.

[0580] Specific example

[0581] Example of a prompt

[0582] User text input: "I'm worried about the recent weather."

[0583] Satellite image path: "satellite_image.jpg"

[0584] Using this prompt, the system recognizes the user's emotions and analyzes Earth observation data to provide appropriate information. For example, if the user enters "I'm worried about the recent weather," the system recognizes this emotion and analyzes the latest satellite imagery to provide current weather conditions. If the user indicates anxiety, the system prioritizes providing reassuring information.

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0586] Step 1:

[0587] The server receives Earth observation data from satellites and other observation instruments. Inputs include satellite imagery and weather data, while output is the received raw data. Specifically, the server periodically retrieves data from observation instruments and stores it in a database.

[0588] Step 2:

[0589] The server uses generative AI to analyze the received Earth observation data. The input is the raw data received in step 1, and the output is the analyzed data. Specifically, the server runs a deep learning model using TensorFlow to predict the risks of climate change and natural disasters.

[0590] Step 3:

[0591] The server classifies the analyzed data into specific categories. The input is the data analyzed in step 2, and the output is the classified data. Specifically, the server assigns the analysis results to categories such as "climate change," "floods," and "earthquakes."

[0592] Step 4:

[0593] The server combines the classified data into a single integrated dataset. The input is the data classified in step 3, and the output is the integrated data. Specifically, the server centralizes information from multiple data sources and generates an integrated dataset.

[0594] Step 5:

[0595] The server generates real-time information based on the integrated data. The input is the data integrated in step 4, and the output is the generated real-time information. Specifically, the server analyzes the integrated data and generates information about current weather conditions and natural disaster risks.

[0596] Step 6:

[0597] The device recognizes emotions from the user's voice tone, facial expressions, and text input. The input is the user's voice and text data, and the output is the recognized emotion data. Specifically, the device runs an emotion recognition model using Transformers to analyze the user's emotions.

[0598] Step 7:

[0599] The server adjusts the information generated based on the emotions recognized by the emotion recognition system. The input is the emotion data recognized in step 6 and the real-time information generated in step 5, and the output is the adjusted information. Specifically, the server changes the priority of information according to the user's emotions and generates appropriate warnings and advice.

[0600] Step 8:

[0601] The terminal provides the user with generated information according to the search terms. The input is the information adjusted in step 7 and the user's search terms, and the output is the information provided to the user. Specifically, the terminal filters the information based on the user's search terms and displays the appropriate information.

[0602] (Example 3)

[0603] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0604] Conventional Earth observation data analysis systems have complex processes for analyzing, classifying, and integrating received data, making real-time information provision difficult. Furthermore, they have a problem with not being able to provide information that responds to user emotions, resulting in a poor user experience.

[0605] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0606] In this invention, the server includes receiving means for receiving Earth observation data, analysis means including a generation AI model for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, providing means for providing the generated information according to search words, and an emotion engine in which the providing means provides information corresponding to the user's emotions. This enables real-time information provision and further enables information provision that corresponds to the user's emotions.

[0607] "Receiving means" refers to a device or system for receiving Earth observation data.

[0608] "Analysis means" refers to a device or system that includes a generative AI model for analyzing received Earth observation data.

[0609] A "generative AI model" is an artificial intelligence model used for natural language processing and data analysis.

[0610] A "classification means" is a device or system that classifies data analyzed by an analysis means into categories.

[0611] An "integration tool" is a device or system that integrates classified data and combines it into a single piece of information.

[0612] "Generation means" refers to a device or system that generates real-time information based on integrated data.

[0613] "Means of provision" refers to a device or system that provides generated information according to the user's search terms.

[0614] An "emotion engine" is a device or system that analyzes a user's emotions and provides information corresponding to those emotions.

[0615] Modes for carrying out the invention

[0616] This invention relates to a system that receives, analyzes, classifies, and integrates Earth observation data, generates real-time information, and provides information that responds to the user's emotions. Specific embodiments of this system are described below.

[0617] 1. Receiving data

[0618] The server is equipped with receiving means for receiving Earth observation data. This receiving means is a device that receives data via satellite communication or the internet. For example, it receives weather data and environmental data transmitted from Earth observation satellites.

[0619] 2. Data Analysis

[0620] The server is equipped with analysis means, including a generative AI model for analyzing received Earth observation data. This analysis means includes a generative AI model using natural language processing techniques (e.g., OpenAI's GPT-4). The server analyzes the received data and extracts relevant information.

[0621] 3. Data Classification

[0622] The server is equipped with a classification mechanism for classifying the analyzed data. This classification mechanism uses machine learning algorithms (e.g., Scikit-learn or TensorFlow). The server classifies the analyzed data into categories such as "temperature," "precipitation," and "wind speed."

[0623] 4. Data Integration

[0624] The server is equipped with an integration mechanism for combining classified data. This integration mechanism uses a database management system (e.g., MySQL or PostgreSQL). The server integrates temperature, precipitation, and wind speed data to generate comprehensive information.

[0625] 5. Generation of real-time information

[0626] The server is equipped with generation means for generating real-time information based on integrated data. This generation means uses a regenerative AI model. For example, if a user searches for "Tokyo weather," the server generates the latest information about Tokyo's weather from the integrated data and provides it to the user.

[0627] 6. Providing information that responds to emotions

[0628] The server has an emotion engine that provides information that corresponds to the user's emotions. This emotion engine uses emotion analysis tools (for example, IBM Watson® Tone Analyzer). For example, if the user is showing sad emotions, the server will provide words of comfort or cheerful news.

[0629] Specific example

[0630] Consider a scenario where a user searches for "Tokyo weather" and the system provides information based on that search term. If the user is expressing sadness, the system might provide a message such as, "The weather in Tokyo is sunny. It's going to be a great day. Cheer up!"

[0631] Example of a prompt

[0632] "If a user searches for 'Tokyo weather' and expresses sadness, please generate weather information that includes words of comfort."

[0633] In this way, we can explain specific embodiments of the system while clearly defining the roles of the server, terminal, and user. The flow of a specific process in Example 3 will be explained using Figure 21.

[0634] Step 1:

[0635] The user enters a search term using their terminal. For example, the user enters "Tokyo weather". The entered search term is sent to the server.

[0636] Step 2:

[0637] The server inputs the received search term into a generating AI model for analysis. Specifically, the server analyzes the search term "Tokyo weather" using natural language processing technology and extracts relevant information. The input is the search term, and the output is the analyzed information.

[0638] Step 3:

[0639] The server classifies the analyzed information into categories using classification tools. Specifically, the server uses machine learning algorithms to classify the analyzed information into categories such as "temperature," "precipitation," and "wind speed." The input is the analyzed information, and the output is the classified data.

[0640] Step 4:

[0641] The server integrates the classified data using integration means. Specifically, the server uses a database management system to integrate temperature, precipitation, and wind speed data to generate comprehensive information. The input is classified data, and the output is integrated data.

[0642] Step 5:

[0643] The server generates real-time information based on integrated data. Specifically, the server uses a regenerative AI model to generate information corresponding to the user's search terms. For example, if a user searches for "Tokyo weather," the server generates the latest information about Tokyo's weather from the integrated data and provides it to the user. The input is integrated data, and the output is the generated real-time information.

[0644] Step 6:

[0645] The server provides information that corresponds to the user's emotions using a delivery method. Specifically, the server uses an emotion engine to analyze the user's emotions and generates information that corresponds to those emotions. For example, if the user is expressing sadness, the server will provide words of comfort or cheerful news. The input is the user's emotional information, and the output is information that corresponds to those emotions.

[0646] (Application Example 3)

[0647] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0648] Traditional information delivery systems could provide information based on users' search terms, but they could not provide personalized information that responded to users' emotions. Therefore, they failed to provide information appropriate to the user's emotional state, resulting in a lack of improved user experience.

[0649] In Application Example 3, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, an emotion analysis means for analyzing the user's emotions, an adjustment means for adjusting the information based on the emotion data analyzed by the emotion analysis means, and a providing means for providing the adjusted information to the user. This makes it possible to provide personalized information not only based on the user's search words but also according to the user's emotional state.

[0650] "Receiving means" refers to devices or systems for receiving Earth observation data.

[0651] "Analysis means" refers to devices or systems, including generating AI, for analyzing received Earth observation data.

[0652] A "classification means" is a device or system used to classify data that has been analyzed by an analysis means.

[0653] An "integration tool" is a device or system for integrating classified data.

[0654] "Generation means" refers to devices or systems for generating real-time information based on integrated data.

[0655] "Means of provision" refers to devices or systems for providing generated information according to search terms.

[0656] "Emotional analysis tools" refer to devices or systems used to analyze a user's emotions.

[0657] "Adjustment means" refers to devices or systems for adjusting information based on emotional data analyzed by emotion analysis means.

[0658] The system for carrying out this invention is configured as follows.

[0659] The server is equipped with a receiving means for receiving Earth observation data. The receiving means is a device for receiving data transmitted from satellites and ground observation equipment. The received data is sent to an analysis means and analyzed using a generative AI model. The analysis means is software for extracting features from the data and extracting necessary information.

[0660] The analyzed data is classified by a classification means. The classification means includes algorithms for dividing data into categories according to its type and content. The classified data is then integrated by an integration means. The integration means is software for combining multiple data sets into a single piece of information.

[0661] Based on integrated data, the generation means generates real-time information. The generation means is software that uses a generation AI model to generate information required by the user. The generated information is provided to the user by the provision means. The provision means includes an interface for displaying information according to the user's search terms.

[0662] Furthermore, emotion analysis tools are used to analyze the user's emotions. These tools are software that uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice. The analyzed emotion data is then used to adjust the information using adjustment tools. These adjustment tools include algorithms for personalizing the information according to the user's emotions.

[0663] For example, if a user searches for "Tokyo weather" and expresses sadness, the system will provide information about Tokyo's weather along with positive news and encouraging words to comfort the user. An example of a prompt would be: "If the user searches for 'Tokyo weather' and expresses sadness, generate information about Tokyo's weather along with positive news and encouraging words to comfort the user."

[0664] In this way, it becomes possible to provide personalized information not only based on the user's search terms, but also in accordance with the user's emotional state.

[0665] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0666] Step 1:

[0667] The user launches a smartphone application and enters a search term into the search bar. The entered search term is sent to the server. The input data is the search term, and the output data is the search term sent to the server.

[0668] Step 2:

[0669] The device's camera and microphone are used to capture the user's facial expressions and voice. The captured data is sent to a server. The input data is the user's facial expressions and voice, and the output data is the facial expressions and voice data sent to the server.

[0670] Step 3:

[0671] The server receives Earth observation data using a receiving device. The input data is Earth observation data, and the output data is the received Earth observation data.

[0672] Step 4:

[0673] The server analyzes the received Earth observation data using analytical tools. It extracts data features and necessary information using a generative AI model. The input data is the received Earth observation data, and the output data is the analyzed data.

[0674] Step 5:

[0675] The server classifies the analyzed data using classification methods. It divides the data into categories according to its type and content. The input data is the analyzed data, and the output data is the classified data.

[0676] Step 6:

[0677] The server integrates classified data using integration methods. It combines multiple data points into a single piece of information. The input data is classified data, and the output data is integrated data.

[0678] Step 7:

[0679] The server generates real-time information based on integrated data using generation methods. It uses a generation AI model to generate the information the user needs. The input data is integrated data, and the output data is the generated information.

[0680] Step 8:

[0681] The server analyzes the user's facial expressions and voice data using emotion analysis tools. It identifies the user's emotions using an emotion analysis algorithm. The input data consists of the user's facial expressions and voice, while the output data is the analyzed emotion data.

[0682] Step 9:

[0683] The server adjusts information based on sentiment data analyzed using adjustment mechanisms. It personalizes information according to the user's emotions. Input data consists of analyzed sentiment data and generated information, while output data is the adjusted information.

[0684] Step 10:

[0685] The server provides users with information that has been adjusted using the provided means. Information is displayed according to the user's search terms. Input data is adjusted information, while output data is the information provided to the user.

[0686] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0687] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0688] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0689] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0690] [Second Embodiment]

[0691] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0692] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0693] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0694] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0695] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0696] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0697] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0698] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0699] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0700] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0701] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0702] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0703] "Example of form 1"

[0704] One embodiment of the present invention is a system equipped with a receiving means for receiving Earth observation data transmitted from an Earth observation satellite. This receiving means is capable of receiving data from the satellite in real time.

[0705] "Example of form 2"

[0706] Next, there is an analysis method that includes a generative AI for analyzing the received Earth observation data. This generative AI uses AI technologies such as deep learning to analyze the Earth observation data. For example, it can detect climate change or natural disasters in specific regions from satellite images.

[0707] "Example of form 3"

[0708] Furthermore, there are classification means for classifying the analyzed data and integration means for integrating the classified data. Through these means, the analyzed data is appropriately classified and integrated, making it possible to efficiently provide the information that users need.

[0709] "Example of form 4"

[0710] Finally, there is a means of providing information that generates real-time data based on integrated data and provides information generated according to the user's search terms. For example, if a user searches for "Tokyo weather," the system generates information about Tokyo's weather from data analyzed, classified, and integrated by a generation AI, and provides it to the user.

[0711] The following describes the processing flow for each example of the form.

[0712] "Example of form 1"

[0713] Step 1: Receive Earth observation data transmitted from Earth observation satellites. This reception is performed in real time using specific receiving equipment.

[0714] "Example of form 2"

[0715] Step 1: The received Earth observation data is analyzed by a generating AI. This analysis is performed using AI technologies such as deep learning, making it possible to detect climate change and natural disasters in specific regions from satellite images.

[0716] "Example of form 3"

[0717] Step 1: Classify the analyzed data. This classification is performed using a specific classification algorithm.

[0718] Step 2: Integrate the classified data. This integration is performed using a specific integration algorithm, ensuring that the analyzed data is appropriately classified and integrated.

[0719] "Example of form 4"

[0720] Step 1: Generate real-time information based on integrated data. This generation is performed using a specific generation algorithm.

[0721] Step 2: Provide information generated in response to the user's search terms. For example, if a user searches for "Tokyo weather," the system generates information about Tokyo's weather from data analyzed, classified, and integrated by the generating AI, and provides it to the user.

[0722] (Example 1)

[0723] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0724] Conventional systems for receiving and analyzing Earth observation data were inefficient in real-time data reception and analysis, making it difficult to quickly provide users with the information they needed. Furthermore, the methods for storing and providing analyzed data were inadequate, resulting in delays for users accessing the data they required.

[0725] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means for analyzing the received Earth observation data, a storage means for storing the data analyzed by the analysis means, a providing means for providing the stored data, and an access means for users to access the data through the providing means. This makes it possible to receive data transmitted from Earth observation satellites in real time and to quickly store and provide the analyzed data.

[0726] "Receiving means" refers to devices or systems for receiving data transmitted from Earth observation satellites.

[0727] "Analysis means" refers to devices or systems used to analyze received Earth observation data and extract necessary information.

[0728] "Storage means" refers to devices or systems for saving analyzed data to a database or similar location.

[0729] "Means of provision" refers to devices or systems used to provide stored data to users or other systems.

[0730] "Access means" refers to devices or systems that allow users to access data stored through the provided means.

[0731] This invention relates to a system for receiving, analyzing, storing, and providing data transmitted from Earth observation satellites in real time. Specific embodiments of this system are described below.

[0732] The server uses a highly sensitive antenna and receiver to receive Earth observation data. This makes it possible to receive data transmitted from Earth observation satellites in real time. The received data is sent to the server via data receiving software.

[0733] Next, the server uses data analysis software to analyze the received data. This analysis process involves formatting, filtering, and extracting necessary information from the data. For example, in the case of weather data, information such as temperature, humidity, and wind speed is extracted.

[0734] The analyzed data is stored in a database on the server. When saving, metadata such as the data type and timestamp is also saved. This makes it easier to search for the data later.

[0735] The stored data is provided to users and other systems through the distribution channels. Users can access the API using their devices and retrieve the necessary data. For example, if a user wants to obtain weather data, they can send a request to the API and receive temperature, humidity, and wind speed data from the server.

[0736] As a concrete example, the process by which a server receives, analyzes, stores, and provides weather data transmitted from an Earth observation satellite is shown below.

[0737] Specific example: The server receives weather data transmitted from Earth observation satellites using a high-sensitivity antenna and receiver. The received data is sent to the server via data receiving software, and data analysis software extracts information such as temperature, humidity, and wind speed. The analyzed data is stored in a database, and users access the API using their terminals to retrieve the necessary data.

[0738] Example of a prompt:

[0739] Please create a program that receives weather data transmitted from Earth observation satellites in real time and extracts information such as temperature, humidity, and wind speed. The hardware to be used will be a high-sensitivity antenna and receiver, and the software will consist of data reception software and data analysis software. The analyzed data should be stored in a database and provided to users via an API.

[0740] In this way, a system can be built in which the server can efficiently receive, analyze, store, and provide data from Earth observation satellites.

[0741] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0742] Step 1: Prepare to receive data

[0743] The server installs and connects a high-sensitivity antenna and receiver to receive data from Earth observation satellites. Next, it starts the data receiving software and configures it to receive signals from the satellites. The input is the installation and connection of the high-sensitivity antenna and receiver, and the output is the startup and completion of the data receiving software configuration.

[0744] Step 2: Data Reception

[0745] The server uses a high-sensitivity antenna and receiver to receive data transmitted from Earth observation satellites in real time. The received data is transferred to the server via data receiving software. The input is data from Earth observation satellites, and the output is the received data. Specifically, the server adjusts the antenna and receiver to capture the optimal signal.

[0746] Step 3: Data Analysis

[0747] The server passes the received data to data analysis software and begins the analysis. This analysis process involves formatting, filtering, and extracting necessary information from the data. For example, in the case of weather data, information such as temperature, humidity, and wind speed is extracted. The input is the received data, and the output is the analyzed data. Specifically, the server runs the data analysis software and processes the data using various algorithms.

[0748] Step 4: Save Data

[0749] The server stores the analyzed data in a database. When saving, it also saves metadata such as the data type and timestamp. The input is the analyzed data, and the output is the data stored in the database. Specifically, the server connects to the database and inserts data using SQL queries.

[0750] Step 5: Data Provision

[0751] The server makes stored data accessible through an API to provide it to users and other systems. Users can access the API using their terminals and retrieve the necessary data. The input is the user's request, and the output is the provided data. Specifically, the server receives an API request, retrieves the necessary data from the database, and sends it back to the user.

[0752] In this way, the server will build a system that can efficiently receive, analyze, store, and provide data from Earth observation satellites.

[0753] (Application Example 1)

[0754] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0755] There is a need for a system that can use real-time data from Earth observation satellites to quickly detect natural disasters and suspicious activity, and issue appropriate warnings to users. However, conventional systems have problems with inefficient processes from data reception to analysis and warning issuance, and lack real-time capabilities. As a result, users have been unable to receive information at the appropriate time, making it difficult to respond quickly.

[0756] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0757] In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, and a classification means for classifying the data analyzed by the analysis means. This makes it possible to efficiently receive and analyze real-time data from Earth observation satellites, quickly detect natural disasters and suspicious activities, and issue warnings to users.

[0758] "Earth observation data" refers to data transmitted from Earth observation satellites that includes information about the Earth's surface and atmosphere.

[0759] "Receiving means" refers to a device or system for receiving Earth observation data transmitted from Earth observation satellites in real time.

[0760] "Analysis means" refers to a device or system that includes a generating AI for analyzing received Earth observation data.

[0761] "Generative AI" is an artificial intelligence technology that analyzes received data to detect specific patterns or anomalies.

[0762] A "classification means" is a device or system for classifying data analyzed by an analysis means into a specific category.

[0763] An "integration means" is a device or system for integrating classified data and generating overall information.

[0764] "Generation means" refers to a device or system for generating real-time information based on integrated data.

[0765] "Means of provision" refers to a device or system for providing generated information to a user according to the search terms.

[0766] A "warning device" is a device or system for issuing a warning to a user based on generated information.

[0767] The system for carrying out this invention is for receiving and analyzing Earth observation data transmitted from Earth observation satellites in real time and issuing warnings to users. Specific embodiments of the system are described below.

[0768] System Configuration

[0769] The system consists of the following main components:

[0770] 1. Receiving means: A device or system that receives Earth observation data transmitted from Earth observation satellites in real time. Specifically, this involves using a dedicated receiving antenna or data receiving server.

[0771] 2. Analysis means: A device or system including a generative AI for analyzing received Earth observation data. The data is analyzed using generative AI models such as Python or TensorFlow.

[0772] 3. Classification means: A device or system that classifies data analyzed by an analysis means into specific categories. This involves efficiently classifying data using a database management system (DBMS).

[0773] 4. Integration means: A device or system that integrates classified data and generates overall information. The integrated data is updated in real time.

[0774] 5. Generation means: A device or system that generates real-time information based on integrated data. The generated information is provided to the user.

[0775] 6. Means of delivery: A device or system that provides generated information to users according to their search terms. Information is provided through smartphone apps or web applications.

[0776] 7. Warning means: A device or system that issues a warning to the user based on the generated information. Warnings are issued using push notifications or email notifications.

[0777] Program processing

[0778] The server receives Earth observation data and analyzes it using a generative AI. The analyzed data is categorized and integrated into specific categories. Real-time information is generated based on the integrated data and provided to the user. Furthermore, warnings are issued to the user based on the generated information.

[0779] Hardware and software to be used

[0780] Hardware: Receiving antenna, data receiving server, smartphone

[0781] Software: Python, TensorFlow, Database Management Systems (DBMS), Smartphone Apps, Web Applications

[0782] Specific example

[0783] For example, data transmitted from Earth observation satellites is received, and a generative AI model is used to detect natural disasters (e.g., earthquakes, floods) and suspicious activities (e.g., forest fires, poaching). The detected information is provided to the user in real time, and warnings are issued as needed.

[0784] Examples of prompts for generative AI models

[0785] Create a Python program that analyzes real-time data from Earth observation satellites to detect natural disasters and suspicious activity. The program should retrieve data via an API and include a function to alert the user based on the analysis results.

[0786] In this way, the system can efficiently utilize Earth observation data and provide users with timely and appropriate information and warnings.

[0787] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0788] Step 1:

[0789] The server receives Earth observation data transmitted from Earth observation satellites. Using receiving equipment, it acquires data through dedicated receiving antennas and data receiving servers. The input is data from Earth observation satellites, and the output is the received raw data.

[0790] Step 2:

[0791] The server analyzes received Earth observation data using a generating AI model. It uses Python and TensorFlow as analysis tools to detect patterns and anomalies in the data. The input is the received raw data, and the output is the analyzed data. Specific operations include data preprocessing, feature extraction, model input, and retrieval of analysis results.

[0792] Step 3:

[0793] The server classifies the analyzed data into specific categories. A database management system (DBMS) is used as the classification method to efficiently classify the analysis results. The input is the analyzed data, and the output is the classified data. Specific operations include inserting data into the database and classifying data through queries.

[0794] Step 4:

[0795] The server integrates classified data to generate overall information. Using integration methods, it updates data in real time and generates integrated information. The input is classified data, and the output is integrated data. Specific operations include data aggregation, statistical processing, and real-time updates.

[0796] Step 5:

[0797] The server generates real-time information based on integrated data. It uses generation methods to produce information for the user. The input is integrated data, and the output is generated information. Specific operations include data format conversion and information generation.

[0798] Step 6:

[0799] The server provides generated information to the user based on the search terms. It uses smartphone apps and web applications to deliver the information to the user. The input consists of the generated information and the user's search terms, while the output is the information provided to the user. Specific operations include receiving search terms, filtering information, and providing the information.

[0800] Step 7:

[0801] The server issues a warning to the user based on the generated information. It uses push notifications and email notifications as warning methods. The input is the generated information, and the output is the warning issued to the user. Specific operations include checking warning conditions, generating notifications, and sending notifications.

[0802] (Example 2)

[0803] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0804] Conventional Earth observation data analysis systems often perform preprocessing and analysis of received data manually, resulting in inefficiency. Furthermore, the classification and integration of analysis results are insufficient, making real-time information provision difficult. Additionally, information based on user search terms may not be adequately provided.

[0805] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, a preprocessing means for preprocessing the received Earth observation data, an analysis means including a generation AI for analyzing the preprocessed data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, and a providing means for providing the generated information according to search words. This enables efficient preprocessing, analysis, classification, and integration of Earth observation data, and realizes real-time information provision. It also enables the provision of appropriate information based on search words from the user.

[0806] "Receiving means" refers to devices or functions for receiving Earth observation data from satellites or other data sources.

[0807] "Preprocessing means" refers to devices or functions that perform processing to convert received Earth observation data into a format suitable for analysis.

[0808] "Analysis means" refers to devices or functions, including generative AI, for analyzing pre-processed data.

[0809] "Generative AI" is artificial intelligence that uses deep learning technology to analyze Earth observation data and detect climate change and natural disasters in specific regions.

[0810] A "classification means" is a device or function that classifies data analyzed by an analysis means into specific categories or patterns.

[0811] An "integration tool" is a device or function that integrates classified data and generates overall information.

[0812] "Generation means" refers to devices or functions that generate real-time information based on integrated data.

[0813] "Means of provision" refers to devices or functions that provide generated information according to the user's search terms.

[0814] A "search term" is a keyword or phrase that a user enters to search for specific information.

[0815] Modes for carrying out the invention

[0816] This invention relates to a system for efficiently analyzing Earth observation data and providing real-time information. Specific embodiments of this system are described below.

[0817] 1. System Configuration

[0818] This system includes the following main means:

[0819] Receiving method: Earth observation data is received from satellites and other data sources.

[0820] Preprocessing means: Convert the received Earth observation data into a format suitable for analysis.

[0821] Analysis means: Includes a generative AI for analyzing preprocessed data.

[0822] Classification method: Data analyzed by the analysis method is classified into specific categories or patterns.

[0823] Integration method: Integrates classified data to generate overall information.

[0824] Generation method: Generates real-time information based on integrated data.

[0825] Delivery method: Generated information is provided according to the user's search terms.

[0826] 2. Hardware and software to be used

[0827] Server: A server with high-performance computing capabilities is used to receive, preprocess, analyze, classify, integrate, generate, and deliver data.

[0828] Generative AI Model: We use a generative AI model based on deep learning technology to analyze Earth observation data.

[0829] Database: Use a database to store analysis results and integrated data.

[0830] User terminal: The user uses a device (PC, smartphone, etc.) to enter search terms and view the analysis results.

[0831] 3. Specific examples

[0832] If a user wants to analyze climate change in a specific region, they should follow these steps.

[0833] 1. Data reception:

[0834] The user uploads satellite image data of the region they want to analyze to the server.

[0835] The server receives the uploaded data.

[0836] 2. Data preprocessing:

[0837] The server removes noise from the received image data and adjusts the resolution.

[0838] The server imputes missing values ​​and normalizes numerical data.

[0839] 3. Application of Generative AI Models:

[0840] The server inputs the pre-processed data into the generating AI model.

[0841] Generative AI models analyze data and detect climate change patterns in specific regions.

[0842] 4. Output of analysis results:

[0843] The server outputs the analysis results obtained from the generated AI model.

[0844] The user reviews the analysis results provided by the server and takes necessary countermeasures.

[0845] Example of a prompt

[0846] "To analyze climate change in a specific region, please use the following satellite imagery data. Preprocess the data and apply a generative AI model to detect climate change patterns."

[0847] In this way, the server, terminals, and users each play their respective roles, resulting in a system that enables efficient analysis of Earth observation data and real-time information provision.

[0848] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0849] Step 1: Data Received

[0850] The server receives Earth observation data from satellites and other data sources. Inputs include satellite image data and numerical data. Outputs are the received raw data.

[0851] Specific operation: The server receives data transmitted from the satellite and stores it in storage.

[0852] Step 2: Data Preprocessing

[0853] The server preprocesses the received Earth observation data. The input is the received raw data. The output is the preprocessed data.

[0854] Specific actions:

[0855] For image data: The server applies a noise reduction filter and adjusts the resolution.

[0856] For numerical data: The server imputes missing values ​​and normalizes the data.

[0857] Step 3: Applying the Generative AI Model

[0858] The server inputs pre-processed data into the generating AI model. The input is the pre-processed data. The output is the analysis result.

[0859] Specific operation: The server inputs pre-processed data into a generating AI model, which then analyzes the data to detect patterns in climate change and natural disasters.

[0860] Step 4: Data Classification

[0861] The server classifies the analysis results obtained from the generated AI model. The input is the analysis results. The output is the classified data.

[0862] Specific operation: The server applies an algorithm to classify the analysis results into specific categories or patterns.

[0863] Step 5: Data Integration

[0864] The server integrates the classified data. The input is the classified data. The output is the integrated data.

[0865] Specific operation: The server integrates the classified data and generates overall information.

[0866] Step 6: Information Generation

[0867] The server generates real-time information based on integrated data. The input is the integrated data. The output is the generated real-time information.

[0868] Specific operation: The server analyzes the integrated data and applies algorithms to generate real-time information.

[0869] Step 7: Information Provision

[0870] The server provides generated information according to the user's search terms. The input consists of the user's search terms and the generated real-time information. The output is the information provided to the user.

[0871] Specific operation: The server receives search terms from the user, extracts appropriate information based on those terms, and provides it to the user's terminal.

[0872] In this way, the server, terminals, and users each play their respective roles, resulting in a system that enables efficient analysis of Earth observation data and real-time information provision.

[0873] (Application Example 2)

[0874] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0875] Conventional Earth observation data analysis systems have struggled to predict climate change and natural disasters in real time and notify users quickly. Furthermore, they lacked the functionality to provide users with the information they needed based on search terms, making information retrieval cumbersome. Therefore, in today's world, where rapid responses to climate change and natural disasters are essential, a more efficient and rapid information delivery system is needed.

[0876] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, a prediction means for receiving satellite image data and predicting the occurrence of climate change and natural disasters, and a notification means for notifying the user of the predicted information in real time. This makes it possible to quickly predict the occurrence of climate change and natural disasters and notify the user in real time.

[0877] "Earth observation data" refers to data collected to observe the conditions of the Earth's surface, atmosphere, oceans, and other natural features.

[0878] "Receiving means" refers to devices or systems for receiving Earth observation data.

[0879] "Generative AI" refers to artificial intelligence models that use artificial intelligence technologies such as deep learning to analyze data and generate new information.

[0880] "Analysis means" refers to devices or systems used to analyze received Earth observation data.

[0881] A "classification tool" is a device or system used to classify analyzed data into specific categories.

[0882] An "integration tool" is a device or system for integrating classified data and generating overall information.

[0883] "Generation means" refers to devices or systems for generating real-time information based on integrated data.

[0884] "Means of provision" refers to devices or systems that provide generated information according to the user's search terms.

[0885] "Satellite image data" refers to image data of the Earth taken from a satellite.

[0886] "Prediction tools" refer to devices and systems that analyze satellite image data to predict the occurrence of climate change and natural disasters.

[0887] A "notification means" is a device or system for notifying users of predicted information in real time.

[0888] A system for carrying out this invention includes a series of processes for receiving, analyzing, classifying, and integrating Earth observation data, generating real-time information, and providing it to the user. Specific embodiments of this system are described below.

[0889] First, the server receives image data transmitted from the satellite using a receiving means for receiving Earth observation data. This receiving means includes a communication module for acquiring data via an internet connection.

[0890] Next, the received Earth observation data is analyzed by an analysis tool that includes generative AI. This analysis tool includes generative AI models using deep learning frameworks such as TensorFlow and Keras. The generative AI model takes satellite image data as input and predicts the occurrence of climate change and natural disasters.

[0891] The analyzed data is classified into specific categories using a classification method. For example, it may be classified into data related to climate change or data related to natural disasters. This classification method is implemented using a database management system (DBMS).

[0892] The classified data is integrated by an integration mechanism to generate overall information. This integration mechanism is implemented using data integration tools or scripts.

[0893] Based on integrated data, the generation mechanism generates real-time information. This generation mechanism then generates information to be provided to the user based on the data analysis results.

[0894] The generated information is provided according to the user's search terms through a delivery method. This delivery method provides the information to the user via a web server or mobile application.

[0895] Furthermore, the system includes a prediction method that receives satellite image data and predicts the occurrence of climate change and natural disasters. This prediction method uses a generative AI model to predict the occurrence of climate change and natural disasters from satellite image data.

[0896] The predicted information will be notified to the user in real time via notification methods. These notification methods will be implemented using smartphone push notification and email notification functions.

[0897] As a concrete example, consider a scenario where a user launches an application on their smartphone, acquires the latest satellite imagery, and analyzes it. An example of a prompt to be input to the generative AI model is as follows:

[0898] "Analyze the latest satellite imagery to predict climate change and natural disaster occurrences in specific regions."

[0899] By inputting this prompt into the generating AI model, the model analyzes satellite images and outputs results that predict the occurrence of climate change and natural disasters.

[0900] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0901] Step 1:

[0902] The server receives Earth observation data. Specifically, it acquires image data transmitted from satellites via an internet connection. The input is image data from satellites, and the output is the received image data.

[0903] Step 2:

[0904] The server analyzes received Earth observation data using analytical methods, including generative AI. Specifically, it inputs image data into generative AI models using deep learning frameworks such as TensorFlow and Keras to predict the occurrence of climate change and natural disasters. The input is the received image data, and the output is the analysis results.

[0905] Step 3:

[0906] The server classifies the analyzed data using classification methods. Specifically, it uses a database management system (DBMS) to classify data into categories such as climate change data and natural disaster data. The input is the analysis results, and the output is the classified data.

[0907] Step 4:

[0908] The server integrates the classified data using integration tools. Specifically, it generates overall information using data integration tools and scripts. The input is classified data, and the output is integrated data.

[0909] Step 5:

[0910] The server generates real-time information using a generation mechanism based on integrated data. Specifically, it generates information to provide to the user based on the results of data analysis. The input is integrated data, and the output is the generated information.

[0911] Step 6:

[0912] The server provides the generated information to the user according to their search terms through various means. Specifically, it provides information to the user through a web server or mobile application. The input is the generated information and the user's search terms, and the output is the information provided to the user.

[0913] Step 7:

[0914] The server receives satellite image data and uses prediction tools to forecast the occurrence of climate change and natural disasters. Specifically, it uses a generative AI model to predict the occurrence of climate change and natural disasters from satellite image data. The input is satellite image data, and the output is the prediction result.

[0915] Step 8:

[0916] The server notifies the user in real time of the predicted information via notification methods. Specifically, this is implemented using smartphone push notification and email notification functions. The input is the prediction result, and the output is the information notified to the user.

[0917] (Example 3)

[0918] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0919] Conventional Earth observation data analysis systems often involved manual data analysis, classification, and integration, making real-time information provision difficult. Furthermore, the inability to quickly provide information based on user search terms meant users couldn't obtain the information they needed in a timely manner. This resulted in reduced user convenience and diminished the usefulness of the information.

[0920] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0921] In this invention, the server includes receiving means for receiving Earth observation data, analysis means including a generative AI model for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, and provision means for providing the generated information according to search terms. This automates data analysis, classification, and integration, enabling real-time information provision. Furthermore, it allows for the rapid provision of information according to the user's search terms, improving user convenience.

[0922] "Receiving means" refers to a device or function for receiving Earth observation data.

[0923] "Analysis means" refers to a device or function that includes a generative AI model for analyzing received Earth observation data.

[0924] A "generative AI model" is an artificial intelligence model used for natural language processing and data analysis.

[0925] "Classification means" refers to a device or function that classifies data analyzed by an analysis means into a specific category.

[0926] An "integration means" is a device or function that integrates classified data into a single set of information.

[0927] "Generation means" refers to a device or function that generates real-time information based on integrated data.

[0928] "Means of provision" refers to a device or function that provides generated information according to the user's search terms.

[0929] "Search terms" are keywords or phrases that users enter when searching for information.

[0930] "Real-time information" refers to information generated based on the current situation and the latest data.

[0931] This invention is a system that efficiently analyzes Earth observation data and provides real-time information tailored to the user's search terms. This system operates through the coordinated efforts of a server, a terminal, and the user.

[0932] First, the user enters a search term using a device (for example, a smartphone or computer). For example, they might enter "Tokyo weather." The device then sends this search term to the server.

[0933] The server receives Earth observation data using a receiving means. The received data is analyzed by an analysis means. This analysis uses a generative AI model (for example, a model using natural language processing technology). Specifically, a general natural language processing model can be used as the generative AI model.

[0934] The analyzed data is classified into specific categories using classification methods. For example, weather data is classified into categories such as "temperature," "probability of precipitation," and "wind speed." Machine learning algorithms (e.g., k-means clustering) are used for this classification.

[0935] The classified data is integrated into a single information set using an integration tool. A database management system (such as MySQL or Amazon Redshift) is used for this integration. This brings together related data into a single information set.

[0936] Based on integrated data, the generation mechanism generates real-time information. A generation AI model is used for this generation. For example, if a user searches for "Tokyo weather," the server generates the latest weather information for Tokyo based on the integrated weather data.

[0937] The generated information is provided to the user using the provided means. The user can check the latest weather information for Tokyo on their device screen. An example of a specific prompt message would be, "The user searched for 'Tokyo weather'. Please provide the latest weather information for Tokyo based on this search term."

[0938] This system allows users to efficiently obtain the information they need. Data analysis, classification, and integration are automated, enabling real-time information delivery. Furthermore, it can quickly provide information tailored to the user's search terms, improving user convenience. The specific processing flow in Example 3 will be explained using Figure 15.

[0939] Step 1:

[0940] The user enters a search term.

[0941] The user enters a search term using a terminal. For example, they might enter "Tokyo weather." This input data is sent from the terminal to the server. The input data is the search term, and the output is the search term sent to the server.

[0942] Step 2:

[0943] The server receives the data.

[0944] The server receives search terms sent from the terminal. Using the receiving mechanism, it also simultaneously receives Earth observation data. The input data consists of the search terms and Earth observation data, while the output is the data passed to the analysis mechanism.

[0945] Step 3:

[0946] The server analyzes the data.

[0947] The server analyzes the received Earth observation data using an analysis tool. A generative AI model is used for this analysis. Specifically, the generative AI model is prompted with a search term and extracts relevant data. The input data consists of Earth observation data and the search term, and the output is the analyzed data. As a concrete example, the server inputs the following prompt to the generative AI model: "The user searched for 'Tokyo weather.' Based on this search term, please provide the latest weather information for Tokyo."

[0948] Step 4:

[0949] The server classifies the data.

[0950] The server classifies the data analyzed by the analysis tools using classification tools. For example, it classifies weather data into categories such as "temperature," "probability of precipitation," and "wind speed." Machine learning algorithms are used for this classification. The input data is the analyzed data, and the output is the classified data. Specifically, the server stores the weather data separated into each category.

[0951] Step 5:

[0952] The server integrates the data.

[0953] The server integrates the classified data using an integration mechanism. For example, it uses a database management system to combine data from each category into a single information set. The input data is classified data, and the output is integrated data. Specifically, the server executes the following SQL query: "SELECT FROM weather_data WHERE location = 'Tokyo';"

[0954] Step 6:

[0955] The server generates real-time information.

[0956] The server generates real-time information based on integrated data. A generative AI model is used for this generation. The input data is integrated data, and the output is the generated real-time information. Specifically, the server inputs the following prompt to the generative AI model: "Generate the latest weather information for Tokyo based on the integrated weather data."

[0957] Step 7:

[0958] The server provides information to the user.

[0959] The server provides the generated information to the user using a delivery method. The user can check the latest weather information for Tokyo on their terminal screen. The input data is the generated real-time information, and the output is the information provided to the user. Specifically, the server sends the generated information to the terminal in HTML format and displays it in the user's browser.

[0960] (Application Example 3)

[0961] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0962] Conventional information provision systems struggle to provide users with searched information in real time, and there is a particular need to efficiently analyze, classify, and integrate large amounts of complex data, such as Earth observation data. Furthermore, there is a lack of technology to quickly generate and provide appropriate information in response to user search queries. As a result, it is not possible to provide users with the information they need in a timely manner, leading to problems with the accuracy and reliability of the information.

[0963] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0964] In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, a generating AI model for generating appropriate information based on the user's search query, and a prompt input means for inputting prompt sentences to the generating AI model to generate information. This makes it possible to provide the information searched by the user in real time, improving the accuracy and reliability of the information.

[0965] "Earth observation data" refers to data collected to observe various phenomena and environmental conditions on Earth.

[0966] "Receiving means" refers to devices or systems for receiving data from an external source.

[0967] "Analysis means" refers to devices or systems used to analyze received data.

[0968] "Generative AI" refers to a system that uses artificial intelligence technology to generate data.

[0969] A "classification tool" is a device or system used to classify analyzed data into specific categories.

[0970] An "integration tool" is a device or system used to combine classified data into a single entity.

[0971] A "generation means" is a device or system for generating new information based on integrated data.

[0972] "Means of provision" refers to devices or systems used to provide generated information to users.

[0973] A "generative AI model" is a model that uses artificial intelligence technology to generate appropriate information based on a user's search query.

[0974] A "prompt input means" is a device or system for inputting prompt text into a generative AI model to generate information.

[0975] A system for carrying out this invention has the following configuration: The server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, a generating AI model for generating appropriate information based on the user's search query, and a prompt input means for inputting prompt sentences to the generating AI model to generate information.

[0976] Hardware and software to use

[0977] Hardware: Servers, user terminals (smartphones, smart glasses)

[0978] Software: Generative AI models (e.g., GPT-4), data analysis tools (e.g., Pandas), cloud databases (e.g., Firebase)

[0979] Data processing and data calculation

[0980] 1. Data Collection: The server collects real-time data from cloud databases. For example, it retrieves data from weather information APIs and news APIs.

[0981] 2. Data Analysis: The server analyzes the collected data using Pandas and extracts the necessary information.

[0982] 3. Data Classification: The server classifies the analyzed data using a generative AI model (GPT-4). For example, it classifies the data into weather information, news, traffic information, etc.

[0983] 4. Data Integration: The server integrates the categorized data and generates relevant information in response to the user's search queries.

[0984] 5. Information Provision: The server generates real-time information based on integrated data and provides it to the user's terminal.

[0985] Specific example

[0986] For example, when a user searches for "Tokyo weather" on their smartphone, the system works as follows:

[0987] 1. Data Collection: The server obtains the latest weather data for Tokyo from a weather information API.

[0988] 2. Data Analysis: The server analyzes the acquired data using Pandas and extracts the necessary information.

[0989] 3. Data Classification: The server classifies the analyzed data as weather information using a generating AI model (GPT-4).

[0990] 4. Data Integration: The server integrates categorized weather information and generates relevant information in response to the user's search queries.

[0991] 5. Information Provision: The server provides the user's terminal with the latest weather information for Tokyo based on integrated data.

[0992] Example of a prompt

[0993] User's search query: "Tokyo weather"

[0994] Prompt to the generating AI model: "Please tell me the latest weather information for Tokyo."

[0995] When this prompt is input into the generating AI model, the model generates the latest weather information for Tokyo from the analyzed, classified, and integrated data and provides it to the user.

[0996] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0997] Step 1:

[0998] The server collects real-time data from a cloud database. Specifically, it retrieves data from weather information APIs and news APIs. The input for this step is data from the APIs, and the output is the retrieved raw data.

[0999] Step 2:

[1000] The server analyzes the collected data using Pandas and extracts the necessary information. Specifically, it cleans and preprocesses the data and converts it into a format that can be analyzed. The input for this step is the raw data obtained in step 1, and the output is the analyzed data.

[1001] Step 3:

[1002] The server classifies the analyzed data using a generative AI model (GPT-4). Specifically, it classifies the data into categories such as weather information, news, and traffic information. The input for this step is the data analyzed in step 2, and the output is the classified data.

[1003] Step 4:

[1004] The server integrates the classified data and generates appropriate information in response to the user's search query. Specifically, it combines related data and generates information corresponding to the user's search query. The input for this step is the data classified in step 3 and the user's search query, and the output is the integrated information.

[1005] Step 5:

[1006] The server generates real-time information based on the integrated data and provides it to the user terminal. Specifically, it inputs prompt messages into the generation AI model and provides the generated information to the user. The input for this step is the information integrated in step 4 and the prompt messages, and the output is the final information provided to the user.

[1007] Specific example

[1008] User's search query: "Tokyo weather"

[1009] Prompt to the generating AI model: "Please tell me the latest weather information for Tokyo."

[1010] When this prompt is input into the generating AI model, the model generates the latest weather information for Tokyo from the analyzed, classified, and integrated data and provides it to the user.

[1011] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1012] "Example of form 1"

[1013] In one embodiment of the present invention, the system includes receiving means for receiving Earth observation data, analysis means including a generating AI for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, and providing means for providing the generated information according to search words. Furthermore, it also includes an emotion engine for recognizing the user's emotions.

[1014] "Example of form 2"

[1015] The emotion engine recognizes emotions from, for example, the user's tone of voice, facial expressions, and text input. This emotion engine then adjusts the information generated based on the user's emotions. For example, if the user is expressing joy, the system will prioritize generating positive information to match that emotion.

[1016] "Example of form 3"

[1017] The delivery method provides users with information adjusted by an emotion engine. For example, if a user is expressing sadness, the system will respond to that emotion by providing comforting words or cheerful news. This enables the delivery of information that is tailored to the user's emotions.

[1018] The following describes the processing flow for each example of the form.

[1019] "Example of form 1"

[1020] Step 1: The system receives Earth observation data.

[1021] Step 2: Analyze the received Earth observation data using a generative AI.

[1022] Step 3: Classify the analyzed data.

[1023] Step 4: Integrate the classified data.

[1024] Step 5: Generate real-time information based on integrated data.

[1025] Step 6: Provide the generated information according to the search terms.

[1026] Step 7: Recognize the user's emotions using the emotion engine.

[1027] "Example of form 2"

[1028] Step 1: The emotion engine recognizes emotions from the user's tone of voice, facial expressions, and text input.

[1029] Step 2: The emotion engine adjusts the information generated based on the user's emotions.

[1030] Step 3: If the user is expressing feelings of joy, the system prioritizes generating positive information that aligns with those feelings.

[1031] "Example of form 3"

[1032] Step 1: Provide users with information adjusted by the emotion engine.

[1033] Step 2: If the user is showing sadness, the system responds to that emotion by providing, for example, words of comfort or happy news.

[1034] (Example 1)

[1035] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1036] Conventional Earth observation data analysis systems have difficulty providing real-time data analysis and information, and they have not been able to provide information that is tailored to the user's emotions. Therefore, in situations where rapid and appropriate information provision is required, they have been unable to provide information that meets the user's needs.

[1037] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1038] In this invention, the server includes receiving means for receiving Earth observation data, analysis means including a generative AI model for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, provision means for providing the generated information according to search words, and an emotion engine for recognizing the user's emotions. This enables real-time data analysis and information provision, and further enables the provision of appropriate information according to the user's emotions.

[1039] "Earth observation data" refers to data about the Earth's surface and atmosphere obtained from Earth observation satellites.

[1040] "Receiving means" refers to devices and software for receiving Earth observation data transmitted from Earth observation satellites in real time.

[1041] A "generative AI model" is an artificial intelligence model used to analyze received Earth observation data, performing pattern recognition and anomaly detection on the data.

[1042] "Analysis means" refers to devices and software used to analyze Earth observation data received using a generated AI model.

[1043] A "classification means" is a device or software used to classify data analyzed by an analysis means into different categories.

[1044] An "integration tool" is a device or software used to integrate classified data and combine it into a single dataset.

[1045] "Generation means" refers to devices or software for generating real-time information based on integrated data.

[1046] "Means of provision" refers to devices or software that provide generated information to users according to their search terms.

[1047] An "emotion engine" is a device or software that recognizes a user's emotions and adjusts the information it provides accordingly.

[1048] Modes for carrying out the invention

[1049] This invention is a system that receives, analyzes, classifies, integrates, generates, and provides Earth observation data transmitted from Earth observation satellites in real time. Furthermore, it also has a function to recognize the user's emotions and adjust the information provided.

[1050] Receiving means

[1051] The server receives Earth observation data transmitted from Earth observation satellites in real time. Specifically, the server acquires data using a high-performance antenna and dedicated receiving software (e.g., SatReceiver Pro). For example, the server receives 10 MB of data per second and temporarily stores it in storage.

[1052] Analysis means

[1053] The server uses a generative AI model (e.g., GPT-4) to analyze the received Earth observation data. First, the server preprocesses the data to remove noise. Then, it uses the generative AI model to perform pattern recognition and anomaly detection on the data. For example, the server analyzes satellite image data to detect signs of forest fires.

[1054] Classification means

[1055] The server classifies the analyzed data. Specifically, the server uses machine learning algorithms (e.g., Scikit-learn) to classify the data into different categories (e.g., weather data, topographic data, environmental data). For example, the server classifies weather data into subcategories such as temperature, humidity, and wind speed.

[1056] Integration means

[1057] The server integrates the classified data. For example, the server combines weather data and topographic data to predict flood risk. A database management system (e.g., PostgreSQL) is used for this integration. The server stores the integrated data as a single dataset and uses it for subsequent processing.

[1058] generation means

[1059] The server generates real-time information based on integrated data. For example, the server generates warning messages based on flood risk predictions. This generation uses natural language generation tools (e.g., OpenAI's GPT-4). The server temporarily stores the generated information in a cache and prepares it for immediate provision.

[1060] Providing means

[1061] The server provides generated information based on the search terms. The user enters the search terms using their terminal, and the server provides relevant information based on those terms. For example, if a user searches for "flood risk," the server provides the latest flood risk information. The server sends the search results to the user's terminal, and the user views them.

[1062] Emotional Engine

[1063] The server uses an emotion engine (e.g., Affectiva) to recognize the user's emotions. When a user searches for information through their device, the server analyzes the user's emotions from their facial expressions and voice, and adjusts the information provided accordingly. For example, if the user has an anxious expression, the server will provide more detailed explanations or reassuring information. The server analyzes emotion data in real time and reflects this in the information provided.

[1064] Specific examples and prompt statements

[1065] Specific example

[1066] If a user wants to know about flood risk, they can obtain the information by following these steps:

[1067] 1. The user uses their device to search for "flood risk".

[1068] 2. The server analyzes Earth observation data and predicts flood risk.

[1069] 3. The server classifies the analysis results and integrates the weather data and topographic data.

[1070] 4. The server generates flood risk alert messages based on the integrated data.

[1071] 5. The server analyzes the user's emotions and adjusts the information as needed.

[1072] 6. The server provides the final information to the user.

[1073] Example of a prompt

[1074] "Please provide the latest information on flood risk."

[1075] By inputting this prompt into the AI ​​model, the server generates and provides real-time flood risk information to the user.

[1076] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1077] Step 1:

[1078] The server receives Earth observation data transmitted from Earth observation satellites. Specifically, the server acquires data using a high-performance antenna and dedicated receiving software. The input is data from Earth observation satellites, and the output is raw data temporarily stored in storage. For example, the server receives 10 MB of data per second and temporarily stores it in storage.

[1079] Step 2:

[1080] The server uses a generative AI model to analyze the received Earth observation data. The input is the raw data received in step 1, and the output is the analyzed data. Specifically, the server first preprocesses the data to remove noise. Then, it uses the generative AI model to perform pattern recognition and anomaly detection on the data. For example, the server analyzes satellite image data to detect signs of forest fires.

[1081] Step 3:

[1082] The server classifies the analyzed data. The input is the data analyzed in step 2, and the output is the classified data. Specifically, the server uses machine learning algorithms to classify the data into different categories. For example, the server classifies weather data into subcategories such as temperature, humidity, and wind speed.

[1083] Step 4:

[1084] The server integrates the classified data. The input is the data classified in step 3, and the output is the integrated data. Specifically, the server combines weather data and topographic data to predict flood risk. A database management system is used for this integration. The server stores the integrated data as a single dataset and uses it for subsequent processing.

[1085] Step 5:

[1086] The server generates real-time information based on the integrated data. The input is the data integrated in step 4, and the output is the generated information. Specifically, the server generates warning messages based on flood risk predictions. A natural language generation tool is used for this generation. The server temporarily stores the generated information in a cache and prepares it for immediate provision.

[1087] Step 6:

[1088] The server provides generated information based on the search terms. The input is the search terms entered by the user, and the output is the information as search results. The user enters the search terms using a terminal, and the server provides relevant information based on those search terms. For example, if the user searches for "flood risk," the server will provide the latest flood risk information. The server sends the search results to the user's terminal, and the user views them.

[1089] Step 7:

[1090] The server uses an emotion engine to recognize the user's emotions. Input is the user's facial expressions and voice data, and output is the analyzed emotion data. When a user searches for information through their device, the server analyzes their emotions from their facial expressions and voice and adjusts the information provided accordingly. For example, if the user has an anxious expression, the server will provide more detailed explanations or reassuring information. The server analyzes the emotion data in real time and reflects this in the information provided.

[1091] (Application Example 1)

[1092] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1093] In operating autonomous vehicles, it is necessary to grasp environmental information such as weather and traffic conditions in real time and select appropriate routes. However, conventional systems have had difficulty efficiently analyzing data from Earth observation satellites and providing it in real time. Furthermore, because information is not provided with consideration for user emotions, there have been challenges in ensuring sufficient safety and efficiency of operation.

[1094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, an emotion engine for recognizing the user's emotions, and a real-time environment monitoring means for supporting the operation of an autonomous vehicle. This makes it possible to analyze data from Earth observation satellites in real time and provide appropriate operation information that takes the user's emotions into consideration.

[1095] "Earth observation data" refers to data about the state of the Earth's surface and atmosphere transmitted from Earth observation satellites.

[1096] "Receiving means" refers to devices or systems for receiving Earth observation data transmitted from Earth observation satellites.

[1097] "Analysis means" refers to devices or systems, including generating AI, for analyzing received Earth observation data.

[1098] "Generative AI" is an artificial intelligence technology that analyzes received data and generates specific information.

[1099] A "classification means" is a device or system that classifies data analyzed by an analysis means into a specific category.

[1100] An "integration tool" is a device or system that integrates classified data and combines it into a single piece of information.

[1101] A "generation means" refers to a device or system that generates real-time information based on integrated data.

[1102] "Means of provision" refers to devices or systems that provide generated information according to search terms.

[1103] An "emotion engine" is a device or system that recognizes a user's emotions and provides information that corresponds to those emotions.

[1104] A "real-time environmental monitoring means" refers to a device or system that monitors environmental information in real time in order to support the operation of autonomous vehicles.

[1105] A system for carrying out this invention includes receiving means for receiving Earth observation data, analysis means including generating AI for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, providing means for providing the generated information according to search words, an emotion engine for recognizing the user's emotions, and real-time environmental monitoring means for supporting the operation of an autonomous vehicle.

[1106] Hardware and software to use

[1107] hardware

[1108] Onboard computer for autonomous vehicles: Performs data processing and analysis.

[1109] Antenna that receives data from Earth observation satellites: Receives Earth observation data.

[1110] software

[1111] SatelliteDataReceiver: A module that receives data from Earth observation satellites.

[1112] AIAnalyzer: A generative AI model that analyzes received data.

[1113] DataClassifier: A module for classifying analytical data.

[1114] DataIntegrator: A module for integrating classification data.

[1115] RealTimeInfoGenerator: A module that generates real-time information based on integrated data.

[1116] EmotionEngine: A module that recognizes the user's emotions.

[1117] Data processing and calculations

[1118] The server first receives data from Earth observation satellites in real time. The received data is analyzed using a generative AI model. The analyzed data is classified into specific categories and then integrated into a single piece of information. Based on this integrated data, real-time operational information is generated. Furthermore, it recognizes the user's emotions and provides information tailored to those emotions.

[1119] Specific example

[1120] For example, if the weather is deteriorating, the server will notify you with "Warning: Weather is deteriorating. Please change your route." On the other hand, if the weather is good, it will notify you with "The current route is safe."

[1121] Example of a prompt

[1122] Create a program that receives data from Earth observation satellites, analyzes weather information, and provides operational information tailored to the user's emotions. If the user is feeling anxious, issue a warning; if the weather is good, notify them that it is safe.

[1123] In this way, it becomes possible to analyze data from Earth observation satellites in real time and provide appropriate operational information that takes into account the user's feelings.

[1124] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1125] Step 1:

[1126] The server receives data from Earth observation satellites. The input is Earth observation data transmitted from the satellites, and the output is the received raw data. Specifically, the server uses an antenna to catch signals from the satellites and receives the data.

[1127] Step 2:

[1128] The server analyzes received Earth observation data using a generating AI model. The input is the received raw data, and the output is the analyzed data. Specifically, the server starts the AIAnalyzer module and inputs prompts for data analysis into the generating AI model.

[1129] Step 3:

[1130] The server classifies the analyzed data into specific categories using classification tools. The input is the analyzed data, and the output is the classified data. Specifically, the server uses the DataClassifier module to classify the data into categories such as weather information, traffic information, and road conditions.

[1131] Step 4:

[1132] The server combines classified data into a single piece of information using an integration mechanism. The input is classified data, and the output is integrated data. Specifically, the server uses the DataIntegrator module to integrate data from each category and generate overall environmental information.

[1133] Step 5:

[1134] The server generates real-time operational information based on integrated data. The input is integrated data, and the output is real-time operational information. Specifically, the server uses the RealTimeInfoGenerator module to generate information such as route optimization and warnings.

[1135] Step 6:

[1136] The server analyzes the user's emotions using an emotion engine that recognizes user emotions. The input is the user's emotion data, and the output is the analyzed emotion information. Specifically, the server uses the EmotionEngine module to analyze the user's facial expressions and voice data to recognize emotions.

[1137] Step 7:

[1138] The server provides appropriate information based on generated operational information and user sentiment information. Inputs are real-time operational information and analyzed sentiment information, while output is optimal operational information provided to the user. Specifically, the server uses a delivery system to notify the user of information such as changes to the operational route or warnings.

[1139] (Example 2)

[1140] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1141] Conventional Earth observation data analysis systems often lacked sufficient preprocessing and analysis of received data, making real-time information generation difficult. Furthermore, they lacked the ability to adjust information based on user sentiment, failing to provide users with appropriate information. This resulted in decreased user satisfaction and limited the system's usefulness.

[1142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1143] In this invention, the server includes receiving means for receiving Earth observation data, preprocessing means for preprocessing the received Earth observation data, analysis means including a generating AI for analyzing the preprocessed Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, providing means for providing the generated information according to search words, collection means for collecting the user's voice and facial expressions, analysis means for analyzing the collected voice and facial expressions, emotion recognition means for recognizing the user's emotions from the analysis results, and information generation means for generating and displaying information based on the recognized emotions. This enables highly accurate analysis of Earth observation data and the provision of information based on the user's emotions.

[1144] "Earth observation data" refers to data collected to observe the conditions of the Earth's surface, atmosphere, oceans, and other natural features.

[1145] "Receiving means" refers to devices or functions for receiving Earth observation data from an external source.

[1146] "Preprocessing means" refers to devices or functions that perform preprocessing, such as noise reduction and normalization, on received Earth observation data to make it easier to analyze.

[1147] "Generative AI" refers to algorithms and models that use artificial intelligence technologies such as deep learning to analyze data and detect specific patterns or anomalies.

[1148] "Analysis means" refers to devices or functions that analyze Earth observation data preprocessed using generative AI.

[1149] A "classification means" is a device or function that classifies data analyzed by an analysis means into a specific category or class.

[1150] An "integration tool" is a device or function that integrates classified data and processes it as a single, cohesive piece of information.

[1151] "Generation means" refers to devices or functions that generate real-time information based on integrated data.

[1152] "Means of provision" refers to devices or functions that provide generated information according to the user's search terms.

[1153] "Collection means" refers to devices or functions used to collect the user's voice and facial expressions.

[1154] "Emotion recognition means" refers to devices or functions that analyze collected audio and facial expressions to recognize the user's emotions.

[1155] "Information generation means" refers to devices or functions that generate information based on recognized emotions and display it to the user.

[1156] This invention relates to a system that analyzes Earth observation data and provides information based on the user's emotions. Specific embodiments of this system are described below.

[1157] Analysis of Earth observation data

[1158] The server has a receiving mechanism for receiving Earth observation data. This receiving mechanism uses a satellite communication module to receive data from Earth observation satellites. The received data is stored in a specified directory.

[1159] Next, the server uses preprocessing tools to preprocess the received data. Preprocessing includes reading the data using the Python Pandas library and imputing missing values. For image data, denoising is performed using OpenCV.

[1160] The preprocessed data is input into an analysis tool that includes a generative AI. The generative AI is built using deep learning frameworks such as TensorFlow and PyTorch. The server loads a TensorFlow model and inputs the preprocessed data. The model detects climate change and natural disaster occurrences in specific regions.

[1161] Data analyzed by the analysis means is classified into specific categories or classes by the classification means. The classified data is integrated by the integration means and processed as a single, cohesive piece of information. Based on the integrated data, the generation means generates real-time information. The generated information is provided by the provision means according to the user's search terms.

[1162] User emotion recognition

[1163] The device has a means of collecting the user's voice and facial expressions. Voice is collected via the microphone, and facial expressions are collected via the camera. The collected data is temporarily stored in the device's memory.

[1164] Next, the device uses analysis tools to analyze the collected audio and facial expressions. Google Cloud Speech-to-Text is used for audio analysis, and OpenCV and Dlib are used for facial expression analysis. The device calls the Google Cloud Speech-to-Text API to convert audio to text. OpenCV and Dlib are used to analyze the user's emotions from their facial expressions.

[1165] Based on the analysis results, the device recognizes the user's emotions using emotion recognition mechanisms. The emotion engine then classifies the user's emotions into categories such as "joy," "sadness," and "anger" based on the analysis results.

[1166] Based on recognized emotions, the device uses information generation mechanisms to generate and display information to the user. For example, if the user says, "I'm very happy today," the device generates positive news or encouraging messages. The generated information is displayed on the device's screen.

[1167] Specific example

[1168] The server uses image data acquired from NASA's MODIS satellite. The generating AI analyzes the image data using deep learning frameworks such as TensorFlow and PyTorch. As a result of the analysis, it detects abnormal temperature increases and flood occurrences in specific regions.

[1169] Example of a prompt:

[1170] "Analyze the latest image data acquired from NASA's MODIS satellite to detect climate change and natural disaster occurrences in specific regions."

[1171] The device collects the user's voice using a microphone and converts it to text using speech recognition software (e.g., Google Cloud Speech-to-Text). The emotion engine analyzes the user's facial expressions using libraries such as OpenCV and Dlib. If the user says, "I'm very happy today," the device displays positive news or encouraging messages.

[1172] Example of a prompt:

[1173] "Analyze the user's voice and facial expressions, and prioritize generating positive information if the user is showing positive emotions."

[1174] In this way, this system enables highly accurate analysis of Earth observation data and provides information based on the user's emotions.

[1175] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1176] Step 1:

[1177] The server receives Earth observation data. Using the receiving means, it receives data from Earth observation satellites via a satellite communication module. The received data is saved to a specified directory.

[1178] Input: Data from Earth observation satellites

[1179] Output: Saved Earth observation data file

[1180] Step 2:

[1181] The server preprocesses the received Earth observation data. Using preprocessing tools, it reads the data using the Python Pandas library and imputes missing values. For image data, it performs denoising using OpenCV.

[1182] Input: Saved Earth observation data file

[1183] Output: Preprocessed Earth observation data

[1184] Step 3:

[1185] The server inputs preprocessed data into a generative AI model for analysis. The generative AI model is built using TensorFlow or PyTorch. The server loads the TensorFlow model and inputs the preprocessed data. The model detects climate change and natural disaster occurrences in specific regions.

[1186] Input: Preprocessed Earth observation data

[1187] Output: Analysis results (detection results for climate change and natural disasters)

[1188] Step 4:

[1189] The server classifies the data analyzed by the analysis means. It uses classification means to categorize the analysis results into specific categories or classes.

[1190] Input: Analysis results

[1191] Output: Classified data

[1192] Step 5:

[1193] The server integrates the classified data. Using integration methods, it processes the classified data as a single, cohesive piece of information.

[1194] Input: Classified data

[1195] Output: Integrated data

[1196] Step 6:

[1197] The server generates real-time information based on integrated data. It uses generation means to generate information based on integrated data.

[1198] Input: Integrated data

[1199] Output: Generated real-time information

[1200] Step 7:

[1201] The server provides generated information according to the search terms. It uses a delivery method to provide information based on the user's search terms.

[1202] Input: Generated real-time information, user search terms

[1203] Output: Provided information

[1204] Step 8:

[1205] The device collects the user's voice and facial expressions. It uses a collection method to capture the user's voice and facial expressions through the microphone and camera. The collected data is temporarily stored in the device's memory.

[1206] Input: User's voice and facial expressions

[1207] Output: Collected audio and facial expression data

[1208] Step 9:

[1209] The device analyzes the collected audio and facial expressions. Using analysis tools, it employs Google Cloud Speech-to-Text for audio analysis and OpenCV and Dlib for facial expression analysis. The device calls the Google Cloud Speech-to-Text API to convert audio to text. It then uses OpenCV and Dlib to analyze the user's emotions from their facial expressions.

[1210] Input: Collected audio and facial expression data

[1211] Output: Analysis results (text conversion results of speech, facial expression analysis results)

[1212] Step 10:

[1213] The device recognizes the user's emotions from the analysis results. Using emotion recognition technology, it classifies the user's emotions based on the analysis results.

[1214] Input: Analysis results

[1215] Output: Recognized emotions

[1216] Step 11:

[1217] The device generates and displays information based on recognized emotions. Using information generation methods, it generates information tailored to the user's emotions and displays it on the device's screen. For example, if the user says, "I'm very happy today," it generates positive news or encouraging messages.

[1218] Input: Recognized emotions

[1219] Output: Generated information, displayed information

[1220] (Application Example 2)

[1221] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1222] While conventional Earth observation data analysis systems have shown some effectiveness in detecting climate change and natural disasters, they lack the ability to adjust information based on user emotions, making it difficult to provide appropriate warnings and advice to users. Furthermore, providing information without considering user emotions can potentially increase user anxiety.

[1223] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1224] In this invention, the server includes receiving means for receiving Earth observation data, analysis means including generating AI for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, emotion recognition means for recognizing the user's emotions, adjustment means for adjusting the information generated based on the emotions recognized by the emotion recognition means, and providing means for providing the generated information according to search words. This makes it possible to provide appropriate warnings and advice according to the user's emotions.

[1225] "Earth observation data" refers to data recorded using satellites and other observation instruments to document the state of the Earth's surface, atmosphere, oceans, and other features.

[1226] "Receiving means" refers to devices or systems for receiving Earth observation data.

[1227] "Analysis means" refers to devices or systems, including generating AI, for analyzing received Earth observation data.

[1228] "Generative AI" is artificial intelligence that uses AI technologies such as deep learning to analyze data and generate new information.

[1229] A "classification means" is a device or system that classifies data analyzed by an analysis means into a specific category.

[1230] An "integration tool" is a device or system that combines classified data into a single integrated dataset.

[1231] A "generation means" refers to a device or system that generates real-time information based on integrated data.

[1232] "Emotion recognition means" refers to devices or systems that recognize emotions from a user's voice tone, facial expressions, or text input.

[1233] "Adjustment means" refers to devices or systems that adjust information generated based on emotions recognized by emotion recognition means.

[1234] "Means of provision" refers to devices or systems that provide generated information to users according to their search terms.

[1235] A system for carrying out this invention includes a series of processes for receiving and analyzing Earth observation data and tailoring and providing information based on the user's emotions. Specific embodiments thereof are described below.

[1236] Hardware and software to be used

[1237] Hardware: Smartphone

[1238] Software: TensorFlow, OpenCV, Transformers (Hugging Face)

[1239] System Configuration

[1240] 1. Receiving method:

[1241] The server receives Earth observation data from satellites and other observation instruments. This includes satellite imagery and weather data.

[1242] 2. Analysis method:

[1243] The server uses generative AI to analyze the received Earth observation data. This generative AI is a deep learning model using TensorFlow and predicts the risks of climate change and natural disasters.

[1244] 3. Classification means:

[1245] The server categorizes the analyzed data into specific categories, such as climate change, floods, and earthquakes.

[1246] 4. Integration methods:

[1247] The server combines the classified data into a single, integrated dataset. This centralizes information from multiple data sources.

[1248] 5. Generation means:

[1249] The server generates real-time information based on integrated data, including information on current weather conditions and natural disaster risks.

[1250] 6. Emotion recognition means:

[1251] The device (smartphone) recognizes emotions from the user's voice tone, facial expressions, and text input. This uses an emotion recognition model based on Transformers.

[1252] 7. Adjustment means:

[1253] The server adjusts the information generated based on the emotions recognized by the emotion recognition system. For example, if a user is showing signs of anxiety, it prioritizes providing information that provides a sense of security.

[1254] 8. Means of provision:

[1255] The device provides the user with generated information based on their search terms. This allows the user to quickly obtain the information they need.

[1256] Specific example

[1257] Example of a prompt

[1258] User text input: "I'm worried about the recent weather."

[1259] Satellite image path: "satellite_image.jpg"

[1260] Using this prompt, the system recognizes the user's emotions and analyzes Earth observation data to provide appropriate information. For example, if the user enters "I'm worried about the recent weather," the system recognizes this emotion and analyzes the latest satellite imagery to provide current weather conditions. If the user indicates anxiety, the system prioritizes providing reassuring information.

[1261] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1262] Step 1:

[1263] The server receives Earth observation data from satellites and other observation instruments. Inputs include satellite imagery and weather data, while output is the received raw data. Specifically, the server periodically retrieves data from observation instruments and stores it in a database.

[1264] Step 2:

[1265] The server uses generative AI to analyze the received Earth observation data. The input is the raw data received in step 1, and the output is the analyzed data. Specifically, the server runs a deep learning model using TensorFlow to predict the risks of climate change and natural disasters.

[1266] Step 3:

[1267] The server classifies the analyzed data into specific categories. The input is the data analyzed in step 2, and the output is the classified data. Specifically, the server assigns the analysis results to categories such as "climate change," "floods," and "earthquakes."

[1268] Step 4:

[1269] The server combines the classified data into a single integrated dataset. The input is the data classified in step 3, and the output is the integrated data. Specifically, the server centralizes information from multiple data sources and generates an integrated dataset.

[1270] Step 5:

[1271] The server generates real-time information based on the integrated data. The input is the data integrated in step 4, and the output is the generated real-time information. Specifically, the server analyzes the integrated data and generates information about current weather conditions and natural disaster risks.

[1272] Step 6:

[1273] The device recognizes emotions from the user's voice tone, facial expressions, and text input. The input is the user's voice and text data, and the output is the recognized emotion data. Specifically, the device runs an emotion recognition model using Transformers to analyze the user's emotions.

[1274] Step 7:

[1275] The server adjusts the information generated based on the emotions recognized by the emotion recognition system. The input is the emotion data recognized in step 6 and the real-time information generated in step 5, and the output is the adjusted information. Specifically, the server changes the priority of information according to the user's emotions and generates appropriate warnings and advice.

[1276] Step 8:

[1277] The terminal provides the user with generated information according to the search terms. The input is the information adjusted in step 7 and the user's search terms, and the output is the information provided to the user. Specifically, the terminal filters the information based on the user's search terms and displays the appropriate information.

[1278] (Example 3)

[1279] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[1280] Conventional Earth observation data analysis systems have complex processes for analyzing, classifying, and integrating received data, making real-time information provision difficult. Furthermore, they have a problem with not being able to provide information that responds to user emotions, resulting in a poor user experience.

[1281] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1282] In this invention, the server includes receiving means for receiving Earth observation data, analysis means including a generation AI model for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, providing means for providing the generated information according to search words, and an emotion engine in which the providing means provides information corresponding to the user's emotions. This enables real-time information provision and further enables information provision that corresponds to the user's emotions.

[1283] "Receiving means" refers to a device or system for receiving Earth observation data.

[1284] "Analysis means" refers to a device or system that includes a generative AI model for analyzing received Earth observation data.

[1285] A "generative AI model" is an artificial intelligence model used for natural language processing and data analysis.

[1286] A "classification means" is a device or system that classifies data analyzed by an analysis means into categories.

[1287] An "integration tool" is a device or system that integrates classified data and combines it into a single piece of information.

[1288] "Generation means" refers to a device or system that generates real-time information based on integrated data.

[1289] "Means of provision" refers to a device or system that provides generated information according to the user's search terms.

[1290] An "emotion engine" is a device or system that analyzes a user's emotions and provides information corresponding to those emotions.

[1291] Modes for carrying out the invention

[1292] This invention relates to a system that receives, analyzes, classifies, and integrates Earth observation data, generates real-time information, and provides information that responds to the user's emotions. Specific embodiments of this system are described below.

[1293] 1. Receiving data

[1294] The server is equipped with receiving means for receiving Earth observation data. This receiving means is a device that receives data via satellite communication or the internet. For example, it receives weather data and environmental data transmitted from Earth observation satellites.

[1295] 2. Data Analysis

[1296] The server is equipped with analysis means, including a generative AI model for analyzing received Earth observation data. This analysis means includes a generative AI model using natural language processing techniques (e.g., OpenAI's GPT-4). The server analyzes the received data and extracts relevant information.

[1297] 3. Data Classification

[1298] The server is equipped with a classification mechanism for classifying the analyzed data. This classification mechanism uses machine learning algorithms (e.g., Scikit-learn or TensorFlow). The server classifies the analyzed data into categories such as "temperature," "precipitation," and "wind speed."

[1299] 4. Data Integration

[1300] The server is equipped with an integration mechanism for combining classified data. This integration mechanism uses a database management system (e.g., MySQL or PostgreSQL). The server integrates temperature, precipitation, and wind speed data to generate comprehensive information.

[1301] 5. Generation of real-time information

[1302] The server is equipped with generation means for generating real-time information based on integrated data. This generation means uses a regenerative AI model. For example, if a user searches for "Tokyo weather," the server generates the latest information about Tokyo's weather from the integrated data and provides it to the user.

[1303] 6. Providing information that responds to emotions

[1304] The server has an emotion engine that provides information that corresponds to the user's emotions. This emotion engine uses emotion analysis tools (for example, IBM Watson's Tone Analyzer). For example, if the user is showing sad emotions, the server will provide words of comfort or cheerful news.

[1305] Specific example

[1306] Consider a scenario where a user searches for "Tokyo weather" and the system provides information based on that search term. If the user is expressing sadness, the system might provide a message such as, "The weather in Tokyo is sunny. It's going to be a great day. Cheer up!"

[1307] Example of a prompt

[1308] "If a user searches for 'Tokyo weather' and expresses sadness, please generate weather information that includes words of comfort."

[1309] In this way, we can explain specific embodiments of the system while clearly defining the roles of the server, terminal, and user. The flow of a specific process in Example 3 will be explained using Figure 21.

[1310] Step 1:

[1311] The user enters a search term using their terminal. For example, the user enters "Tokyo weather". The entered search term is sent to the server.

[1312] Step 2:

[1313] The server inputs the received search term into a generating AI model for analysis. Specifically, the server analyzes the search term "Tokyo weather" using natural language processing technology and extracts relevant information. The input is the search term, and the output is the analyzed information.

[1314] Step 3:

[1315] The server classifies the analyzed information into categories using classification tools. Specifically, the server uses machine learning algorithms to classify the analyzed information into categories such as "temperature," "precipitation," and "wind speed." The input is the analyzed information, and the output is the classified data.

[1316] Step 4:

[1317] The server integrates the classified data using integration means. Specifically, the server uses a database management system to integrate temperature, precipitation, and wind speed data to generate comprehensive information. The input is classified data, and the output is integrated data.

[1318] Step 5:

[1319] The server generates real-time information based on integrated data. Specifically, the server uses a regenerative AI model to generate information corresponding to the user's search terms. For example, if a user searches for "Tokyo weather," the server generates the latest information about Tokyo's weather from the integrated data and provides it to the user. The input is integrated data, and the output is the generated real-time information.

[1320] Step 6:

[1321] The server provides information that corresponds to the user's emotions using a delivery method. Specifically, the server uses an emotion engine to analyze the user's emotions and generates information that corresponds to those emotions. For example, if the user is expressing sadness, the server will provide words of comfort or cheerful news. The input is the user's emotional information, and the output is information that corresponds to those emotions.

[1322] (Application Example 3)

[1323] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1324] Traditional information delivery systems could provide information based on users' search terms, but they could not provide personalized information that responded to users' emotions. Therefore, they failed to provide information appropriate to the user's emotional state, resulting in a lack of improved user experience.

[1325] In Application Example 3, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, an emotion analysis means for analyzing the user's emotions, an adjustment means for adjusting the information based on the emotion data analyzed by the emotion analysis means, and a providing means for providing the adjusted information to the user. This makes it possible to provide personalized information not only based on the user's search words but also according to the user's emotional state.

[1326] "Receiving means" refers to devices or systems for receiving Earth observation data.

[1327] "Analysis means" refers to devices or systems, including generating AI, for analyzing received Earth observation data.

[1328] A "classification means" is a device or system used to classify data that has been analyzed by an analysis means.

[1329] An "integration tool" is a device or system for integrating classified data.

[1330] "Generation means" refers to devices or systems for generating real-time information based on integrated data.

[1331] "Means of provision" refers to devices or systems for providing generated information according to search terms.

[1332] "Emotional analysis tools" refer to devices or systems used to analyze a user's emotions.

[1333] "Adjustment means" refers to devices or systems for adjusting information based on emotional data analyzed by emotion analysis means.

[1334] The system for carrying out this invention is configured as follows.

[1335] The server is equipped with a receiving means for receiving Earth observation data. The receiving means is a device for receiving data transmitted from satellites and ground observation equipment. The received data is sent to an analysis means and analyzed using a generative AI model. The analysis means is software for extracting features from the data and extracting necessary information.

[1336] The analyzed data is classified by a classification means. The classification means includes algorithms for dividing data into categories according to its type and content. The classified data is then integrated by an integration means. The integration means is software for combining multiple data sets into a single piece of information.

[1337] Based on integrated data, the generation means generates real-time information. The generation means is software that uses a generation AI model to generate information required by the user. The generated information is provided to the user by the provision means. The provision means includes an interface for displaying information according to the user's search terms.

[1338] Furthermore, emotion analysis tools are used to analyze the user's emotions. These tools are software that uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice. The analyzed emotion data is then used to adjust the information using adjustment tools. These adjustment tools include algorithms for personalizing the information according to the user's emotions.

[1339] For example, if a user searches for "Tokyo weather" and expresses sadness, the system will provide information about Tokyo's weather along with positive news and encouraging words to comfort the user. An example of a prompt would be: "If the user searches for 'Tokyo weather' and expresses sadness, generate information about Tokyo's weather along with positive news and encouraging words to comfort the user."

[1340] In this way, it becomes possible to provide personalized information not only based on the user's search terms, but also in accordance with the user's emotional state.

[1341] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[1342] Step 1:

[1343] The user launches a smartphone application and enters a search term into the search bar. The entered search term is sent to the server. The input data is the search term, and the output data is the search term sent to the server.

[1344] Step 2:

[1345] The device's camera and microphone are used to capture the user's facial expressions and voice. The captured data is sent to a server. The input data is the user's facial expressions and voice, and the output data is the facial expressions and voice data sent to the server.

[1346] Step 3:

[1347] The server receives Earth observation data using a receiving device. The input data is Earth observation data, and the output data is the received Earth observation data.

[1348] Step 4:

[1349] The server analyzes the received Earth observation data using analytical tools. It extracts data features and necessary information using a generative AI model. The input data is the received Earth observation data, and the output data is the analyzed data.

[1350] Step 5:

[1351] The server classifies the analyzed data using classification methods. It divides the data into categories according to its type and content. The input data is the analyzed data, and the output data is the classified data.

[1352] Step 6:

[1353] The server integrates classified data using integration methods. It combines multiple data points into a single piece of information. The input data is classified data, and the output data is integrated data.

[1354] Step 7:

[1355] The server generates real-time information based on integrated data using generation methods. It uses a generation AI model to generate the information the user needs. The input data is integrated data, and the output data is the generated information.

[1356] Step 8:

[1357] The server analyzes the user's facial expressions and voice data using emotion analysis tools. It identifies the user's emotions using an emotion analysis algorithm. The input data consists of the user's facial expressions and voice, while the output data is the analyzed emotion data.

[1358] Step 9:

[1359] The server adjusts information based on sentiment data analyzed using adjustment mechanisms. It personalizes information according to the user's emotions. Input data consists of analyzed sentiment data and generated information, while output data is the adjusted information.

[1360] Step 10:

[1361] The server provides users with information that has been adjusted using the provided means. Information is displayed according to the user's search terms. Input data is adjusted information, while output data is the information provided to the user.

[1362] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1363] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1364] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1365] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1366] [Third Embodiment]

[1367] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1368] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1369] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1370] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1371] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1372] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1373] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1374] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1375] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1376] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1377] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1378] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[1379] "Example of form 1"

[1380] One embodiment of the present invention is a system equipped with a receiving means for receiving Earth observation data transmitted from an Earth observation satellite. This receiving means is capable of receiving data from the satellite in real time.

[1381] "Example of form 2"

[1382] Next, there is an analysis method that includes a generative AI for analyzing the received Earth observation data. This generative AI uses AI technologies such as deep learning to analyze the Earth observation data. For example, it can detect climate change or natural disasters in specific regions from satellite images.

[1383] "Example of form 3"

[1384] Furthermore, there are classification means for classifying the analyzed data and integration means for integrating the classified data. Through these means, the analyzed data is appropriately classified and integrated, making it possible to efficiently provide the information that users need.

[1385] "Example of form 4"

[1386] Finally, there is a means of providing information that generates real-time data based on integrated data and provides information generated according to the user's search terms. For example, if a user searches for "Tokyo weather," the system generates information about Tokyo's weather from data analyzed, classified, and integrated by a generation AI, and provides it to the user.

[1387] The following describes the processing flow for each example of the form.

[1388] "Example of form 1"

[1389] Step 1: Receive Earth observation data transmitted from Earth observation satellites. This reception is performed in real time using specific receiving equipment.

[1390] "Example of form 2"

[1391] Step 1: The received Earth observation data is analyzed by a generating AI. This analysis is performed using AI technologies such as deep learning, making it possible to detect climate change and natural disasters in specific regions from satellite images.

[1392] "Example of form 3"

[1393] Step 1: Classify the analyzed data. This classification is performed using a specific classification algorithm.

[1394] Step 2: Integrate the classified data. This integration is performed using a specific integration algorithm, ensuring that the analyzed data is appropriately classified and integrated.

[1395] "Example of form 4"

[1396] Step 1: Generate real-time information based on integrated data. This generation is performed using a specific generation algorithm.

[1397] Step 2: Provide information generated in response to the user's search terms. For example, if a user searches for "Tokyo weather," the system generates information about Tokyo's weather from data analyzed, classified, and integrated by the generating AI, and provides it to the user.

[1398] (Example 1)

[1399] Next, we will describe Embodiment 1 of Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1400] Conventional systems for receiving and analyzing Earth observation data were inefficient in real-time data reception and analysis, making it difficult to quickly provide users with the information they needed. Furthermore, the methods for storing and providing analyzed data were inadequate, resulting in delays for users accessing the data they required.

[1401] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means for analyzing the received Earth observation data, a storage means for storing the data analyzed by the analysis means, a providing means for providing the stored data, and an access means for users to access the data through the providing means. This makes it possible to receive data transmitted from Earth observation satellites in real time and to quickly store and provide the analyzed data.

[1402] "Receiving means" refers to devices or systems for receiving data transmitted from Earth observation satellites.

[1403] "Analysis means" refers to devices or systems used to analyze received Earth observation data and extract necessary information.

[1404] "Storage means" refers to devices or systems for saving analyzed data to a database or similar location.

[1405] "Means of provision" refers to devices or systems used to provide stored data to users or other systems.

[1406] "Access means" refers to devices or systems that allow users to access data stored through the provided means.

[1407] This invention relates to a system for receiving, analyzing, storing, and providing data transmitted from Earth observation satellites in real time. Specific embodiments of this system are described below.

[1408] The server uses a highly sensitive antenna and receiver to receive Earth observation data. This makes it possible to receive data transmitted from Earth observation satellites in real time. The received data is sent to the server via data receiving software.

[1409] Next, the server uses data analysis software to analyze the received data. This analysis process involves formatting, filtering, and extracting necessary information from the data. For example, in the case of weather data, information such as temperature, humidity, and wind speed is extracted.

[1410] The analyzed data is stored in a database on the server. When saving, metadata such as the data type and timestamp is also saved. This makes it easier to search for the data later.

[1411] The stored data is provided to users and other systems through the distribution channels. Users can access the API using their devices and retrieve the necessary data. For example, if a user wants to obtain weather data, they can send a request to the API and receive temperature, humidity, and wind speed data from the server.

[1412] As a concrete example, the process by which a server receives, analyzes, stores, and provides weather data transmitted from an Earth observation satellite is shown below.

[1413] Specific example: The server receives weather data transmitted from Earth observation satellites using a high-sensitivity antenna and receiver. The received data is sent to the server via data receiving software, and data analysis software extracts information such as temperature, humidity, and wind speed. The analyzed data is stored in a database, and users access the API using their terminals to retrieve the necessary data.

[1414] Example of a prompt:

[1415] Please create a program that receives weather data transmitted from Earth observation satellites in real time and extracts information such as temperature, humidity, and wind speed. The hardware to be used will be a high-sensitivity antenna and receiver, and the software will consist of data reception software and data analysis software. The analyzed data should be stored in a database and provided to users via an API.

[1416] In this way, a system can be built in which the server can efficiently receive, analyze, store, and provide data from Earth observation satellites.

[1417] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1418] Step 1: Prepare to receive data

[1419] The server installs and connects a high-sensitivity antenna and receiver to receive data from Earth observation satellites. Next, it starts the data receiving software and configures it to receive signals from the satellites. The input is the installation and connection of the high-sensitivity antenna and receiver, and the output is the startup and completion of the data receiving software configuration.

[1420] Step 2: Data Reception

[1421] The server uses a high-sensitivity antenna and receiver to receive data transmitted from Earth observation satellites in real time. The received data is transferred to the server via data receiving software. The input is data from Earth observation satellites, and the output is the received data. Specifically, the server adjusts the antenna and receiver to capture the optimal signal.

[1422] Step 3: Data Analysis

[1423] The server passes the received data to data analysis software and begins the analysis. This analysis process involves formatting, filtering, and extracting necessary information from the data. For example, in the case of weather data, information such as temperature, humidity, and wind speed is extracted. The input is the received data, and the output is the analyzed data. Specifically, the server runs the data analysis software and processes the data using various algorithms.

[1424] Step 4: Save Data

[1425] The server stores the analyzed data in a database. When saving, it also saves metadata such as the data type and timestamp. The input is the analyzed data, and the output is the data stored in the database. Specifically, the server connects to the database and inserts data using SQL queries.

[1426] Step 5: Data Provision

[1427] The server makes stored data accessible through an API to provide it to users and other systems. Users can access the API using their terminals and retrieve the necessary data. The input is the user's request, and the output is the provided data. Specifically, the server receives an API request, retrieves the necessary data from the database, and sends it back to the user.

[1428] In this way, the server will build a system that can efficiently receive, analyze, store, and provide data from Earth observation satellites.

[1429] (Application Example 1)

[1430] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1431] There is a need for a system that can use real-time data from Earth observation satellites to quickly detect natural disasters and suspicious activity, and issue appropriate warnings to users. However, conventional systems have problems with inefficient processes from data reception to analysis and warning issuance, and lack real-time capabilities. As a result, users have been unable to receive information at the appropriate time, making it difficult to respond quickly.

[1432] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1433] In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, and a classification means for classifying the data analyzed by the analysis means. This makes it possible to efficiently receive and analyze real-time data from Earth observation satellites, quickly detect natural disasters and suspicious activities, and issue warnings to users.

[1434] "Earth observation data" refers to data transmitted from Earth observation satellites that includes information about the Earth's surface and atmosphere.

[1435] "Receiving means" refers to a device or system for receiving Earth observation data transmitted from Earth observation satellites in real time.

[1436] "Analysis means" refers to a device or system that includes a generating AI for analyzing received Earth observation data.

[1437] "Generative AI" is an artificial intelligence technology that analyzes received data to detect specific patterns or anomalies.

[1438] A "classification means" is a device or system for classifying data analyzed by an analysis means into a specific category.

[1439] An "integration means" is a device or system for integrating classified data and generating overall information.

[1440] "Generation means" refers to a device or system for generating real-time information based on integrated data.

[1441] "Means of provision" refers to a device or system for providing generated information to a user according to the search terms.

[1442] A "warning device" is a device or system for issuing a warning to a user based on generated information.

[1443] The system for carrying out this invention is for receiving and analyzing Earth observation data transmitted from Earth observation satellites in real time and issuing warnings to users. Specific embodiments of the system are described below.

[1444] System Configuration

[1445] The system consists of the following main components:

[1446] 1. Receiving means: A device or system that receives Earth observation data transmitted from Earth observation satellites in real time. Specifically, this involves using a dedicated receiving antenna or data receiving server.

[1447] 2. Analysis means: A device or system including a generative AI for analyzing received Earth observation data. The data is analyzed using generative AI models such as Python or TensorFlow.

[1448] 3. Classification means: A device or system that classifies data analyzed by an analysis means into specific categories. This involves efficiently classifying data using a database management system (DBMS).

[1449] 4. Integration means: A device or system that integrates classified data and generates overall information. The integrated data is updated in real time.

[1450] 5. Generation means: A device or system that generates real-time information based on integrated data. The generated information is provided to the user.

[1451] 6. Means of delivery: A device or system that provides generated information to users according to their search terms. Information is provided through smartphone apps or web applications.

[1452] 7. Warning means: A device or system that issues a warning to the user based on the generated information. Warnings are issued using push notifications or email notifications.

[1453] Program processing

[1454] The server receives Earth observation data and analyzes it using a generative AI. The analyzed data is categorized and integrated into specific categories. Real-time information is generated based on the integrated data and provided to the user. Furthermore, warnings are issued to the user based on the generated information.

[1455] Hardware and software to be used

[1456] Hardware: Receiving antenna, data receiving server, smartphone

[1457] Software: Python, TensorFlow, Database Management Systems (DBMS), Smartphone Apps, Web Applications

[1458] Specific example

[1459] For example, data transmitted from Earth observation satellites is received, and a generative AI model is used to detect natural disasters (e.g., earthquakes, floods) and suspicious activities (e.g., forest fires, poaching). The detected information is provided to the user in real time, and warnings are issued as needed.

[1460] Examples of prompts for generative AI models

[1461] Create a Python program that analyzes real-time data from Earth observation satellites to detect natural disasters and suspicious activity. The program should retrieve data via an API and include a function to alert the user based on the analysis results.

[1462] In this way, the system can efficiently utilize Earth observation data and provide users with timely and appropriate information and warnings.

[1463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1464] Step 1:

[1465] The server receives Earth observation data transmitted from Earth observation satellites. Using receiving equipment, it acquires data through dedicated receiving antennas and data receiving servers. The input is data from Earth observation satellites, and the output is the received raw data.

[1466] Step 2:

[1467] The server analyzes received Earth observation data using a generating AI model. It uses Python and TensorFlow as analysis tools to detect patterns and anomalies in the data. The input is the received raw data, and the output is the analyzed data. Specific operations include data preprocessing, feature extraction, model input, and retrieval of analysis results.

[1468] Step 3:

[1469] The server classifies the analyzed data into specific categories. A database management system (DBMS) is used as the classification method to efficiently classify the analysis results. The input is the analyzed data, and the output is the classified data. Specific operations include inserting data into the database and classifying data through queries.

[1470] Step 4:

[1471] The server integrates classified data to generate overall information. Using integration methods, it updates data in real time and generates integrated information. The input is classified data, and the output is integrated data. Specific operations include data aggregation, statistical processing, and real-time updates.

[1472] Step 5:

[1473] The server generates real-time information based on integrated data. It uses generation methods to produce information for the user. The input is integrated data, and the output is generated information. Specific operations include data format conversion and information generation.

[1474] Step 6:

[1475] The server provides generated information to the user based on the search terms. It uses smartphone apps and web applications to deliver the information to the user. The input consists of the generated information and the user's search terms, while the output is the information provided to the user. Specific operations include receiving search terms, filtering information, and providing the information.

[1476] Step 7:

[1477] The server issues a warning to the user based on the generated information. It uses push notifications and email notifications as warning methods. The input is the generated information, and the output is the warning issued to the user. Specific operations include checking warning conditions, generating notifications, and sending notifications.

[1478] (Example 2)

[1479] Next, we will describe Example 2 of the morphological example. 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."

[1480] Conventional Earth observation data analysis systems often perform preprocessing and analysis of received data manually, resulting in inefficiency. Furthermore, the classification and integration of analysis results are insufficient, making real-time information provision difficult. Additionally, information based on user search terms may not be adequately provided.

[1481] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, a preprocessing means for preprocessing the received Earth observation data, an analysis means including a generation AI for analyzing the preprocessed data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, and a providing means for providing the generated information according to search words. This enables efficient preprocessing, analysis, classification, and integration of Earth observation data, and realizes real-time information provision. It also enables the provision of appropriate information based on search words from the user.

[1482] "Receiving means" refers to devices or functions for receiving Earth observation data from satellites or other data sources.

[1483] "Preprocessing means" refers to devices or functions that perform processing to convert received Earth observation data into a format suitable for analysis.

[1484] "Analysis means" refers to devices or functions, including generative AI, for analyzing pre-processed data.

[1485] "Generative AI" is artificial intelligence that uses deep learning technology to analyze Earth observation data and detect climate change and natural disasters in specific regions.

[1486] A "classification means" is a device or function that classifies data analyzed by an analysis means into specific categories or patterns.

[1487] An "integration tool" is a device or function that integrates classified data and generates overall information.

[1488] "Generation means" refers to devices or functions that generate real-time information based on integrated data.

[1489] "Means of provision" refers to devices or functions that provide generated information according to the user's search terms.

[1490] A "search term" is a keyword or phrase that a user enters to search for specific information.

[1491] Modes for carrying out the invention

[1492] This invention relates to a system for efficiently analyzing Earth observation data and providing real-time information. Specific embodiments of this system are described below.

[1493] 1. System Configuration

[1494] This system includes the following main means:

[1495] Receiving method: Earth observation data is received from satellites and other data sources.

[1496] Preprocessing means: Convert the received Earth observation data into a format suitable for analysis.

[1497] Analysis means: Includes a generative AI for analyzing preprocessed data.

[1498] Classification method: Data analyzed by the analysis method is classified into specific categories or patterns.

[1499] Integration method: Integrates classified data to generate overall information.

[1500] Generation method: Generates real-time information based on integrated data.

[1501] Delivery method: Generated information is provided according to the user's search terms.

[1502] 2. Hardware and software to be used

[1503] Server: A server with high-performance computing capabilities is used to receive, preprocess, analyze, classify, integrate, generate, and deliver data.

[1504] Generative AI Model: We use a generative AI model based on deep learning technology to analyze Earth observation data.

[1505] Database: Use a database to store analysis results and integrated data.

[1506] User terminal: The user uses a device (PC, smartphone, etc.) to enter search terms and view the analysis results.

[1507] 3. Specific examples

[1508] If a user wants to analyze climate change in a specific region, they should follow these steps.

[1509] 1. Data reception:

[1510] The user uploads satellite image data of the region they want to analyze to the server.

[1511] The server receives the uploaded data.

[1512] 2. Data preprocessing:

[1513] The server removes noise from the received image data and adjusts the resolution.

[1514] The server imputes missing values ​​and normalizes numerical data.

[1515] 3. Application of Generative AI Models:

[1516] The server inputs the pre-processed data into the generating AI model.

[1517] Generative AI models analyze data and detect climate change patterns in specific regions.

[1518] 4. Output of analysis results:

[1519] The server outputs the analysis results obtained from the generated AI model.

[1520] The user reviews the analysis results provided by the server and takes necessary countermeasures.

[1521] Example of a prompt

[1522] "To analyze climate change in a specific region, please use the following satellite imagery data. Preprocess the data and apply a generative AI model to detect climate change patterns."

[1523] In this way, the server, terminals, and users each play their respective roles, resulting in a system that enables efficient analysis of Earth observation data and real-time information provision.

[1524] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1525] Step 1: Data Received

[1526] The server receives Earth observation data from satellites and other data sources. Inputs include satellite image data and numerical data. Outputs are the received raw data.

[1527] Specific operation: The server receives data transmitted from the satellite and stores it in storage.

[1528] Step 2: Data Preprocessing

[1529] The server preprocesses the received Earth observation data. The input is the received raw data. The output is the preprocessed data.

[1530] Specific actions:

[1531] For image data: The server applies a noise reduction filter and adjusts the resolution.

[1532] For numerical data: The server imputes missing values ​​and normalizes the data.

[1533] Step 3: Applying the Generative AI Model

[1534] The server inputs pre-processed data into the generating AI model. The input is the pre-processed data. The output is the analysis result.

[1535] Specific operation: The server inputs pre-processed data into a generating AI model, which then analyzes the data to detect patterns in climate change and natural disasters.

[1536] Step 4: Data Classification

[1537] The server classifies the analysis results obtained from the generated AI model. The input is the analysis results. The output is the classified data.

[1538] Specific operation: The server applies an algorithm to classify the analysis results into specific categories or patterns.

[1539] Step 5: Data Integration

[1540] The server integrates the classified data. The input is the classified data. The output is the integrated data.

[1541] Specific operation: The server integrates the classified data and generates overall information.

[1542] Step 6: Information Generation

[1543] The server generates real-time information based on integrated data. The input is the integrated data. The output is the generated real-time information.

[1544] Specific operation: The server analyzes the integrated data and applies algorithms to generate real-time information.

[1545] Step 7: Information Provision

[1546] The server provides generated information according to the user's search terms. The input consists of the user's search terms and the generated real-time information. The output is the information provided to the user.

[1547] Specific operation: The server receives search terms from the user, extracts appropriate information based on those terms, and provides it to the user's terminal.

[1548] In this way, the server, terminals, and users each play their respective roles, resulting in a system that enables efficient analysis of Earth observation data and real-time information provision.

[1549] (Application Example 2)

[1550] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1551] Conventional Earth observation data analysis systems have struggled to predict climate change and natural disasters in real time and notify users quickly. Furthermore, they lacked the functionality to provide users with the information they needed based on search terms, making information retrieval cumbersome. Therefore, in today's world, where rapid responses to climate change and natural disasters are essential, a more efficient and rapid information delivery system is needed.

[1552] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, a prediction means for receiving satellite image data and predicting the occurrence of climate change and natural disasters, and a notification means for notifying the user of the predicted information in real time. This makes it possible to quickly predict the occurrence of climate change and natural disasters and notify the user in real time.

[1553] "Earth observation data" refers to data collected to observe the conditions of the Earth's surface, atmosphere, oceans, and other natural features.

[1554] "Receiving means" refers to devices or systems for receiving Earth observation data.

[1555] "Generative AI" refers to artificial intelligence models that use artificial intelligence technologies such as deep learning to analyze data and generate new information.

[1556] "Analysis means" refers to devices or systems used to analyze received Earth observation data.

[1557] A "classification tool" is a device or system used to classify analyzed data into specific categories.

[1558] An "integration tool" is a device or system for integrating classified data and generating overall information.

[1559] "Generation means" refers to devices or systems for generating real-time information based on integrated data.

[1560] "Means of provision" refers to devices or systems that provide generated information according to the user's search terms.

[1561] "Satellite image data" refers to image data of the Earth taken from a satellite.

[1562] "Prediction tools" refer to devices and systems that analyze satellite image data to predict the occurrence of climate change and natural disasters.

[1563] A "notification means" is a device or system for notifying users of predicted information in real time.

[1564] A system for carrying out this invention includes a series of processes for receiving, analyzing, classifying, and integrating Earth observation data, generating real-time information, and providing it to the user. Specific embodiments of this system are described below.

[1565] First, the server receives image data transmitted from the satellite using a receiving means for receiving Earth observation data. This receiving means includes a communication module for acquiring data via an internet connection.

[1566] Next, the received Earth observation data is analyzed by an analysis tool that includes generative AI. This analysis tool includes generative AI models using deep learning frameworks such as TensorFlow and Keras. The generative AI model takes satellite image data as input and predicts the occurrence of climate change and natural disasters.

[1567] The analyzed data is classified into specific categories using a classification method. For example, it may be classified into data related to climate change or data related to natural disasters. This classification method is implemented using a database management system (DBMS).

[1568] The classified data is integrated by an integration mechanism to generate overall information. This integration mechanism is implemented using data integration tools or scripts.

[1569] Based on integrated data, the generation mechanism generates real-time information. This generation mechanism then generates information to be provided to the user based on the data analysis results.

[1570] The generated information is provided according to the user's search terms through a delivery method. This delivery method provides the information to the user via a web server or mobile application.

[1571] Furthermore, the system includes a prediction method that receives satellite image data and predicts the occurrence of climate change and natural disasters. This prediction method uses a generative AI model to predict the occurrence of climate change and natural disasters from satellite image data.

[1572] The predicted information will be notified to the user in real time via notification methods. These notification methods will be implemented using smartphone push notification and email notification functions.

[1573] As a concrete example, consider a scenario where a user launches an application on their smartphone, acquires the latest satellite imagery, and analyzes it. An example of a prompt to be input to the generative AI model is as follows:

[1574] "Analyze the latest satellite imagery to predict climate change and natural disaster occurrences in specific regions."

[1575] By inputting this prompt into the generating AI model, the model analyzes satellite images and outputs results that predict the occurrence of climate change and natural disasters.

[1576] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1577] Step 1:

[1578] The server receives Earth observation data. Specifically, it acquires image data transmitted from satellites via an internet connection. The input is image data from satellites, and the output is the received image data.

[1579] Step 2:

[1580] The server analyzes received Earth observation data using analytical methods, including generative AI. Specifically, it inputs image data into generative AI models using deep learning frameworks such as TensorFlow and Keras to predict the occurrence of climate change and natural disasters. The input is the received image data, and the output is the analysis results.

[1581] Step 3:

[1582] The server classifies the analyzed data using classification methods. Specifically, it uses a database management system (DBMS) to classify data into categories such as climate change data and natural disaster data. The input is the analysis results, and the output is the classified data.

[1583] Step 4:

[1584] The server integrates the classified data using integration tools. Specifically, it generates overall information using data integration tools and scripts. The input is classified data, and the output is integrated data.

[1585] Step 5:

[1586] The server generates real-time information using a generation mechanism based on integrated data. Specifically, it generates information to provide to the user based on the results of data analysis. The input is integrated data, and the output is the generated information.

[1587] Step 6:

[1588] The server provides the generated information to the user according to their search terms through various means. Specifically, it provides information to the user through a web server or mobile application. The input is the generated information and the user's search terms, and the output is the information provided to the user.

[1589] Step 7:

[1590] The server receives satellite image data and uses prediction tools to forecast the occurrence of climate change and natural disasters. Specifically, it uses a generative AI model to predict the occurrence of climate change and natural disasters from satellite image data. The input is satellite image data, and the output is the prediction result.

[1591] Step 8:

[1592] The server notifies the user in real time of the predicted information via notification methods. Specifically, this is implemented using smartphone push notification and email notification functions. The input is the prediction result, and the output is the information notified to the user.

[1593] (Example 3)

[1594] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[1595] Conventional Earth observation data analysis systems often involved manual data analysis, classification, and integration, making real-time information provision difficult. Furthermore, the inability to quickly provide information based on user search terms meant users couldn't obtain the information they needed in a timely manner. This resulted in reduced user convenience and diminished the usefulness of the information.

[1596] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1597] In this invention, the server includes receiving means for receiving Earth observation data, analysis means including a generative AI model for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, and provision means for providing the generated information according to search terms. This automates data analysis, classification, and integration, enabling real-time information provision. Furthermore, it allows for the rapid provision of information according to the user's search terms, improving user convenience.

[1598] "Receiving means" refers to a device or function for receiving Earth observation data.

[1599] "Analysis means" refers to a device or function that includes a generative AI model for analyzing received Earth observation data.

[1600] A "generative AI model" is an artificial intelligence model used for natural language processing and data analysis.

[1601] "Classification means" refers to a device or function that classifies data analyzed by an analysis means into a specific category.

[1602] An "integration means" is a device or function that integrates classified data into a single set of information.

[1603] "Generation means" refers to a device or function that generates real-time information based on integrated data.

[1604] "Means of provision" refers to a device or function that provides generated information according to the user's search terms.

[1605] "Search terms" are keywords or phrases that users enter when searching for information.

[1606] "Real-time information" refers to information generated based on the current situation and the latest data.

[1607] This invention is a system that efficiently analyzes Earth observation data and provides real-time information tailored to the user's search terms. This system operates through the coordinated efforts of a server, a terminal, and the user.

[1608] First, the user enters a search term using a device (for example, a smartphone or computer). For example, they might enter "Tokyo weather." The device then sends this search term to the server.

[1609] The server receives Earth observation data using a receiving means. The received data is analyzed by an analysis means. This analysis uses a generative AI model (for example, a model using natural language processing technology). Specifically, a general natural language processing model can be used as the generative AI model.

[1610] The analyzed data is classified into specific categories using classification methods. For example, weather data is classified into categories such as "temperature," "probability of precipitation," and "wind speed." Machine learning algorithms (e.g., k-means clustering) are used for this classification.

[1611] The classified data is integrated into a single information set using an integration tool. A database management system (such as MySQL or Amazon Redshift) is used for this integration. This brings together related data into a single information set.

[1612] Based on integrated data, the generation mechanism generates real-time information. A generation AI model is used for this generation. For example, if a user searches for "Tokyo weather," the server generates the latest weather information for Tokyo based on the integrated weather data.

[1613] The generated information is provided to the user using the provided means. The user can check the latest weather information for Tokyo on their device screen. An example of a specific prompt message would be, "The user searched for 'Tokyo weather'. Please provide the latest weather information for Tokyo based on this search term."

[1614] This system allows users to efficiently obtain the information they need. Data analysis, classification, and integration are automated, enabling real-time information delivery. Furthermore, it can quickly provide information tailored to the user's search terms, improving user convenience. The specific processing flow in Example 3 will be explained using Figure 15.

[1615] Step 1:

[1616] The user enters a search term.

[1617] The user enters a search term using a terminal. For example, they might enter "Tokyo weather." This input data is sent from the terminal to the server. The input data is the search term, and the output is the search term sent to the server.

[1618] Step 2:

[1619] The server receives the data.

[1620] The server receives search terms sent from the terminal. Using the receiving mechanism, it also simultaneously receives Earth observation data. The input data consists of the search terms and Earth observation data, while the output is the data passed to the analysis mechanism.

[1621] Step 3:

[1622] The server analyzes the data.

[1623] The server analyzes the received Earth observation data using an analysis tool. A generative AI model is used for this analysis. Specifically, the generative AI model is prompted with a search term and extracts relevant data. The input data consists of Earth observation data and the search term, and the output is the analyzed data. As a concrete example, the server inputs the following prompt to the generative AI model: "The user searched for 'Tokyo weather.' Based on this search term, please provide the latest weather information for Tokyo."

[1624] Step 4:

[1625] The server classifies the data.

[1626] The server classifies the data analyzed by the analysis tools using classification tools. For example, it classifies weather data into categories such as "temperature," "probability of precipitation," and "wind speed." Machine learning algorithms are used for this classification. The input data is the analyzed data, and the output is the classified data. Specifically, the server stores the weather data separated into each category.

[1627] Step 5:

[1628] The server integrates the data.

[1629] The server integrates the classified data using an integration mechanism. For example, it uses a database management system to combine data from each category into a single information set. The input data is classified data, and the output is integrated data. Specifically, the server executes the following SQL query: "SELECT FROM weather_data WHERE location = 'Tokyo';"

[1630] Step 6:

[1631] The server generates real-time information.

[1632] The server generates real-time information based on integrated data. A generative AI model is used for this generation. The input data is integrated data, and the output is the generated real-time information. Specifically, the server inputs the following prompt to the generative AI model: "Generate the latest weather information for Tokyo based on the integrated weather data."

[1633] Step 7:

[1634] The server provides information to the user.

[1635] The server provides the generated information to the user using a delivery method. The user can check the latest weather information for Tokyo on their terminal screen. The input data is the generated real-time information, and the output is the information provided to the user. Specifically, the server sends the generated information to the terminal in HTML format and displays it in the user's browser.

[1636] (Application Example 3)

[1637] Next, we will describe application example 3 of form example 3. 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."

[1638] Conventional information provision systems struggle to provide users with searched information in real time, and there is a particular need to efficiently analyze, classify, and integrate large amounts of complex data, such as Earth observation data. Furthermore, there is a lack of technology to quickly generate and provide appropriate information in response to user search queries. As a result, it is not possible to provide users with the information they need in a timely manner, leading to problems with the accuracy and reliability of the information.

[1639] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1640] In this invention, the server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, a generating AI model for generating appropriate information based on the user's search query, and a prompt input means for inputting prompt sentences to the generating AI model to generate information. This makes it possible to provide the information searched by the user in real time, improving the accuracy and reliability of the information.

[1641] "Earth observation data" refers to data collected to observe various phenomena and environmental conditions on Earth.

[1642] "Receiving means" refers to devices or systems for receiving data from an external source.

[1643] "Analysis means" refers to devices or systems used to analyze received data.

[1644] "Generative AI" refers to a system that uses artificial intelligence technology to generate data.

[1645] A "classification tool" is a device or system used to classify analyzed data into specific categories.

[1646] An "integration tool" is a device or system used to combine classified data into a single entity.

[1647] A "generation means" is a device or system for generating new information based on integrated data.

[1648] "Means of provision" refers to devices or systems used to provide generated information to users.

[1649] A "generative AI model" is a model that uses artificial intelligence technology to generate appropriate information based on a user's search query.

[1650] A "prompt input means" is a device or system for inputting prompt text into a generative AI model to generate information.

[1651] A system for carrying out this invention has the following configuration: The server includes a receiving means for receiving Earth observation data, an analysis means including a generating AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analysis means, an integration means for integrating the classified data, a generation means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, a generating AI model for generating appropriate information based on the user's search query, and a prompt input means for inputting prompt sentences to the generating AI model to generate information.

[1652] Hardware and software to use

[1653] Hardware: Servers, user terminals (smartphones, smart glasses)

[1654] Software: Generative AI models (e.g., GPT-4), data analysis tools (e.g., Pandas), cloud databases (e.g., Firebase)

[1655] Data processing and data calculation

[1656] 1. Data Collection: The server collects real-time data from cloud databases. For example, it retrieves data from weather information APIs and news APIs.

[1657] 2. Data Analysis: The server analyzes the collected data using Pandas and extracts the necessary information.

[1658] 3. Data Classification: The server classifies the analyzed data using a generative AI model (GPT-4). For example, it classifies the data into weather information, news, traffic information, etc.

[1659] 4. Data Integration: The server integrates the categorized data and generates relevant information in response to the user's search queries.

[1660] 5. Information Provision: The server generates real-time information based on integrated data and provides it to the user's terminal.

[1661] Specific example

[1662] For example, when a user searches for "Tokyo weather" on their smartphone, the system works as follows:

[1663] 1. Data Collection: The server obtains the latest weather data for Tokyo from a weather information API.

[1664] 2. Data Analysis: The server analyzes the acquired data using Pandas and extracts the necessary information.

[1665] 3. Data Classification: The server classifies the analyzed data as weather information using a generating AI model (GPT-4).

[1666] 4. Data Integration: The server integrates categorized weather information and generates relevant information in response to the user's search queries.

[1667] 5. Information Provision: The server provides the user's terminal with the latest weather information for Tokyo based on integrated data.

[1668] Example of a prompt

[1669] User's search query: "Tokyo weather"

[1670] Prompt to the generating AI model: "Please tell me the latest weather information for Tokyo."

[1671] When this prompt is input into the generating AI model, the model generates the latest weather information for Tokyo from the analyzed, classified, and integrated data and provides it to the user.

[1672] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1673] Step 1:

[1674] The server collects real-time data from a cloud database. Specifically, it retrieves data from weather information APIs and news APIs. The input for this step is data from the APIs, and the output is the retrieved raw data.

[1675] Step 2:

[1676] The server analyzes the collected data using Pandas and extracts the necessary information. Specifically, it cleans and preprocesses the data and converts it into a format that can be analyzed. The input for this step is the raw data obtained in step 1, and the output is the analyzed data.

[1677] Step 3:

[1678] The server classifies the analyzed data using a generative AI model (GPT-4). Specifically, it classifies the data into categories such as weather information, news, and traffic information. The input for this step is the data analyzed in step 2, and the output is the classified data.

[1679] Step 4:

[1680] The server integrates the classified data and generates appropriate information in response to the user's search query. Specifically, it combines related data and generates information corresponding to the user's search query. The input for this step is the data classified in step 3 and the user's search query, and the output is the integrated information.

[1681] Step 5:

[1682] The server generates real-time information based on the integrated data and provides it to the user terminal. Specifically, it inputs prompt messages into the generation AI model and provides the generated information to the user. The input for this step is the information integrated in step 4 and the prompt messages, and the output is the final information provided to the user.

[1683] Specific example

[1684] User's search query: "Tokyo weather"

[1685] Prompt to the generating AI model: "Please tell me the latest weather information for Tokyo."

[1686] When this prompt is input into the generating AI model, the model generates the latest weather information for Tokyo from the analyzed, classified, and integrated data and provides it to the user.

[1687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1688] "Example of form 1"

[1689] In one embodiment of the present invention, the system includes receiving means for receiving Earth observation data, analysis means including a generating AI for analyzing the received Earth observation data, classification means for classifying the data analyzed by the analysis means, integration means for integrating the classified data, generation means for generating real-time information based on the integrated data, and providing means for providing the generated information according to search words. Furthermore, it also includes an emotion engine for recognizing the user's emotions.

[1690] "Example of form 2"

[1691] The emotion engine recognizes emotions from, for example, the user's tone of voice, facial expressions, and text input. This emotion engine then adjusts the information generated based on the user's emotions. For example, if the user is expressing joy, the system will prioritize generating positive information to match that emotion.

[1692] "Example of form 3"

[1693] ...

Claims

1. A system including a server, wherein the server comprises a processor and memory, and the processor executes a program read into the memory, A receiving means for receiving Earth observation data transmitted from an Earth observation satellite, Analysis means including a generating AI for analyzing the received Earth observation data, A classification means for classifying the data analyzed by the aforementioned analysis means into predetermined categories, An integration means for integrating classified data, A generation means that generates real-time information based on integrated data, A means of providing generated information according to search terms, An emotion recognition means that recognizes the emotions of the user who entered the aforementioned search term, An adjustment means that adjusts the generated information based on the emotion recognized by the emotion recognition means, Equipped with, The analysis means analyzes the search words entered by the user using natural language processing technology with the generating AI to extract information related to the search words, and based on the extracted related information, processes the Earth observation data to extract information that indicates the characteristics of the Earth observation data, thereby analyzing the Earth observation data so that the search words are reflected. The generation means inputs the integrated data and a prompt sentence containing the search word to the integrated data into the generation AI, and generates the information obtained as the real-time information. The providing means provides information that has been adjusted by the adjustment means based on the information generated by the generation means, according to the search word. A system characterized by the following features.

2. The system according to claim 1, wherein the generation means generates information relating to events currently occurring on Earth.

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