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 technology]

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

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

[0004] The amount of Earth observation data is enormous, and it is difficult to analyze all of it in real time and provide users with the information they need. [Means for solving the problem]

[0005] This invention generates real-time information by receiving Earth observation data and analyzing, classifying, and integrating it using generative AI. Furthermore, by providing information generated based on user search terms, it is possible to efficiently provide users with the information they need. [Brief explanation of the drawings]

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

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

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

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

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

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

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

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

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

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

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0027] "Example 1"

[0028] One embodiment of the present invention is a system including a receiving means for receiving earth observation data transmitted from an earth observation satellite, the receiving means being capable of receiving data from the satellite in real time.

[0029] "Example 2"

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

[0031] "Example 3"

[0032] Furthermore, there are classification means for classifying the analyzed data and integration means for integrating the classified data. These means allow the analyzed data to be appropriately classified and integrated, making it possible to efficiently provide the information required by users.

[0033] "Example 4"

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

[0035] The processing flow of each embodiment will be described below.

[0036] "Example 1"

[0037] Step 1: Receive earth observation data transmitted from an earth observation satellite. This reception is performed in real time using a specific receiving device.

[0038] "Example 2"

[0039] Step 1: The received Earth observation data is analyzed by the generated AI. This analysis is carried out using AI techniques such as deep learning, and it is possible to detect climate change and the occurrence of natural disasters in specific areas from satellite images.

[0040] "Example 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, and the analyzed data is appropriately classified and integrated.

[0043] "Example 4"

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

[0045] Step 2: Provide information generated based on the user's search terms. For example, if a user searches for "weather in Tokyo," the system will generate information about the weather in Tokyo from the data analyzed, classified, and integrated by the generative AI and provide it to the user.

[0046] Example 1

[0047] Next, a description will be given of 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."

[0048] Conventional systems for receiving and analyzing Earth observation data have not been able to efficiently receive and analyze data in real time, making it difficult to quickly provide users with the information they need. Furthermore, the methods for storing and providing analyzed data are inadequate, resulting in the problem of users having to wait a long time to access the data they need.

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

[0050] "Receiving means" refers to a device or system for receiving data transmitted from an Earth observation satellite.

[0051] "Analysis means" refers to a device or system that analyzes the received Earth observation data and extracts necessary information.

[0052] "Storage means" refers to a device or system for storing analyzed data in a database or the like.

[0053] The "providing means" is a device or system for providing the stored data to users or other systems.

[0054] "Access means" refers to a device or system that allows a user to access stored data through a provision means.

[0055] The present invention relates to a system for receiving, analyzing, storing, and providing data transmitted from an earth observation satellite in real time. Specific embodiments of this system will be described below.

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

[0057] The server then uses data analysis software to analyze the data it receives. This analysis process involves shaping, filtering, and extracting the necessary information, such as temperature, humidity, and wind speed in the case of weather data.

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

[0059] The stored data is provided to users or other systems through a provisioning method. Users can access the API using their terminal to obtain 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 specific example, the process by which a server receives, analyzes, stores, and provides weather data transmitted from an Earth observation satellite is shown below.

[0061] Example: A server receives weather data transmitted from an Earth observation satellite using a high-sensitivity antenna and receiver. The received data is sent to the server via data reception software, and information such as temperature, humidity, and wind speed is extracted using data analysis software. The analyzed data is stored in a database, and users can access the API using their devices to obtain the required data.

[0062] Example prompt sentence:

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

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

[0065] The flow of the identification process in the first embodiment will be described with reference to FIG.

[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 launches the data reception software and configures it to receive signals from the satellite. The input is the installation and connection of the high-sensitivity antenna and receiver, and the output is the launch of the data reception software and completion of the configuration.

[0068] Step 2: Receiving data

[0069] The server uses a highly sensitive antenna and receiver to receive data transmitted from Earth observation satellites in real time. The received data is transferred to the server through data reception software. The input is data from the Earth observation satellite, 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 the data analysis software and begins the analysis. This analysis process involves shaping and filtering the data and extracting the necessary information. 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 saves the parsed data in a database. When saving, it also saves metadata such as the data type and timestamp. The input is the parsed data, and the output is the data saved in the database. Specifically, the server connects to the database and inserts data using an SQL query.

[0074] Step 5: Provide data

[0075] The server makes the stored data accessible through an API to provide it to users and other systems. Users can access the API using their terminals to obtain the data they need. The input is the request from the user, and the output is the provided data. Specifically, the server receives the API request, retrieves the required data from the database, and returns it to the user.

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

[0077] (Application example 1)

[0078] Next, a description will be given of Application Example 1 of Embodiment 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 quickly detect natural disasters and suspicious activities using real-time data from Earth observation satellites and issue appropriate warnings to users. However, conventional systems have issues with the process from receiving data to analyzing it and issuing warnings being inefficient and lacking real-time capabilities. This means that users are unable to receive information at the appropriate time, making it difficult to respond quickly.

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

[0081] In this invention, the server includes a receiving means for receiving earth observation data, an analyzing means including a generating AI for analyzing the received earth observation data, and a classifying means for classifying the data analyzed by the analyzing 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 containing information about the Earth's surface and atmosphere transmitted from Earth observation satellites.

[0083] The "receiving means" is a device or system for receiving earth observation data transmitted from an earth observation satellite in real time.

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

[0085] "Generative AI" is an artificial intelligence technology that analyzes incoming data and detects specific patterns and anomalies.

[0086] The "classification means" is a device or system for classifying the data analyzed by the analysis means into specific categories.

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

[0088] A "generator" is a device or system for generating real-time information based on the integrated data.

[0089] The "provision means" is a device or system for providing the generated information to the user in accordance with the search word.

[0090] An "alert means" is a device or system for issuing an alert to a user based on the generated information.

[0091] A system for carrying out the present invention receives and analyzes earth observation data transmitted from an earth observation satellite in real time, and issues a warning to a user. A specific embodiment of the system will be 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, it uses a dedicated receiving antenna or data receiving server.

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

[0096] 3. Classification means: A device or system that classifies the data analyzed by the analysis means into specific categories. A database management system (DBMS) is used to efficiently classify data.

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

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

[0099] 6. Means of provision: A device or system that provides the generated information to users in response to search words. Information is provided through smartphone apps or web applications.

[0100] 7. Alerting means: A device or system that issues an alert to the user based on the generated information. Alerts are issued using push notifications or email notifications.

[0101] Program processing

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

[0103] Hardware and software used

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

[0105] Software: Python, TensorFlow, database management system (DBMS), smartphone app, web application

[0106] Specific examples

[0107] For example, it receives data transmitted from Earth observation satellites and uses generative AI models to detect natural disasters (e.g., earthquakes, floods) and suspicious activities (e.g., forest fires, illegal fishing). The detected information is provided to users in real time, and warnings are issued as needed.

[0108] Example prompts for generative AI models

[0109] Your goal is to write a Python program that analyzes real-time data from Earth observation satellites to detect natural disasters and suspicious activity. The data should be retrieved from an API and the program should include the ability to alert users based on the analysis results.

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

[0111] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0112] Step 1:

[0113] The server receives Earth observation data transmitted from Earth observation satellites. Using a receiving means, the server acquires data through a dedicated receiving antenna or a data receiving server. The input is data from the Earth observation satellite, and the output is the received raw data.

[0114] Step 2:

[0115] The server analyzes the received Earth observation data using a generative AI model. It uses Python and TensorFlow as the analytical 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, input to the model, and obtaining the analysis results.

[0116] Step 3:

[0117] The server classifies the analyzed data into specific categories. It uses a database management system (DBMS) as a classification tool 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 the classified data to generate comprehensive information. It uses an integration means to update the data in real time and generate the integrated information. The input is the classified data, and the output is the 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 the integrated data. Using the generation means, it generates information to provide to users. The input is the integrated data, and the output is the generated information. Specific operations include data format conversion and information generation.

[0122] Step 6:

[0123] The server provides the generated information to the user according to the search terms. The server uses a smartphone app or web application to provide the information to the user. The input is the generated information and the user's search terms, and the output is the information provided to the user. Specific operations include receiving search terms, filtering the information, and providing the information.

[0124] Step 7:

[0125] The server issues a warning to the user based on the generated information. The warning method is push notification or email notification. The input is the generated information, and the output is the warning issued to the user. Specific operations include checking the warning conditions, generating the notification, and sending the notification.

[0126] Example 2

[0127] Next, a description will be given of 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."

[0128] In conventional Earth observation data analysis systems, preprocessing and analysis of received data is often done manually, which is inefficient. Furthermore, classification and integration of analysis results are insufficient, making it difficult to provide information in real time. Furthermore, information provided based on user search terms may not be appropriate.

[0129] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 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 provision means for providing the generated information in accordance with a search term. This enables efficient preprocessing, analysis, classification, and integration of Earth observation data, and realizes real-time information provision. It also enables appropriate information to be provided based on the user's search term.

[0130] "Receiving means" means any device or facility for receiving Earth observation data from a satellite or other data source.

[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 that include generative AI for analyzing preprocessed data.

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

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

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

[0136] "Generation means" refers to a device or function that generates real-time information based on the integrated data.

[0137] "Providing means" refers to a device or function that provides the generated information in response to the user's search words.

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

[0139] MODE FOR CARRYING OUT THE INVENTION

[0140] The present invention relates to a system for efficiently analyzing earth observation data and providing real-time information. Specific embodiments of this system will be described below.

[0141] 1. System Configuration

[0142] The system includes the following main means:

[0143] Reception means: Receives Earth observation data from satellites and other data sources.

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

[0145] Analysis tools: Includes generative AI for analyzing pre-processed data.

[0146] Classification means: Classifying data analyzed by analytical means into specific categories or patterns.

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

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

[0149] Provision method: The generated information is provided according to the user's search terms.

[0150] 2. Hardware and Software Used

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

[0152] Generative AI model: Earth observation data is analyzed using a generative AI model that uses deep learning technology.

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

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

[0155] 3. Specific Examples

[0156] If a user wants to analyze climate change in a particular region, they follow these steps:

[0157] 1. Data reception:

[0158] Users upload satellite image data of the area they wish 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] For numerical data, the server imputes missing values ​​and performs normalization.

[0163] 3. Applying generative AI models:

[0164] The server inputs the preprocessed data into a generative 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 generative AI model.

[0168] The user checks the analysis results provided by the server and takes necessary measures.

[0169] Prompt Sentence Examples

[0170] "Use the satellite imagery data below to analyze climate change in a specific region. Preprocess the data and apply a generative AI model to detect patterns of climate change."

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

[0172] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0173] Step 1: Receiving data

[0174] The server receives Earth observation data from satellites and other data sources. The input is satellite image data and numerical data. The output is 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 pre-processes the received Earth observation data. The input is the received raw data. The output is the pre-processed data.

[0178] Specific behavior:

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

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

[0181] Step 3: Applying the generative AI model

[0182] The server inputs the preprocessed data into the generative AI model. The input is the preprocessed data. The output is the analysis result.

[0183] How it works: The server feeds the preprocessed data into a generative AI model, which then analyzes the data to detect patterns of climate change and natural disasters.

[0184] Step 4: Data Classification

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

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

[0187] Step 5: Data Integration

[0188] The server aggregates the classified data. The input is the classified data. The output is the aggregated 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 the integrated data. The input is the integrated data. The output is the generated real-time information.

[0192] What it does: The server analyzes the combined data and applies algorithms to generate real-time information.

[0193] Step 7: Provide information

[0194] The server provides the generated information according to the user's search words. The input is the user's search words 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 them, and provides it to the user's device.

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

[0197] (Application example 2)

[0198] Next, a description will be given of 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 had difficulty predicting climate change and natural disasters in real time and notifying users promptly. Furthermore, they lacked the functionality to provide users with the information they needed based on search terms, making it difficult to obtain information. Therefore, in today's world, where rapid responses to climate change and natural disasters are required, a more efficient and prompt information provision system is needed.

[0200] The identification processing by the identification 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 analyzing means including a generation AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analyzing means, an integration means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information in accordance with a search word, a prediction means for receiving satellite image data and predicting the occurrence of climate change or 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 or natural disasters and notify the user in real time.

[0201] "Earth observation data" is data collected to observe the state of the Earth's surface, atmosphere, oceans, etc.

[0202] "Receiving means" refers to a device or system for receiving Earth observation data.

[0203] "Generative AI" is an artificial intelligence model that uses artificial intelligence techniques such as deep learning to analyze data and generate new information.

[0204] "Analysis means" refers to a device or system for analyzing received Earth observation data.

[0205] A "classification means" is a device or system for classifying analyzed data into specific categories.

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

[0207] "Generation means" refers to a device or system for generating real-time information based on the integrated data.

[0208] The "provision means" refers to a device or system for providing the generated information in response to the user's search words.

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

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

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

[0212] A system for implementing the present invention includes a series of processes for receiving, analyzing, classifying, and integrating Earth observation data, generating real-time information, and providing it to users. A specific embodiment of this system will be described below.

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

[0214] The received Earth observation data is then analyzed by analytical means, including generative AI models using deep learning frameworks such as TensorFlow and Keras. The generative AI models receive satellite image data as input and predict climate change and the occurrence of natural disasters.

[0215] The analyzed data is classified into specific categories using a classification mechanism, such as data on climate change or data on natural disasters, etc. This classification mechanism is implemented using a database management system (DBMS).

[0216] The classified data is then integrated to generate the overall information by an integration method, which is implemented using a data integration tool or script.

[0217] Based on the integrated data, the generating means generates real-time information, which is then provided to the user based on the results of the data analysis.

[0218] The generated information is provided to the user in response to the user's search words by the providing means, which provides the information to the user through a web server or a mobile application.

[0219] Furthermore, a prediction means for receiving satellite image data and predicting the occurrence of climate change and natural disasters is included. This prediction means predicts the occurrence of climate change and natural disasters from the satellite image data using a generative AI model.

[0220] The predicted information is notified to the user in real time via a notification method implemented using the push notification function or email notification function of the smartphone.

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

[0222] "Analyze the latest satellite images to predict climate change and natural disasters in specific regions."

[0223] By inputting this prompt into the generative AI model, the model will analyze satellite images and output predictions for climate change and the occurrence of natural disasters.

[0224] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0225] Step 1:

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

[0227] Step 2:

[0228] The server analyzes the received Earth observation data using analytical methods including generative AI. Specifically, image data is input into a generative AI model using deep learning frameworks such as TensorFlow and Keras to predict climate change and the occurrence of 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 a classification method. Specifically, it uses a database management system (DBMS) to classify the data into categories such as data on climate change and data on natural disasters. The input is the analysis results, and the output is the classified data.

[0231] Step 4:

[0232] The server integrates the classified data using an integration method. Specifically, it uses a data integration tool or script to generate a holistic information. The input is the classified data, and the output is the integrated data.

[0233] Step 5:

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

[0235] Step 6:

[0236] The server provides the generated information to the user in response to the user's search words through a provisioning means. Specifically, the server provides the information to the user through a web server or mobile application. The input is the generated information and the user's search words, and the output is the information provided to the user.

[0237] Step 7:

[0238] The server receives satellite image data and uses a prediction means to predict climate change and the occurrence of natural disasters. Specifically, a generative AI model is used to predict climate change and the occurrence of 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 of the predicted information in real time via a notification method. Specifically, this is implemented using the push notification function or email notification function of a smartphone. The input is the prediction result, and the output is the information to be notified to the user.

[0241] Example 3

[0242] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0243] In conventional Earth observation data analysis systems, data analysis, classification, and integration were often done manually, making it difficult to provide information in real time. Furthermore, information was not provided promptly in response to user search terms, making it impossible for users to obtain the information they needed in a timely manner. This resulted in issues such as reduced user convenience and a loss of the usefulness of the information.

[0244] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

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

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

[0247] "Analysis means" means 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] The "classification means" is a device or function that classifies the data analyzed by the analysis means into specific categories.

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

[0251] A "generation means" is a device or function that generates real-time information based on the integrated data.

[0252] The "providing means" is a device or function that provides the generated information in response to the user's search words.

[0253] A "search word" is a keyword or phrase that a user enters when searching for information.

[0254] "Real-time information" is information generated based on current conditions and the latest data.

[0255] This invention is a system that efficiently analyzes Earth observation data and provides real-time information according to user search terms. This system operates in cooperation with a server, a terminal, and a user.

[0256] First, a user inputs a search term using a device (e.g., a smartphone or a PC). For example, they input "weather in Tokyo." The device then sends this search term to the server.

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

[0258] The analyzed data is then classified into specific categories using classification techniques. For example, weather data is classified into categories such as "temperature," "precipitation probability," and "wind speed." This classification is achieved using machine learning algorithms (e.g., k-means clustering).

[0259] The classified data is integrated into a single information set using an integration tool. A database management system (e.g., MySQL (registered trademark) or Amazon Redshift) is used for the integration, which brings together related data into a single information set.

[0260] Based on the integrated data, the generation means generates real-time information. This generation uses a generative AI model. For example, if a user searches for "weather in Tokyo," 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 provision means. The user can check the latest weather information for Tokyo on the screen of their device. An example of a specific prompt sentence is, "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 provision. In addition, information corresponding to the user's search words can be provided quickly, improving user convenience. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[0263] Step 1:

[0264] The user enters a search term

[0265] A user uses a terminal to input a search term. For example, the user inputs "Weather in Tokyo." 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 the search words sent from the terminal. Using the receiving means, it also receives the Earth observation data at the same time. The input data is the search words and the Earth observation data, and the output is the data passed to the analyzing means.

[0269] Step 3:

[0270] The server analyzes the data

[0271] The server uses an analysis means to analyze the received Earth observation data. A generative AI model is used for this analysis. Specifically, a search term is input as a prompt into the generative AI model, and related data is extracted. The input data is the Earth observation data and the search term, and the output is the analyzed data. Specifically, the server inputs the following prompt sentence into the generative AI model: "A user has searched for 'weather in Tokyo.' Please provide the latest weather information for Tokyo based on this search term."

[0272] Step 4:

[0273] The server classifies the data

[0274] The server classifies the data analyzed by the analysis means using the classification means. For example, the server classifies the weather data into categories such as "temperature," "precipitation probability," and "wind speed." A machine learning algorithm is 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 by category.

[0275] Step 5:

[0276] The server consolidates the data

[0277] The server integrates the categorized data using an integration method. For example, it uses a database management system to compile the data from each category into a single information set. The input data is the categorized data, and the output is the integrated data. Specifically, 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 generative AI model is used for this generation. The input data is the 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."

[0281] Step 7:

[0282] The server provides information to the user

[0283] The server provides the generated information to the user using the providing means. The user can check the latest weather information for 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. In concrete terms, the server sends the generated information to the terminal in HTML format and displays it in the user's browser.

[0284] (Application example 3)

[0285] Next, a description will be given of 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 will be referred to as a "terminal."

[0286] Conventional information provision systems have difficulty providing users with the information they search for 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. This means that the information users need cannot be provided in a timely manner, resulting in a decline in the accuracy and reliability of the information.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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 analyzing means including a generating AI for analyzing the received Earth observation data, a classifying means for classifying the data analyzed by the analyzing means, an integrating means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information according to a search word, a generating AI model for generating appropriate information based on a user's search query, and a prompt input means for inputting a prompt sentence into the generating AI model to generate information. This makes it possible to provide information searched by a user in real time, thereby improving the accuracy and reliability of the information.

[0289] "Earth observation data" is data collected to observe various phenomena and environments on Earth.

[0290] "Receiving means" refers to a device or system for receiving data from the outside.

[0291] The "analysis means" is a device or system for analyzing received data.

[0292] "Generative AI" is a system for generating data using artificial intelligence technology.

[0293] A "classification means" is a device or system for classifying analyzed data into specific categories.

[0294] An "integration means" is a device or system for consolidating classified data into one.

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

[0296] "Providing means" refers to a device or system for providing the generated information to the user.

[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 a prompt sentence into a generative AI model to generate information.

[0299] A system for carrying out this invention has the following configuration: A server includes receiving means for receiving Earth observation data, analyzing means including a generation AI for analyzing the received Earth observation data, classification means for classifying 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 in accordance with search words, a generation AI model for generating appropriate information based on a user's search query, and prompt input means for inputting a prompt sentence into the generation AI model to generate information.

[0300] Hardware and software used

[0301] Hardware: Server, user device (smartphone, 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 calculation

[0304] 1. Data collection: The server collects real-time data from a cloud database, for example, from a weather API or a news API.

[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, into weather information, news, traffic information, etc.

[0307] 4. Data Integration: The server integrates the classified data and generates appropriate information according to the user's query.

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

[0309] Specific examples

[0310] For example, if a user searches for "weather in Tokyo" on their smartphone, the system will operate as follows:

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

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

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

[0314] 4. Data integration: The server integrates the categorized weather information and generates appropriate information according to the user's search query.

[0315] 5. Information provision: The server provides the latest weather information for Tokyo to the user's device based on the integrated data.

[0316] Prompt Sentence Examples

[0317] User search query: "Tokyo weather"

[0318] Prompt for generative AI model: "What is the latest weather information for Tokyo?"

[0319] When this prompt sentence is input into the generative AI model, the model will generate the latest weather information for Tokyo from the analyzed, classified, and integrated data and provide it to the user.

[0320] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[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 of 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 to extract the necessary information. Specifically, it cleans and preprocesses the data and converts it into an analyzable format. The input of 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 it into weather information, news, traffic information, etc. The input of this step is the data analyzed in step 2, and the output is the classified data.

[0327] Step 4:

[0328] The server integrates the categorized data and generates appropriate information according to the user's search query. Specifically, it aggregates related data and generates information corresponding to the user's search query. The input of this step is the categorized data from 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's device. Specifically, it inputs a prompt into the generative AI model and provides the generated information to the user. The input of this step is the information integrated in step 4 and the prompt, and the output is the final information provided to the user.

[0331] Specific examples

[0332] User search query: "Tokyo weather"

[0333] Prompt for generative AI model: "What is the latest weather information for Tokyo?"

[0334] When this prompt sentence is input into the generative AI model, the model will generate the latest weather information for Tokyo from the analyzed, classified, and integrated data and provide it to the user.

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

[0336] "Example 1"

[0337] In one embodiment of the present invention, the system includes a receiving means for receiving Earth observation data, an analyzing means including a generating AI for analyzing the received Earth observation data, a classifying means for classifying the data analyzed by the analyzing means, an integrating means for integrating the classified data, a generating means for generating real-time information based on the integrated data, and a providing means for providing the generated information in response to a search word. The system also includes an emotion engine for recognizing user emotions.

[0338] "Example 2"

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

[0340] "Example 3"

[0341] The provision means provides the user with information adjusted by the emotion engine. For example, if the user expresses sadness, the system will provide words of comfort or happy news to correspond to that emotion. This makes it possible to provide information that corresponds to the user's emotions.

[0342] The processing flow of each embodiment will be described below.

[0343] "Example 1"

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

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

[0346] Step 3: Classify the analyzed data.

[0347] Step 4: Integrate the classified data.

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

[0349] Step 6: The generated information is provided according to the search terms.

[0350] Step 7: Recognize user emotions with the emotion engine.

[0351] "Example 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 generated information based on the user's emotions.

[0354] Step 3: If the user expresses joy, the system will prioritize generating positive information to match that emotion.

[0355] "Example 3"

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

[0357] Step 2: If the user expresses sad emotions, the system will provide corresponding responses, such as comforting words or happy news.

[0358] Example 1

[0359] Next, a description will be given of 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."

[0360] Conventional Earth observation data analysis systems have difficulty analyzing data and providing information in real time, and do not provide information that responds to user emotions. As a result, in situations where prompt and appropriate information is required, it has been impossible to provide information that meets user needs.

[0361] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0362] In this invention, the server includes a receiving means for receiving Earth observation data, an analyzing means including a generative AI model for analyzing the received Earth observation data, a classifying means for classifying the data analyzed by the analyzing means, an integrating means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, and an emotion engine for recognizing user emotions. This enables real-time data analysis and information provision, and further realizes 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 incoming Earth observation data, performing pattern recognition and anomaly detection in the data.

[0366] "Analysis means" refers to devices and software for analyzing received Earth observation data using generative AI models.

[0367] The "classification means" is a device or software for classifying the data analyzed by the analysis means into different categories.

[0368] An "integration means" is a device or software that integrates classified data and compiles it into a single data set.

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

[0370] The "provision means" refers to a device or software that provides the generated information to the user in response to the search word.

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

[0372] MODE FOR CARRYING OUT THE INVENTION

[0373] This invention is a system that receives, analyzes, classifies, integrates, generates, and provides real-time Earth observation data transmitted from Earth observation satellites. It also has the ability to recognize user 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 the data using a high-performance antenna and dedicated receiving software (e.g., SatReceiver Pro). For example, the server receives 10MB 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. The server first preprocesses the data and removes noise. The generative AI model is then used to recognize patterns and detect anomalies in 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, topographical data, environmental data). For example, the server classifies weather data into subcategories such as temperature, humidity, and wind speed.

[0380] Integration Methods

[0381] The server integrates the classified data. For example, the server combines meteorological data and topographical data to predict flood risk. This integration is performed using a database management system (e.g., PostgreSQL). The server stores the integrated data as a single dataset for subsequent processing.

[0382] generation means

[0383] The server generates real-time information based on the integrated data. For example, the server generates a warning message based on the flood risk prediction results. This is done using a natural language generation tool (e.g., OpenAI's GPT-4). The server temporarily caches the generated information and prepares it for immediate delivery.

[0384] Providing means

[0385] The server provides the generated information according to the search term. The user enters a search term using a terminal, and the server provides relevant information based on the search term. 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, where the user can view them.

[0386] Emotion Engine

[0387] The server uses an emotion engine (e.g., Affectiva) that recognizes the user's emotions. When the user searches for information through the device, the server analyzes the user's emotions from their facial expressions and voice and adjusts the information provided. For example, if the user looks anxious, the server will provide more detailed explanations or information that provides reassurance. The server analyzes emotional data in real time and reflects this information when providing it.

[0388] Examples and prompts

[0389] Specific examples

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

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

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

[0393] 3. The server classifies the analysis results and integrates the meteorological and topographical data.

[0394] 4. The server generates flood risk warning 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] Prompt Sentence Examples

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

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

[0400] The flow of the identification process in the first embodiment will be described with reference to FIG.

[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 the Earth observation satellite, and the output is raw data temporarily stored in storage. For example, the server receives 10MB of data per second and temporarily stores it in storage.

[0403] Step 2:

[0404] The server uses the 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, the generative AI model is used to recognize patterns and detect anomalies in 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 analyzed data from 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 meteorological data and topographical data to predict flood risk. This integration is performed using a database management system. The server stores the integrated data as a single dataset 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 the flood risk prediction results. A natural language generation tool is used for this generation. The server temporarily caches the generated information and prepares it for immediate provision.

[0411] Step 6:

[0412] The server provides the generated information according to the search words. The input is the search words from the user, and the output is the information as search results. The user enters the search words using their terminal, and the server provides related information based on the search words. 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.

[0413] Step 7:

[0414] The server uses an emotion engine that recognizes the user's emotions. The input is the user's facial and voice data, and the output is analyzed emotional data. When the user searches for information through the device, the server analyzes the user's emotions from their facial expressions and voice and adjusts the information provided. For example, if the user looks anxious, the server will provide more detailed explanations or information that gives a sense of security. The server analyzes emotional data in real time and reflects this when providing information.

[0415] (Application example 1)

[0416] Next, a description will be given of Application Example 1 of Embodiment 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] When 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. In addition, the information provided does not take into account the user's emotions, which has led to issues such as insufficient safety and efficiency of operation.

[0418] The identification processing by the identification 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 analyzing means including a generation AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analyzing means, an integration means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information in accordance with search words, an emotion engine for recognizing user emotions, and a real-time environmental 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 user 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] The "receiving means" is a device or system for receiving earth observation data transmitted from an earth observation satellite.

[0421] "Analysis means" refers to a device or system that includes a generation AI for analyzing received Earth observation data.

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

[0423] The "classification means" is a device or system that classifies the data analyzed by the analysis means into specific categories.

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

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

[0426] The "providing means" is a device or system that provides the generated information in response to the search word.

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

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

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

[0430] Hardware and software used

[0431] Hardware

[0432] On-board computers in autonomous vehicles: Perform data processing and analysis.

[0433] Antenna for receiving 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 incoming data.

[0437] DataClassifier: A module that classifies analysis data.

[0438] DataIntegrator: A module that integrates classification data.

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

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

[0441] Data processing and calculation

[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. Real-time operational information is generated based on the integrated data. Furthermore, the server recognizes the user's emotions and provides information according to those emotions.

[0443] Specific examples

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

[0445] Prompt Sentence Examples

[0446] Create a program that receives data from Earth observation satellites, analyzes weather information, and provides driving information based on the user's emotions. If the user is feeling anxious, warn them, and if the weather is good, notify them of their safety.

[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 emotions.

[0448] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0449] Step 1:

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

[0451] Step 2:

[0452] The server analyzes the received Earth observation data using a generative AI model. The input is the received raw data, and the output is the analyzed data. Specifically, the server launches the AIAnalyzer module and inputs prompts to the generative AI model to analyze the data.

[0453] Step 3:

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

[0455] Step 4:

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

[0457] Step 5:

[0458] The server generates real-time operation information based on the integrated data. The input is the integrated data, and the output is real-time operation 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 the user's emotions. The input is the user's emotional data, and the output is the analyzed emotional information. Specifically, the server uses the EmotionEngine module to analyze the user's facial expressions and voice data and recognize emotions.

[0461] Step 7:

[0462] The server provides appropriate information based on the generated operation information and the user's emotional information. The input is real-time operation information and analyzed emotional information, and the output is optimal operation information provided to the user. Specifically, the server uses the provision means to notify the user of information such as route changes and warnings.

[0463] Example 2

[0464] Next, a description will be given of 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."

[0465] Conventional Earth observation data analysis systems did not adequately preprocess or analyze the received data, making it difficult to generate information in real time. Furthermore, they lacked the ability to adjust information based on user sentiment, making it impossible to provide appropriate information to users. This resulted in low user satisfaction and limited the usefulness of the system.

[0466] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[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 generation 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, provision means for providing the generated information in response to search words, collection means for collecting a user's voice and facial expression, analysis means for analyzing the collected voice and facial expression, emotion recognition means for recognizing the user's emotion from the analysis results, and information generation means for generating and displaying information based on the recognized emotion. This enables highly accurate analysis of Earth observation data and provision of information based on the user's emotion.

[0468] "Earth observation data" is data collected to observe the state of the Earth's surface, atmosphere, oceans, etc.

[0469] "Receiving means" refers to a device or function for receiving Earth observation data from an external source.

[0470] "Preprocessing means" refers to a device or function that performs preprocessing such as noise removal and normalization to make the received Earth observation data easier to analyze.

[0471] "Generative AI" refers to algorithms and models that use artificial intelligence techniques 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 that has been preprocessed using generative AI.

[0473] The "classification means" is a device or function that classifies the data analyzed by the analysis means into specific categories or classes.

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

[0475] "Generation means" refers to a device or function that generates real-time information based on the integrated data.

[0476] "Providing means" refers to a device or function that provides the generated information in response to the user's search words.

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

[0478] "Emotion recognition means" refers to a device or function that analyzes collected voice and facial expressions and recognizes the user's emotions.

[0479] "Information generation means" refers to a device or function that generates information based on the recognized emotion and displays it to the user.

[0480] The present invention relates to a system for analyzing earth observation data and providing information based on a user's emotions. A specific embodiment of this system will be described below.

[0481] Earth observation data analysis

[0482] The server has a receiving means for receiving earth observation data. The receiving means receives data from an earth observation satellite using a satellite communication module. The received data is stored in a specified directory.

[0483] The server then uses preprocessing tools to preprocess the received data. This includes loading the data and imputing missing values ​​using Python's Pandas library. For image data, OpenCV is used to remove noise.

[0484] The preprocessed data is then input to 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 the TensorFlow model and inputs the preprocessed data. The model detects climate change and natural disasters in a specific region.

[0485] The 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 piece of information. The generation means generates real-time information based on the integrated data. The generated information is provided by the provision means in response to the user's search words.

[0486] User emotion recognition

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

[0488] The device then uses analysis means to analyze the collected voice and facial expressions. It uses Google® Cloud Speech-to-Text for voice analysis and OpenCV and Dlib for facial expression analysis. The device calls the Google Cloud Speech-to-Text API to convert the voice to text. It then uses OpenCV and Dlib to analyze emotions from the user's facial expressions.

[0489] Based on the analysis results, the device uses emotion recognition means to recognize the user's emotions, and the emotion engine classifies the user's emotions into categories such as "happiness," "sadness," and "anger."

[0490] Based on the recognized emotion, the device generates information using the information generation means and displays it to the user. For example, if the user says, "I'm very happy today," the device generates positive news or an encouraging message. The generated information is displayed on the device's display.

[0491] Specific examples

[0492] The server uses image data acquired from NASA's MODIS satellite. The generative AI analyzes the image data using deep learning frameworks such as TensorFlow and PyTorch. The analysis results can detect abnormal temperature rises or floods in specific areas.

[0493] Example prompt sentence:

[0494] "Analyze the latest image data from NASA's MODIS satellite to detect climate change and natural disasters occurring in a specific region."

[0495] The device collects the user's voice using a microphone and converts it into 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 will display positive news or encouraging messages.

[0496] Example prompt sentence:

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

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

[0499] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0500] Step 1:

[0501] The server receives the earth observation data. The server receives the data from the earth observation satellite through the satellite communication module using the receiving means. The received data is stored in a specified directory.

[0502] Input: Data from Earth observation satellites

[0503] Output: Saved Earth Observation data files

[0504] Step 2:

[0505] The server preprocesses the received Earth observation data. Using the preprocessing method, the data is read and missing values ​​are imputed using Python's Pandas library. For image data, noise is removed using OpenCV.

[0506] Input: Saved Earth Observation data files

[0507] Output: Preprocessed Earth Observation data

[0508] Step 3:

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

[0510] Input: Preprocessed Earth Observation data

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

[0512] Step 4:

[0513] The server classifies the data analyzed by the analysis means, and uses the classification means to classify 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 and processes the classified data as a single piece of information using an integration means.

[0518] Input: Classified data

[0519] Output: Consolidated data

[0520] Step 6:

[0521] The server generates real-time information based on the integrated data. The generating means generates information based on the integrated data.

[0522] Input: Integrated data

[0523] Output: Generated real-time information

[0524] Step 7:

[0525] The server provides the generated information in response to the search words, and uses the providing means to provide information based on the user's search words.

[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. The device uses a collection means to collect the user's voice and facial expressions through a microphone and a camera. The collected data is temporarily stored in the device's memory.

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

[0531] Output: Collected voice and facial expression data

[0532] Step 9:

[0533] The device analyzes the collected voice and facial expressions. Using the analysis method, Google Cloud Speech-to-Text is used for voice analysis, and OpenCV and Dlib are used for facial expression analysis. The device calls the Google Cloud Speech-to-Text API to convert the voice to text. OpenCV and Dlib are used to analyze emotions from the user's facial expressions.

[0534] Input: Collected voice and facial expression data

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

[0536] Step 10:

[0537] The device recognizes the user's emotions from the analysis results, and classifies the user's emotions based on the analysis results using the emotion recognition means.

[0538] Input: Analysis results

[0539] Output: Recognized emotion

[0540] Step 11:

[0541] The device generates and displays information based on the recognized emotion. Information tailored to the user's emotion is generated using the information generation means and displayed on the device display. For example, if the user says, "I'm very happy today," positive news or an encouraging message is generated.

[0542] Input: Recognized emotion

[0543] Output: the information generated and displayed

[0544] (Application example 2)

[0545] Next, a description will be given of 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] Conventional Earth observation data analysis systems have proven effective in detecting climate change and natural disasters, but 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 increase users' anxiety.

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

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

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

[0550] "Receiving means" refers to a device or system for receiving Earth observation data.

[0551] "Analysis means" refers to a device or system that includes a generation 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] The "classification means" is a device or system that classifies the data analyzed by the analysis means into specific categories.

[0554] An "aggregator" is a device or system that combines disaggregated data into a single, integrated data set.

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

[0556] An "emotion recognition means" is a device or system that recognizes emotions from a user's tone of voice, facial expression, or text input.

[0557] The "adjustment means" is a device or system that adjusts the generated information based on the emotion recognized by the emotion recognition means.

[0558] "Providing means" refers to a device or system that provides the generated information to the user in response to the search word.

[0559] The system for implementing this invention includes a series of processes for receiving and analyzing earth observation data, and providing tailored information based on the user's emotions. Specific embodiments of the system are described below.

[0560] Hardware and software used

[0561] Hardware: Smartphone

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

[0563] System configuration

[0564] 1. Receiving means:

[0565] The server receives Earth observation data from satellites and other observation instruments, including satellite images 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 risk 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 aggregates the classified data into a single, integrated data set, thereby unifying information from multiple data sources.

[0572] 5. Generation means:

[0573] The server generates real-time information based on the integrated data, including information on current weather conditions and the risk of natural disasters.

[0574] 6. Emotion recognition means:

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

[0576] 7. Adjustment means:

[0577] The server adjusts the generated information based on the emotion recognized by the emotion recognition means, for example, if the user is showing signs of anxiety, it prioritizes providing reassuring information.

[0578] 8. Means of provision:

[0579] The device then provides the generated information to the user based on the search term, allowing the user to quickly obtain the information they need.

[0580] Specific examples

[0581] Prompt Sentence Examples

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

[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 a user types, "I'm worried about the weather these days," the system recognizes the emotion and analyzes the latest satellite images to provide the current weather conditions. If the user expresses anxiety, the system prioritizes providing reassuring information.

[0585] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0586] Step 1:

[0587] The server receives Earth observation data from satellites and other observation equipment. The input is satellite images and meteorological data, and the output is the received raw data. Specifically, the server periodically retrieves data from the observation equipment 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 climate change and natural disaster risks.

[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 sorts the analysis results into categories such as "climate change," "flood," and "earthquake."

[0592] Step 4:

[0593] The server aggregates 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 aggregates 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 on the current climate situation and the risk of natural disasters.

[0596] Step 6:

[0597] The device recognizes emotions from the user's tone of voice, facial expressions, and text input. The input is the user's voice data and text data, and the output is 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 generated information based on the emotions recognized by the emotion recognition means. 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 generated information to the user according to the search words. The input is the information adjusted in step 7 and the user's search words, and the output is the information provided to the user. Specifically, the terminal filters the information based on the user's search words and displays appropriate information.

[0602] Example 3

[0603] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0604] Conventional Earth observation data analysis systems have a complex process for analyzing, classifying, and integrating received data, making it difficult to provide information in real time. Furthermore, they are unable to provide information that responds to user emotions, which hinders the user experience.

[0605] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0606] In this invention, the server includes a receiving means for receiving Earth observation data, an analyzing means including a generative AI model for analyzing the received Earth observation data, a classifying means for classifying the data analyzed by the analyzing means, an integrating means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information in response to search words, and an emotion engine by which the providing means provides information corresponding to the user's emotions. This makes it possible to provide information in real time and further realize the provision of information corresponding to the user's emotions.

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

[0608] An "analysis means" is 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] The "classification means" is a device or system that classifies the data analyzed by the analysis means into categories.

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

[0612] A "generator" is a device or system that generates real-time information based on the integrated data.

[0613] The "providing means" is a device or system that provides the generated information in response to the user's search words.

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

[0615] MODE FOR CARRYING OUT THE INVENTION

[0616] The present invention relates to a system for receiving, analyzing, classifying, and integrating earth observation data, generating real-time information, and providing information that responds to user emotions. Specific embodiments of this system will be described below.

[0617] 1. Receiving Data

[0618] The server includes a receiving unit for receiving earth observation data. This receiving unit is a device that receives data via satellite communication or the Internet. For example, it receives meteorological data and environmental data transmitted from earth observation satellites.

[0619] 2. Data Analysis

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

[0621] 3. Data Classification

[0622] The server is equipped with a classification method for classifying the analyzed data. This classification method uses a machine learning algorithm (for example, 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 means for integrating the classified data. This integration means uses a database management system (e.g., MySQL or PostgreSQL). The server integrates the data on temperature, precipitation, and wind speed to generate comprehensive information.

[0625] 5. Real-time information generation

[0626] The server has a generating means for generating real-time information based on the integrated data. This generating means again uses a generative AI model. For example, if a user searches for "weather in Tokyo," the server generates the latest information about the weather in Tokyo from the integrated data and provides it to the user.

[0627] 6. Providing emotionally relevant information

[0628] The server includes an emotion engine that allows the providing means to provide information corresponding to the user's emotion. The emotion engine uses an emotion analysis tool (e.g., IBM Watson (registered trademark) Tone Analyzer). For example, if the user shows sad emotion, the server provides comforting words or happy news.

[0629] Specific examples

[0630] Consider a case where a user searches for "Weather in Tokyo" and the system provides information based on the search term. If the user indicates sad emotion, the system will provide a message such as "The weather in Tokyo is sunny. It will be a great day today. Cheer up!"

[0631] Prompt Sentence Examples

[0632] "If a user searches for 'Tokyo weather' and expresses sadness, generate weather information with comforting words."

[0633] In this way, it is possible to explain a specific embodiment of the system while clarifying the roles of the server, the terminal, and the user. The flow of the identification process in Example 3 will be explained with reference to FIG.

[0634] Step 1:

[0635] The user inputs a search term using the terminal. For example, the user inputs "Weather in Tokyo." The input search term is sent to the server.

[0636] Step 2:

[0637] The server inputs the received search words into the generative AI model and performs analysis. Specifically, the server analyzes the search word "weather in Tokyo" using natural language processing technology and extracts related information. The input is the search word, and the output is the analyzed information.

[0638] Step 3:

[0639] The server classifies the analyzed information into categories using a classification method. Specifically, the server uses a machine learning algorithm 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 an integration means. Specifically, the server uses a database management system to integrate the temperature, precipitation, and wind speed data to generate comprehensive information. The input is the classified data, and the output is the integrated data.

[0642] Step 5:

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

[0644] Step 6:

[0645] The server uses a providing means to provide information corresponding to the user's emotions. Specifically, the server uses an emotion engine to analyze the user's emotions and generate information corresponding to those emotions. For example, if the user expresses sadness, the server provides words of comfort or happy news. The input is the user's emotional information, and the output is information corresponding to the emotions.

[0646] (Application example 3)

[0647] Next, a description will be given of 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 will be referred to as a "terminal."

[0648] Conventional information provision systems were able to provide information based on the user's search terms, but were unable to provide personalized information according to the user's emotions. As a result, they were unable to provide information appropriate to the user's emotional state, resulting in a lack of improvement in the user experience.

[0649] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analyzing means including a generation AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analyzing means, an integration means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information in accordance with search words, an emotion analysis means for analyzing the user's emotions, an adjustment means for adjusting 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 a device or system for receiving Earth observation data.

[0651] "Analysis means" refers to a device or system that includes a generation AI for analyzing received Earth observation data.

[0652] The "classification means" is a device or system for classifying the data analyzed by the analysis means.

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

[0654] "Generation means" refers to a device or system for generating real-time information based on the integrated data.

[0655] The "provision means" refers to a device or system for providing the generated information in response to the search word.

[0656] "Emotion analysis means" refers to a device or system for analyzing a user's emotions.

[0657] The "adjustment means" is a device or system for adjusting information based on the emotion data analyzed by the emotion analysis means.

[0658] A system for implementing the present 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 analyzing means, where it is analyzed using a generative AI model. The analyzing means is software for extracting the characteristics of the data and extracting the necessary information.

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

[0661] Based on the integrated data, a generating means generates real-time information. The generating means is software for generating information required by a user using a generative AI model. The generated information is provided to the user by a providing means. The providing means includes an interface for displaying information according to the user's search words.

[0662] Furthermore, an emotion analysis means is used to analyze the user's emotions. The emotion analysis means is software for analyzing the user's facial expressions and tone of voice using the smartphone's camera and microphone. The analyzed emotion data is used by the adjustment means to adjust the information. The adjustment means includes an algorithm for personalizing the information according to the user's emotions.

[0663] As a specific example, if a user searches for "Tokyo weather" and expresses sad emotions, the system will provide information about "Tokyo weather" along with positive news and encouraging words to comfort the user. An example of a prompt sentence is, "If a user searches for 'Tokyo weather' and expresses sad emotions, please generate positive news and encouraging words to comfort the user along with information about Tokyo weather."

[0664] In this way, personalized information can be provided based not only on the user's search terms, but also on the user's emotional state.

[0665] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0666] Step 1:

[0667] A user launches an application on their smartphone 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 the server. The input data is the user's facial expressions and voice, and the output data is the facial expression and voice data sent to the server.

[0670] Step 3:

[0671] The server receives the Earth observation data using the receiving means, where the input data is the 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 means. It uses a generative AI model to extract features from the data and extract the necessary information. 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 a classification means, dividing the data into categories according to the type and content of the data. The input data is the analyzed data, and the output data is the classified data.

[0676] Step 6:

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

[0678] Step 7:

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

[0680] Step 8:

[0681] The server analyzes the user's facial expression and voice data using an emotion analysis means, and identifies the user's emotion using an emotion analysis algorithm. The input data is the user's facial expression and voice data, and the output data is the analyzed emotion data.

[0682] Step 9:

[0683] The server adjusts the information based on the analyzed emotion data using the adjustment means, personalizing the information according to the user's emotion. The input data is the analyzed emotion data and the generated information, and the output data is the adjusted information.

[0684] Step 10:

[0685] The server provides the adjusted information to the user using the providing means, and displays the information according to the user's search words. The input data is the adjusted information, and the 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0687] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0688] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

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

[0690] [Second embodiment]

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

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

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

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

[0695] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

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

[0697] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0698] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0701] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0703] "Example 1"

[0704] One embodiment of the present invention is a system including a receiving means for receiving earth observation data transmitted from an earth observation satellite, the receiving means being capable of receiving data from the satellite in real time.

[0705] "Example 2"

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

[0707] "Example 3"

[0708] Furthermore, there are classification means for classifying the analyzed data and integration means for integrating the classified data. These means allow the analyzed data to be appropriately classified and integrated, making it possible to efficiently provide the information required by users.

[0709] "Example 4"

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

[0711] The processing flow of each embodiment will be described below.

[0712] "Example 1"

[0713] Step 1: Receive earth observation data transmitted from an earth observation satellite. This reception is performed in real time using a specific receiving device.

[0714] "Example 2"

[0715] Step 1: The received Earth observation data is analyzed by the generated AI. This analysis is carried out using AI techniques such as deep learning, and it is possible to detect climate change and the occurrence of natural disasters in specific areas from satellite images.

[0716] "Example 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, and the analyzed data is appropriately classified and integrated.

[0719] "Example 4"

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

[0721] Step 2: Provide information generated based on the user's search terms. For example, if a user searches for "weather in Tokyo," the system will generate information about the weather in Tokyo from the data analyzed, classified, and integrated by the generative AI and provide it to the user.

[0722] Example 1

[0723] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0724] Conventional systems for receiving and analyzing Earth observation data have not been able to efficiently receive and analyze data in real time, making it difficult to quickly provide users with the information they need. Furthermore, the methods for storing and providing analyzed data are inadequate, resulting in the problem of users having to wait a long time to access the data they need.

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

[0726] "Receiving means" refers to a device or system for receiving data transmitted from an Earth observation satellite.

[0727] "Analysis means" refers to a device or system that analyzes the received Earth observation data and extracts necessary information.

[0728] "Storage means" refers to a device or system for storing analyzed data in a database or the like.

[0729] The "providing means" is a device or system for providing the stored data to users or other systems.

[0730] "Access means" refers to a device or system that allows a user to access stored data through a provision means.

[0731] The present invention relates to a system for receiving, analyzing, storing, and providing data transmitted from an earth observation satellite in real time. Specific embodiments of this system will be described below.

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

[0733] The server then uses data analysis software to analyze the data it receives. This analysis process involves shaping, filtering, and extracting the necessary information, such as temperature, humidity, and wind speed in the case of weather data.

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

[0735] The stored data is provided to users or other systems through a provisioning method. Users can access the API using their terminal to obtain 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 specific example, the process by which a server receives, analyzes, stores, and provides weather data transmitted from an Earth observation satellite is shown below.

[0737] Example: A server receives weather data transmitted from an Earth observation satellite using a high-sensitivity antenna and receiver. The received data is sent to the server via data reception software, and information such as temperature, humidity, and wind speed is extracted using data analysis software. The analyzed data is stored in a database, and users can access the API using their devices to obtain the required data.

[0738] Example prompt sentence:

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

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

[0741] The flow of the identification process in the first embodiment will be described with reference to FIG.

[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 launches the data reception software and configures it to receive signals from the satellite. The input is the installation and connection of the high-sensitivity antenna and receiver, and the output is the launch of the data reception software and completion of the configuration.

[0744] Step 2: Receiving data

[0745] The server uses a highly sensitive antenna and receiver to receive data transmitted from Earth observation satellites in real time. The received data is transferred to the server through data reception software. The input is data from the Earth observation satellite, 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 the data analysis software and begins the analysis. This analysis process involves shaping and filtering the data and extracting the necessary information. 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 saves the parsed data in a database. When saving, it also saves metadata such as the data type and timestamp. The input is the parsed data, and the output is the data saved in the database. Specifically, the server connects to the database and inserts data using an SQL query.

[0750] Step 5: Provide data

[0751] The server makes the stored data accessible through an API to provide it to users and other systems. Users can access the API using their terminals to obtain the data they need. The input is the request from the user, and the output is the provided data. Specifically, the server receives the API request, retrieves the required data from the database, and returns it to the user.

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

[0753] (Application example 1)

[0754] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0755] There is a need for a system that can quickly detect natural disasters and suspicious activities using real-time data from Earth observation satellites and issue appropriate warnings to users. However, conventional systems have issues with the process from receiving data to analyzing it and issuing warnings being inefficient and lacking real-time capabilities. This means that users are unable to receive information at the appropriate time, making it difficult to respond quickly.

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

[0757] In this invention, the server includes a receiving means for receiving earth observation data, an analyzing means including a generating AI for analyzing the received earth observation data, and a classifying means for classifying the data analyzed by the analyzing 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 containing information about the Earth's surface and atmosphere transmitted from Earth observation satellites.

[0759] The "receiving means" is a device or system for receiving earth observation data transmitted from an earth observation satellite in real time.

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

[0761] "Generative AI" is an artificial intelligence technology that analyzes incoming data and detects specific patterns and anomalies.

[0762] The "classification means" is a device or system for classifying the data analyzed by the analysis means into specific categories.

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

[0764] A "generator" is a device or system for generating real-time information based on the integrated data.

[0765] The "provision means" is a device or system for providing the generated information to the user in accordance with the search word.

[0766] An "alert means" is a device or system for issuing an alert to a user based on the generated information.

[0767] A system for carrying out the present invention receives and analyzes earth observation data transmitted from an earth observation satellite in real time, and issues a warning to a user. A specific embodiment of the system will be 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, it uses a dedicated receiving antenna or data receiving server.

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

[0772] 3. Classification means: A device or system that classifies the data analyzed by the analysis means into specific categories. A database management system (DBMS) is used to efficiently classify data.

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

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

[0775] 6. Means of provision: A device or system that provides the generated information to users in response to search words. Information is provided through smartphone apps or web applications.

[0776] 7. Alerting means: A device or system that issues an alert to the user based on the generated information. Alerts are issued using push notifications or email notifications.

[0777] Program processing

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

[0779] Hardware and software used

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

[0781] Software: Python, TensorFlow, database management system (DBMS), smartphone app, web application

[0782] Specific examples

[0783] For example, it receives data transmitted from Earth observation satellites and uses generative AI models to detect natural disasters (e.g., earthquakes, floods) and suspicious activities (e.g., forest fires, illegal fishing). The detected information is provided to users in real time, and warnings are issued as needed.

[0784] Example prompts for generative AI models

[0785] Your goal is to write a Python program that analyzes real-time data from Earth observation satellites to detect natural disasters and suspicious activity. The data should be retrieved from an API and the program should include the ability to alert users based on the analysis results.

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

[0787] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0788] Step 1:

[0789] The server receives Earth observation data transmitted from Earth observation satellites. Using a receiving means, the server acquires data through a dedicated receiving antenna or a data receiving server. The input is data from the Earth observation satellite, and the output is the received raw data.

[0790] Step 2:

[0791] The server analyzes the received Earth observation data using a generative AI model. It uses Python and TensorFlow as the analytical 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, input to the model, and obtaining the analysis results.

[0792] Step 3:

[0793] The server classifies the analyzed data into specific categories. It uses a database management system (DBMS) as a classification tool 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 the classified data to generate comprehensive information. It uses an integration means to update the data in real time and generate the integrated information. The input is the classified data, and the output is the 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 the integrated data. Using the generation means, it generates information to provide to users. The input is the integrated data, and the output is the generated information. Specific operations include data format conversion and information generation.

[0798] Step 6:

[0799] The server provides the generated information to the user according to the search terms. The server uses a smartphone app or web application to provide the information to the user. The input is the generated information and the user's search terms, and the output is the information provided to the user. Specific operations include receiving search terms, filtering the information, and providing the information.

[0800] Step 7:

[0801] The server issues a warning to the user based on the generated information. The warning method is push notification or email notification. The input is the generated information, and the output is the warning issued to the user. Specific operations include checking the warning conditions, generating the notification, and sending the notification.

[0802] Example 2

[0803] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0804] In conventional Earth observation data analysis systems, preprocessing and analysis of received data is often done manually, which is inefficient. Furthermore, classification and integration of analysis results are insufficient, making it difficult to provide information in real time. Furthermore, information provided based on user search terms may not be appropriate.

[0805] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 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 provision means for providing the generated information in accordance with a search term. This enables efficient preprocessing, analysis, classification, and integration of Earth observation data, and realizes real-time information provision. It also enables appropriate information to be provided based on the user's search term.

[0806] "Receiving means" means any device or facility for receiving Earth observation data from a satellite or other data source.

[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 that include generative AI for analyzing preprocessed data.

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

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

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

[0812] "Generation means" refers to a device or function that generates real-time information based on the integrated data.

[0813] "Providing means" refers to a device or function that provides the generated information in response to the user's search words.

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

[0815] MODE FOR CARRYING OUT THE INVENTION

[0816] The present invention relates to a system for efficiently analyzing earth observation data and providing real-time information. Specific embodiments of this system will be described below.

[0817] 1. System Configuration

[0818] The system includes the following main means:

[0819] Reception means: Receives Earth observation data from satellites and other data sources.

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

[0821] Analysis tools: Includes generative AI for analyzing pre-processed data.

[0822] Classification means: Classifying data analyzed by analytical means into specific categories or patterns.

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

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

[0825] Provision method: The generated information is provided according to the user's search terms.

[0826] 2. Hardware and Software Used

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

[0828] Generative AI model: Earth observation data is analyzed using a generative AI model that uses deep learning technology.

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

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

[0831] 3. Specific Examples

[0832] If a user wants to analyze climate change in a particular region, they follow these steps:

[0833] 1. Data reception:

[0834] Users upload satellite image data of the area they wish 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] For numerical data, the server imputes missing values ​​and performs normalization.

[0839] 3. Applying generative AI models:

[0840] The server inputs the preprocessed data into a generative 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 generative AI model.

[0844] The user checks the analysis results provided by the server and takes necessary measures.

[0845] Prompt Sentence Examples

[0846] "Use the satellite imagery data below to analyze climate change in a specific region. Preprocess the data and apply a generative AI model to detect patterns of climate change."

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

[0848] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0849] Step 1: Receiving data

[0850] The server receives Earth observation data from satellites and other data sources. The input is satellite image data and numerical data. The output is 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 pre-processes the received Earth observation data. The input is the received raw data. The output is the pre-processed data.

[0854] Specific behavior:

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

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

[0857] Step 3: Applying the generative AI model

[0858] The server inputs the preprocessed data into the generative AI model. The input is the preprocessed data. The output is the analysis result.

[0859] How it works: The server feeds the preprocessed data into a generative AI model, which then analyzes the data to detect patterns of climate change and natural disasters.

[0860] Step 4: Data Classification

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

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

[0863] Step 5: Data Integration

[0864] The server aggregates the classified data. The input is the classified data. The output is the aggregated 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 the integrated data. The input is the integrated data. The output is the generated real-time information.

[0868] What it does: The server analyzes the combined data and applies algorithms to generate real-time information.

[0869] Step 7: Provide information

[0870] The server provides the generated information according to the user's search words. The input is the user's search words 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 them, and provides it to the user's device.

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

[0873] (Application example 2)

[0874] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0875] Conventional Earth observation data analysis systems have had difficulty predicting climate change and natural disasters in real time and notifying users promptly. Furthermore, they lacked the functionality to provide users with the information they needed based on search terms, making it difficult to obtain information. Therefore, in today's world, where rapid responses to climate change and natural disasters are required, a more efficient and prompt information provision system is needed.

[0876] The identification processing by the identification 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 analyzing means including a generation AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analyzing means, an integration means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information in accordance with a search word, a prediction means for receiving satellite image data and predicting the occurrence of climate change or 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 or natural disasters and notify the user in real time.

[0877] "Earth observation data" is data collected to observe the state of the Earth's surface, atmosphere, oceans, etc.

[0878] "Receiving means" refers to a device or system for receiving Earth observation data.

[0879] "Generative AI" is an artificial intelligence model that uses artificial intelligence techniques such as deep learning to analyze data and generate new information.

[0880] "Analysis means" refers to a device or system for analyzing received Earth observation data.

[0881] A "classification means" is a device or system for classifying analyzed data into specific categories.

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

[0883] "Generation means" refers to a device or system for generating real-time information based on the integrated data.

[0884] The "provision means" refers to a device or system for providing the generated information in response to the user's search words.

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

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

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

[0888] A system for implementing the present invention includes a series of processes for receiving, analyzing, classifying, and integrating Earth observation data, generating real-time information, and providing it to users. A specific embodiment of this system will be described below.

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

[0890] The received Earth observation data is then analyzed by analytical means, including generative AI models using deep learning frameworks such as TensorFlow and Keras. The generative AI models receive satellite image data as input and predict climate change and the occurrence of natural disasters.

[0891] The analyzed data is classified into specific categories using a classification mechanism, such as data on climate change or data on natural disasters, etc. This classification mechanism is implemented using a database management system (DBMS).

[0892] The classified data is then integrated to generate the overall information by an integration method, which is implemented using a data integration tool or script.

[0893] Based on the integrated data, the generating means generates real-time information, which is then provided to the user based on the results of the data analysis.

[0894] The generated information is provided to the user in response to the user's search words by the providing means, which provides the information to the user through a web server or a mobile application.

[0895] Furthermore, a prediction means for receiving satellite image data and predicting the occurrence of climate change and natural disasters is included. This prediction means predicts the occurrence of climate change and natural disasters from the satellite image data using a generative AI model.

[0896] The predicted information is notified to the user in real time via a notification method implemented using the push notification function or email notification function of the smartphone.

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

[0898] "Analyze the latest satellite images to predict climate change and natural disasters in specific regions."

[0899] By inputting this prompt into the generative AI model, the model will analyze satellite images and output predictions for climate change and the occurrence of natural disasters.

[0900] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0901] Step 1:

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

[0903] Step 2:

[0904] The server analyzes the received Earth observation data using analytical methods including generative AI. Specifically, image data is input into a generative AI model using deep learning frameworks such as TensorFlow and Keras to predict climate change and the occurrence of 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 a classification method. Specifically, it uses a database management system (DBMS) to classify the data into categories such as data on climate change and data on natural disasters. The input is the analysis results, and the output is the classified data.

[0907] Step 4:

[0908] The server integrates the classified data using an integration method. Specifically, it uses a data integration tool or script to generate a holistic information. The input is the classified data, and the output is the integrated data.

[0909] Step 5:

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

[0911] Step 6:

[0912] The server provides the generated information to the user in response to the user's search words through a provisioning means. Specifically, the server provides the information to the user through a web server or mobile application. The input is the generated information and the user's search words, and the output is the information provided to the user.

[0913] Step 7:

[0914] The server receives satellite image data and uses a prediction means to predict climate change and the occurrence of natural disasters. Specifically, a generative AI model is used to predict climate change and the occurrence of 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 of the predicted information in real time via a notification method. Specifically, this is implemented using the push notification function or email notification function of a smartphone. The input is the prediction result, and the output is the information to be notified to the user.

[0917] Example 3

[0918] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0919] In conventional Earth observation data analysis systems, data analysis, classification, and integration were often done manually, making it difficult to provide information in real time. Furthermore, information was not provided promptly in response to user search terms, making it impossible for users to obtain the information they needed in a timely manner. This resulted in issues such as reduced user convenience and a loss of the usefulness of the information.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

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

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

[0923] "Analysis means" means 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] The "classification means" is a device or function that classifies the data analyzed by the analysis means into specific categories.

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

[0927] A "generation means" is a device or function that generates real-time information based on the integrated data.

[0928] The "providing means" is a device or function that provides the generated information in response to the user's search words.

[0929] A "search word" is a keyword or phrase that a user enters when searching for information.

[0930] "Real-time information" is information generated based on current conditions and the latest data.

[0931] This invention is a system that efficiently analyzes Earth observation data and provides real-time information according to user search terms. This system operates in cooperation with a server, a terminal, and a user.

[0932] First, a user inputs a search term using a device (e.g., a smartphone or a PC). For example, they input "weather in Tokyo." The device then sends this search term to the server.

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

[0934] The analyzed data is then classified into specific categories using classification techniques. For example, weather data is classified into categories such as "temperature," "precipitation probability," and "wind speed." This classification is achieved using machine learning algorithms (e.g., k-means clustering).

[0935] The classified data is then integrated into a single information set using an integration tool, such as a database management system (e.g., MySQL or Amazon Redshift), which brings together related data into a single information set.

[0936] Based on the integrated data, the generation means generates real-time information. This generation uses a generative AI model. For example, if a user searches for "weather in Tokyo," 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 provision means. The user can check the latest weather information for Tokyo on the screen of their device. An example of a specific prompt sentence is, "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 provision. In addition, information corresponding to the user's search words can be provided quickly, improving user convenience. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[0939] Step 1:

[0940] The user enters a search term

[0941] A user uses a terminal to input a search term. For example, the user inputs "Weather in Tokyo." 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 the search words sent from the terminal. Using the receiving means, it also receives the Earth observation data at the same time. The input data is the search words and the Earth observation data, and the output is the data passed to the analyzing means.

[0945] Step 3:

[0946] The server analyzes the data

[0947] The server uses an analysis means to analyze the received Earth observation data. A generative AI model is used for this analysis. Specifically, a search term is input as a prompt into the generative AI model, and related data is extracted. The input data is the Earth observation data and the search term, and the output is the analyzed data. Specifically, the server inputs the following prompt sentence into the generative AI model: "A user has searched for 'weather in Tokyo.' Please provide the latest weather information for Tokyo based on this search term."

[0948] Step 4:

[0949] The server classifies the data

[0950] The server classifies the data analyzed by the analysis means using the classification means. For example, the server classifies the weather data into categories such as "temperature," "precipitation probability," and "wind speed." A machine learning algorithm is 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 by category.

[0951] Step 5:

[0952] The server consolidates the data

[0953] The server integrates the categorized data using an integration method. For example, it uses a database management system to compile the data from each category into a single information set. The input data is the categorized data, and the output is the 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 the integrated data. A generative AI model is used for this generation. The input data is the 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 the providing means. The user can check the latest weather information for 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. In concrete terms, 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, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0962] Conventional information provision systems have difficulty providing users with the information they search for 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. This means that the information users need cannot be provided in a timely manner, resulting in a decline in the accuracy and reliability of the information.

[0963] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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 analyzing means including a generating AI for analyzing the received Earth observation data, a classifying means for classifying the data analyzed by the analyzing means, an integrating means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information according to a search word, a generating AI model for generating appropriate information based on a user's search query, and a prompt input means for inputting a prompt sentence into the generating AI model to generate information. This makes it possible to provide information searched by a user in real time, thereby improving the accuracy and reliability of the information.

[0965] "Earth observation data" is data collected to observe various phenomena and environments on Earth.

[0966] "Receiving means" refers to a device or system for receiving data from the outside.

[0967] The "analysis means" is a device or system for analyzing received data.

[0968] "Generative AI" is a system for generating data using artificial intelligence technology.

[0969] A "classification means" is a device or system for classifying analyzed data into specific categories.

[0970] An "integration means" is a device or system for consolidating classified data into one.

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

[0972] "Providing means" refers to a device or system for providing the generated information to the user.

[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 a prompt sentence into a generative AI model to generate information.

[0975] A system for carrying out this invention has the following configuration: A server includes receiving means for receiving Earth observation data, analyzing means including a generation AI for analyzing the received Earth observation data, classification means for classifying 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 in accordance with search words, a generation AI model for generating appropriate information based on a user's search query, and prompt input means for inputting a prompt sentence into the generation AI model to generate information.

[0976] Hardware and software used

[0977] Hardware: Server, user device (smartphone, 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 calculation

[0980] 1. Data collection: The server collects real-time data from a cloud database, for example, from a weather API or a news API.

[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, into weather information, news, traffic information, etc.

[0983] 4. Data Integration: The server integrates the classified data and generates appropriate information according to the user's query.

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

[0985] Specific examples

[0986] For example, if a user searches for "weather in Tokyo" on their smartphone, the system will operate as follows:

[0987] 1. Data collection: The server retrieves the latest weather data for Tokyo from the 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 generative AI model (GPT-4).

[0990] 4. Data integration: The server integrates the categorized weather information and generates appropriate information according to the user's search query.

[0991] 5. Information provision: The server provides the latest weather information for Tokyo to the user's device based on the integrated data.

[0992] Prompt Sentence Examples

[0993] User search query: "Tokyo weather"

[0994] Prompt for generative AI model: "What is the latest weather information for Tokyo?"

[0995] When this prompt sentence is input into the generative AI model, the model will generate the latest weather information for Tokyo from the analyzed, classified, and integrated data and provide it to the user.

[0996] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[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 of 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 to extract the necessary information. Specifically, it cleans and preprocesses the data and converts it into an analyzable format. The input of 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 it into weather information, news, traffic information, etc. The input of this step is the data analyzed in step 2, and the output is the classified data.

[1003] Step 4:

[1004] The server integrates the categorized data and generates appropriate information according to the user's search query. Specifically, it aggregates related data and generates information corresponding to the user's search query. The input of this step is the categorized data from 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's device. Specifically, it inputs a prompt into the generative AI model and provides the generated information to the user. The input of this step is the information integrated in step 4 and the prompt, and the output is the final information provided to the user.

[1007] Specific examples

[1008] User search query: "Tokyo weather"

[1009] Prompt for generative AI model: "What is the latest weather information for Tokyo?"

[1010] When this prompt sentence is input into the generative AI model, the model will generate the latest weather information for Tokyo from the analyzed, classified, and integrated data and provide it to the user.

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

[1012] "Example 1"

[1013] In one embodiment of the present invention, the system includes a receiving means for receiving Earth observation data, an analyzing means including a generating AI for analyzing the received Earth observation data, a classifying means for classifying the data analyzed by the analyzing means, an integrating means for integrating the classified data, a generating means for generating real-time information based on the integrated data, and a providing means for providing the generated information in response to a search word. The system also includes an emotion engine for recognizing user emotions.

[1014] "Example 2"

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

[1016] "Example 3"

[1017] The provision means provides the user with information adjusted by the emotion engine. For example, if the user expresses sadness, the system will provide words of comfort or happy news to correspond to that emotion. This makes it possible to provide information that corresponds to the user's emotions.

[1018] The processing flow of each embodiment will be described below.

[1019] "Example 1"

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

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

[1022] Step 3: Classify the analyzed data.

[1023] Step 4: Integrate the classified data.

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

[1025] Step 6: The generated information is provided according to the search terms.

[1026] Step 7: Recognize user emotions with the emotion engine.

[1027] "Example 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 generated information based on the user's emotions.

[1030] Step 3: If the user expresses joy, the system will prioritize generating positive information to match that emotion.

[1031] "Example 3"

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

[1033] Step 2: If the user expresses sad emotions, the system will provide corresponding responses, such as comforting words or happy news.

[1034] Example 1

[1035] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1036] Conventional Earth observation data analysis systems have difficulty analyzing data and providing information in real time, and do not provide information that responds to user emotions. As a result, in situations where prompt and appropriate information is required, it has been impossible to provide information that meets user needs.

[1037] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1038] In this invention, the server includes a receiving means for receiving Earth observation data, an analyzing means including a generative AI model for analyzing the received Earth observation data, a classifying means for classifying the data analyzed by the analyzing means, an integrating means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information according to search words, and an emotion engine for recognizing user emotions. This enables real-time data analysis and information provision, and further realizes 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 incoming Earth observation data, performing pattern recognition and anomaly detection in the data.

[1042] "Analysis means" refers to devices and software for analyzing received Earth observation data using generative AI models.

[1043] The "classification means" is a device or software for classifying the data analyzed by the analysis means into different categories.

[1044] An "integration means" is a device or software that integrates classified data and compiles it into a single data set.

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

[1046] The "provision means" refers to a device or software that provides the generated information to the user in response to the search word.

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

[1048] MODE FOR CARRYING OUT THE INVENTION

[1049] This invention is a system that receives, analyzes, classifies, integrates, generates, and provides real-time Earth observation data transmitted from Earth observation satellites. It also has the ability to recognize user 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 the data using a high-performance antenna and dedicated receiving software (e.g., SatReceiver Pro). For example, the server receives 10MB 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. The server first preprocesses the data and removes noise. The generative AI model is then used to recognize patterns and detect anomalies in 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, topographical data, environmental data). For example, the server classifies weather data into subcategories such as temperature, humidity, and wind speed.

[1056] Integration Methods

[1057] The server integrates the classified data. For example, the server combines meteorological data and topographical data to predict flood risk. This integration is performed using a database management system (e.g., PostgreSQL). The server stores the integrated data as a single dataset for subsequent processing.

[1058] generation means

[1059] The server generates real-time information based on the integrated data. For example, the server generates a warning message based on the flood risk prediction results. This is done using natural language generation tools (e.g., OpenAI's GPT-4). The server temporarily caches the generated information and prepares it for immediate delivery.

[1060] Providing means

[1061] The server provides the generated information according to the search term. The user enters a search term using a terminal, and the server provides relevant information based on the search term. 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, where the user can view them.

[1062] Emotion Engine

[1063] The server uses an emotion engine (e.g., Affectiva) that recognizes the user's emotions. When the user searches for information through the device, the server analyzes the user's emotions from their facial expressions and voice and adjusts the information provided. For example, if the user looks anxious, the server will provide more detailed explanations or information that provides reassurance. The server analyzes emotional data in real time and reflects this information when providing it.

[1064] Examples and prompts

[1065] Specific examples

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

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

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

[1069] 3. The server classifies the analysis results and integrates the meteorological and topographical data.

[1070] 4. The server generates flood risk warning 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] Prompt Sentence Examples

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

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

[1076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[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 the Earth observation satellite, and the output is raw data temporarily stored in storage. For example, the server receives 10MB of data per second and temporarily stores it in storage.

[1079] Step 2:

[1080] The server uses the 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, the generative AI model is used to recognize patterns and detect anomalies in 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 analyzed data from 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 meteorological data and topographical data to predict flood risk. This integration is performed using a database management system. The server stores the integrated data as a single dataset 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 the flood risk prediction results. A natural language generation tool is used for this generation. The server temporarily caches the generated information and prepares it for immediate provision.

[1087] Step 6:

[1088] The server provides the generated information according to the search words. The input is the search words from the user, and the output is the information as search results. The user enters the search words using their terminal, and the server provides related information based on the search words. 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.

[1089] Step 7:

[1090] The server uses an emotion engine that recognizes the user's emotions. The input is the user's facial and voice data, and the output is analyzed emotional data. When the user searches for information through the device, the server analyzes the user's emotions from their facial expressions and voice and adjusts the information provided. For example, if the user looks anxious, the server will provide more detailed explanations or information that gives a sense of security. The server analyzes emotional data in real time and reflects this when providing information.

[1091] (Application example 1)

[1092] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1093] When 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. In addition, the information provided does not take into account the user's emotions, which has led to issues such as insufficient safety and efficiency of operation.

[1094] The identification processing by the identification 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 analyzing means including a generation AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analyzing means, an integration means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information in accordance with search words, an emotion engine for recognizing user emotions, and a real-time environmental 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 user 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] The "receiving means" is a device or system for receiving earth observation data transmitted from an earth observation satellite.

[1097] "Analysis means" refers to a device or system that includes a generation AI for analyzing received Earth observation data.

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

[1099] The "classification means" is a device or system that classifies the data analyzed by the analysis means into specific categories.

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

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

[1102] The "providing means" is a device or system that provides the generated information in response to the search word.

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

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

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

[1106] Hardware and software used

[1107] Hardware

[1108] On-board computers in autonomous vehicles: Perform data processing and analysis.

[1109] Antenna for receiving 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 incoming data.

[1113] DataClassifier: A module that classifies analysis data.

[1114] DataIntegrator: A module that integrates classification data.

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

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

[1117] Data processing and calculation

[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. Real-time operational information is generated based on the integrated data. Furthermore, the server recognizes the user's emotions and provides information according to those emotions.

[1119] Specific examples

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

[1121] Prompt Sentence Examples

[1122] Create a program that receives data from Earth observation satellites, analyzes weather information, and provides driving information based on the user's emotions. If the user is feeling anxious, warn them, and if the weather is good, notify them of their safety.

[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 emotions.

[1124] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1125] Step 1:

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

[1127] Step 2:

[1128] The server analyzes the received Earth observation data using a generative AI model. The input is the received raw data, and the output is the analyzed data. Specifically, the server launches the AIAnalyzer module and inputs prompts to the generative AI model to analyze the data.

[1129] Step 3:

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

[1131] Step 4:

[1132] The server uses an integration means to combine the classified data into a single piece of information. The input is the classified data, and the output is the integrated data. Specifically, the server uses the DataIntegrator module to integrate the data from each category and generate overall environmental information.

[1133] Step 5:

[1134] The server generates real-time operation information based on the integrated data. The input is the integrated data, and the output is real-time operation 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 the user's emotions. The input is the user's emotional data, and the output is the analyzed emotional information. Specifically, the server uses the EmotionEngine module to analyze the user's facial expressions and voice data and recognize emotions.

[1137] Step 7:

[1138] The server provides appropriate information based on the generated operation information and the user's emotional information. The input is real-time operation information and analyzed emotional information, and the output is optimal operation information provided to the user. Specifically, the server uses the provision means to notify the user of information such as route changes and warnings.

[1139] Example 2

[1140] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1141] Conventional Earth observation data analysis systems did not adequately preprocess or analyze the received data, making it difficult to generate information in real time. Furthermore, they lacked the ability to adjust information based on user sentiment, making it impossible to provide appropriate information to users. This resulted in low user satisfaction and limited the usefulness of the system.

[1142] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[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 generation 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, provision means for providing the generated information in response to search words, collection means for collecting a user's voice and facial expression, analysis means for analyzing the collected voice and facial expression, emotion recognition means for recognizing the user's emotion from the analysis results, and information generation means for generating and displaying information based on the recognized emotion. This enables highly accurate analysis of Earth observation data and provision of information based on the user's emotion.

[1144] "Earth observation data" is data collected to observe the state of the Earth's surface, atmosphere, oceans, etc.

[1145] "Receiving means" refers to a device or function for receiving Earth observation data from an external source.

[1146] "Preprocessing means" refers to a device or function that performs preprocessing such as noise removal and normalization to make the received Earth observation data easier to analyze.

[1147] "Generative AI" refers to algorithms and models that use artificial intelligence techniques 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 that has been preprocessed using generative AI.

[1149] The "classification means" is a device or function that classifies the data analyzed by the analysis means into specific categories or classes.

[1150] An "integration means" is a device or function that integrates classified data and processes it as a single piece of information.

[1151] "Generation means" refers to a device or function that generates real-time information based on the integrated data.

[1152] "Providing means" refers to a device or function that provides the generated information in response to the user's search words.

[1153] "Collection means" refers to devices or functions for collecting the user's voice and facial expressions.

[1154] "Emotion recognition means" refers to a device or function that analyzes collected voice and facial expressions and recognizes the user's emotions.

[1155] "Information generation means" refers to a device or function that generates information based on the recognized emotion and displays it to the user.

[1156] The present invention relates to a system for analyzing earth observation data and providing information based on a user's emotions. A specific embodiment of this system will be described below.

[1157] Earth observation data analysis

[1158] The server has a receiving means for receiving earth observation data. The receiving means receives data from an earth observation satellite using a satellite communication module. The received data is stored in a specified directory.

[1159] The server then uses preprocessing tools to preprocess the received data. This includes loading the data and imputing missing values ​​using Python's Pandas library. For image data, OpenCV is used to remove noise.

[1160] The preprocessed data is then input to 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 the TensorFlow model and inputs the preprocessed data. The model detects climate change and natural disasters in a specific region.

[1161] The 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 piece of information. The generation means generates real-time information based on the integrated data. The generated information is provided by the provision means in response to the user's search words.

[1162] User emotion recognition

[1163] The device has a means of collecting the user's voice and facial expressions. Voice is collected through a microphone, and facial expressions are collected through a camera. The collected data is temporarily stored in the device's memory.

[1164] The device then uses analytics to analyze the collected voice and facial expressions. Google Cloud Speech-to-Text is used for voice analysis, and OpenCV and Dlib are used for facial expression analysis. The device calls the Google Cloud Speech-to-Text API to convert the voice to text. OpenCV and Dlib are used to analyze emotions from the user's facial expressions.

[1165] Based on the analysis results, the device uses emotion recognition means to recognize the user's emotions, and the emotion engine classifies the user's emotions into categories such as "happiness," "sadness," and "anger."

[1166] Based on the recognized emotion, the device generates information using the information generation means and displays it to the user. For example, if the user says, "I'm very happy today," the device generates positive news or an encouraging message. The generated information is displayed on the device's display.

[1167] Specific examples

[1168] The server uses image data acquired from NASA's MODIS satellite. The generative AI analyzes the image data using deep learning frameworks such as TensorFlow and PyTorch. The analysis results can detect abnormal temperature rises or floods in specific areas.

[1169] Example prompt sentence:

[1170] "Analyze the latest image data from NASA's MODIS satellite to detect climate change and natural disasters occurring in a specific region."

[1171] The device collects the user's voice using a microphone and converts it into 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 will display positive news or encouraging messages.

[1172] Example prompt sentence:

[1173] "Analyze the user's voice and facial expressions, and if the user is expressing joy, prioritize generating positive information."

[1174] In this way, the system achieves highly accurate analysis of Earth observation data and provides information based on the user's emotions.

[1175] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1176] Step 1:

[1177] The server receives the earth observation data. The server receives the data from the earth observation satellite through the satellite communication module using the receiving means. The received data is stored in a specified directory.

[1178] Input: Data from Earth observation satellites

[1179] Output: Saved Earth Observation data files

[1180] Step 2:

[1181] The server preprocesses the received Earth observation data. Using the preprocessing method, the data is read and missing values ​​are imputed using Python's Pandas library. For image data, noise is removed using OpenCV.

[1182] Input: Saved Earth Observation data files

[1183] Output: Preprocessed Earth Observation data

[1184] Step 3:

[1185] The server inputs the preprocessed data into a generative AI model for analysis. The generative AI model is built using TensorFlow and PyTorch. The server loads the TensorFlow model and inputs the preprocessed data. The model detects climate change and natural disasters in a specific area.

[1186] Input: Preprocessed Earth Observation data

[1187] Output: Analysis results (detection results of climate change and natural disasters)

[1188] Step 4:

[1189] The server classifies the data analyzed by the analysis means, and uses the classification means to classify 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 and processes the classified data as a single piece of information using an integration means.

[1194] Input: Classified data

[1195] Output: Consolidated data

[1196] Step 6:

[1197] The server generates real-time information based on the integrated data. The generating means generates information based on the integrated data.

[1198] Input: Integrated data

[1199] Output: Generated real-time information

[1200] Step 7:

[1201] The server provides the generated information in response to the search words, and uses the providing means to provide information based on the user's search words.

[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. The device uses a collection means to collect the user's voice and facial expressions through a microphone and a camera. The collected data is temporarily stored in the device's memory.

[1206] Input: User's voice and facial expressions

[1207] Output: Collected voice and facial expression data

[1208] Step 9:

[1209] The device analyzes the collected voice and facial expressions. Using the analysis method, Google Cloud Speech-to-Text is used for voice analysis, and OpenCV and Dlib are used for facial expression analysis. The device calls the Google Cloud Speech-to-Text API to convert the voice to text. OpenCV and Dlib are used to analyze emotions from the user's facial expressions.

[1210] Input: Collected voice and facial expression data

[1211] Output: Analysis results (speech-to-text conversion results, facial expression analysis results)

[1212] Step 10:

[1213] The device recognizes the user's emotions from the analysis results, and classifies the user's emotions based on the analysis results using the emotion recognition means.

[1214] Input: Analysis results

[1215] Output: Recognized emotion

[1216] Step 11:

[1217] The device generates and displays information based on the recognized emotion. Information tailored to the user's emotion is generated using the information generation means and displayed on the device display. For example, if the user says, "I'm very happy today," positive news or an encouraging message is generated.

[1218] Input: Recognized emotion

[1219] Output: the information generated and displayed

[1220] (Application example 2)

[1221] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1222] Conventional Earth observation data analysis systems have proven effective in detecting climate change and natural disasters, but 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 increase users' anxiety.

[1223] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1224] In this invention, the server includes a receiving means for receiving earth observation data, an analyzing means including a generating AI for analyzing the received earth observation data, a classifying means for classifying the data analyzed by the analyzing means, an integrating means for integrating the classified data, a generating means for generating real-time information based on the integrated data, an emotion recognizing means for recognizing a user's emotion, an adjusting means for adjusting the generated information based on the emotion recognized by the emotion recognizing means, and a providing means for providing the generated information according to a search word. This makes it possible to provide appropriate warnings and advice according to the user's emotion.

[1225] "Earth observation data" refers to data recorded using satellites and other observation equipment to record the state of the Earth's surface, atmosphere, oceans, etc.

[1226] "Receiving means" refers to a device or system for receiving Earth observation data.

[1227] "Analysis means" refers to a device or system that includes a generation 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] The "classification means" is a device or system that classifies the data analyzed by the analysis means into specific categories.

[1230] An "aggregator" is a device or system that combines disaggregated data into a single, integrated data set.

[1231] A "generation means" is a device or system that generates real-time information based on the integrated data.

[1232] An "emotion recognition means" is a device or system that recognizes emotions from a user's tone of voice, facial expression, or text input.

[1233] The "adjustment means" is a device or system that adjusts the generated information based on the emotion recognized by the emotion recognition means.

[1234] "Providing means" refers to a device or system that provides the generated information to the user in response to the search word.

[1235] The system for implementing this invention includes a series of processes for receiving and analyzing earth observation data, and providing tailored information based on the user's emotions. Specific embodiments of the system are described below.

[1236] Hardware and software used

[1237] Hardware: Smartphone

[1238] Software: TensorFlow, OpenCV, Transformers (Hugging Face)

[1239] System configuration

[1240] 1. Receiving means:

[1241] The server receives Earth observation data from satellites and other observation instruments, including satellite images 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 risk 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 aggregates the classified data into a single, integrated data set, thereby unifying information from multiple data sources.

[1248] 5. Generation means:

[1249] The server generates real-time information based on the integrated data, including information on current weather conditions and the risk of natural disasters.

[1250] 6. Emotion recognition means:

[1251] The device (smartphone) recognizes emotions from the user's tone of voice, facial expressions, and text input using an emotion recognition model using Transformers.

[1252] 7. Adjustment means:

[1253] The server adjusts the generated information based on the emotion recognized by the emotion recognition means, for example, if the user is showing signs of anxiety, it prioritizes providing reassuring information.

[1254] 8. Means of provision:

[1255] The device then provides the generated information to the user based on the search term, allowing the user to quickly obtain the information they need.

[1256] Specific examples

[1257] Prompt Sentence Examples

[1258] User text input: "I'm worried about the weather these days."

[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 a user types, "I'm worried about the weather these days," the system recognizes the emotion and analyzes the latest satellite images to provide the current weather conditions. If the user expresses anxiety, the system prioritizes providing reassuring information.

[1261] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1262] Step 1:

[1263] The server receives Earth observation data from satellites and other observation equipment. The input is satellite images and meteorological data, and the output is the received raw data. Specifically, the server periodically retrieves data from the observation equipment 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 climate change and natural disaster risks.

[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 sorts the analysis results into categories such as "climate change," "flood," and "earthquake."

[1268] Step 4:

[1269] The server aggregates 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 aggregates 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 on the current climate situation and the risk of natural disasters.

[1272] Step 6:

[1273] The device recognizes emotions from the user's tone of voice, facial expressions, and text input. The input is the user's voice data and text data, and the output is 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 generated information based on the emotions recognized by the emotion recognition means. 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 generated information to the user according to the search words. The input is the information adjusted in step 7 and the user's search words, and the output is the information provided to the user. Specifically, the terminal filters the information based on the user's search words and displays appropriate information.

[1278] Example 3

[1279] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1280] Conventional Earth observation data analysis systems have a complex process for analyzing, classifying, and integrating received data, making it difficult to provide information in real time. Furthermore, they are unable to provide information that responds to user emotions, which hinders the user experience.

[1281] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1282] In this invention, the server includes a receiving means for receiving Earth observation data, an analyzing means including a generative AI model for analyzing the received Earth observation data, a classifying means for classifying the data analyzed by the analyzing means, an integrating means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information in response to search words, and an emotion engine by which the providing means provides information corresponding to the user's emotions. This makes it possible to provide information in real time and further realize the provision of information corresponding to the user's emotions.

[1283] "Receiving means" means a device or system for receiving Earth observation data.

[1284] An "analysis means" is 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] The "classification means" is a device or system that classifies the data analyzed by the analysis means into categories.

[1287] An "integration means" is a device or system that integrates classified data into a single piece of information.

[1288] A "generator" is a device or system that generates real-time information based on the integrated data.

[1289] The "providing means" is a device or system that provides the generated information in response to the user's search words.

[1290] An "emotion engine" is a device or system that analyzes a user's emotions and provides information corresponding to those emotions.

[1291] MODE FOR CARRYING OUT THE INVENTION

[1292] The present invention relates to a system for receiving, analyzing, classifying, and integrating earth observation data, generating real-time information, and providing information that responds to user emotions. Specific embodiments of this system will be described below.

[1293] 1. Receiving Data

[1294] The server includes a receiving unit for receiving earth observation data. This receiving unit is a device that receives data via satellite communication or the Internet. For example, it receives meteorological data and environmental data transmitted from earth observation satellites.

[1295] 2. Data Analysis

[1296] The server is equipped with an analysis means including a generative AI model for analyzing the received Earth observation data. The analysis means includes a generative AI model (e.g., OpenAI's GPT-4) that uses natural language processing technology. The server analyzes the received data and extracts relevant information.

[1297] 3. Data Classification

[1298] The server is equipped with a classification method for classifying the analyzed data. This classification method uses a machine learning algorithm (for example, 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 means for integrating the classified data. This integration means uses a database management system (e.g., MySQL or PostgreSQL). The server integrates the data on temperature, precipitation, and wind speed to generate comprehensive information.

[1301] 5. Real-time information generation

[1302] The server has a generating means for generating real-time information based on the integrated data. This generating means again uses a generative AI model. For example, if a user searches for "weather in Tokyo," the server generates the latest information about the weather in Tokyo from the integrated data and provides it to the user.

[1303] 6. Providing emotionally relevant information

[1304] The server is equipped with an emotion engine that provides information corresponding to the user's emotions. This emotion engine uses an emotion analysis tool (e.g., IBM Watson's Tone Analyzer). For example, if the user expresses sadness, the server provides comforting words or happy news.

[1305] Specific examples

[1306] Consider a case where a user searches for "Weather in Tokyo" and the system provides information based on the search term. If the user indicates sad emotion, the system will provide a message such as "The weather in Tokyo is sunny. It will be a great day today. Cheer up!"

[1307] Prompt Sentence Examples

[1308] "If a user searches for 'Tokyo weather' and expresses sadness, generate weather information with comforting words."

[1309] In this way, it is possible to explain a specific embodiment of the system while clarifying the roles of the server, the terminal, and the user. The flow of the identification process in Example 3 will be explained with reference to FIG.

[1310] Step 1:

[1311] The user inputs a search term using the terminal. For example, the user inputs "Weather in Tokyo." The input search term is sent to the server.

[1312] Step 2:

[1313] The server inputs the received search words into the generative AI model and performs analysis. Specifically, the server analyzes the search word "weather in Tokyo" using natural language processing technology and extracts related information. The input is the search word, and the output is the analyzed information.

[1314] Step 3:

[1315] The server classifies the analyzed information into categories using a classification method. Specifically, the server uses a machine learning algorithm 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 an integration means. Specifically, the server uses a database management system to integrate the temperature, precipitation, and wind speed data to generate comprehensive information. The input is the classified data, and the output is the integrated data.

[1318] Step 5:

[1319] The server generates real-time information based on the integrated data. Specifically, the server again uses the generative AI model to generate information according to the user's search terms. For example, if a user searches for "weather in Tokyo," the server generates the latest information about the weather in Tokyo from the integrated data and provides it to the user. The input is the integrated data, and the output is the generated real-time information.

[1320] Step 6:

[1321] The server uses a providing means to provide information corresponding to the user's emotions. Specifically, the server uses an emotion engine to analyze the user's emotions and generate information corresponding to those emotions. For example, if the user expresses sadness, the server provides words of comfort or happy news. The input is the user's emotional information, and the output is information corresponding to the emotions.

[1322] (Application example 3)

[1323] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1324] Conventional information provision systems were able to provide information based on the user's search terms, but were unable to provide personalized information according to the user's emotions. As a result, they were unable to provide information appropriate to the user's emotional state, resulting in a lack of improvement in the user experience.

[1325] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a receiving means for receiving Earth observation data, an analyzing means including a generation AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analyzing means, an integration means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information in accordance with search words, an emotion analysis means for analyzing the user's emotions, an adjustment means for adjusting 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 a device or system for receiving Earth observation data.

[1327] "Analysis means" refers to a device or system that includes a generation AI for analyzing received Earth observation data.

[1328] The "classification means" is a device or system for classifying the data analyzed by the analysis means.

[1329] An "integration means" is a device or system for integrating classified data.

[1330] "Generation means" refers to a device or system for generating real-time information based on the integrated data.

[1331] The "provision means" refers to a device or system for providing the generated information in response to the search word.

[1332] "Emotion analysis means" refers to a device or system for analyzing a user's emotions.

[1333] The "adjustment means" is a device or system for adjusting information based on the emotion data analyzed by the emotion analysis means.

[1334] A system for implementing the present 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 analyzing means, where it is analyzed using a generative AI model. The analyzing means is software for extracting the characteristics of the data and extracting the necessary information.

[1336] The analyzed data is classified by a classification means, which includes an algorithm for dividing the data into categories according to the type and content of the data. The classified data is then integrated by an integration means, which is software for combining multiple pieces of data into a single piece of information.

[1337] Based on the integrated data, a generating means generates real-time information. The generating means is software for generating information required by a user using a generative AI model. The generated information is provided to the user by a providing means. The providing means includes an interface for displaying information according to the user's search words.

[1338] Furthermore, an emotion analysis means is used to analyze the user's emotions. The emotion analysis means is software for analyzing the user's facial expressions and tone of voice using the smartphone's camera and microphone. The analyzed emotion data is used by the adjustment means to adjust the information. The adjustment means includes an algorithm for personalizing the information according to the user's emotions.

[1339] As a specific example, if a user searches for "Tokyo weather" and expresses sad emotions, the system will provide information about "Tokyo weather" along with positive news and encouraging words to comfort the user. An example of a prompt sentence is, "If a user searches for 'Tokyo weather' and expresses sad emotions, please generate positive news and encouraging words to comfort the user along with information about Tokyo weather."

[1340] In this way, personalized information can be provided based not only on the user's search terms, but also on the user's emotional state.

[1341] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1342] Step 1:

[1343] A user launches an application on their smartphone 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 the server. The input data is the user's facial expressions and voice, and the output data is the facial expression and voice data sent to the server.

[1346] Step 3:

[1347] The server receives the Earth observation data using the receiving means, where the input data is the 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 means. It uses a generative AI model to extract features from the data and extract the necessary information. 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 a classification means, dividing the data into categories according to the type and content of the data. The input data is the analyzed data, and the output data is the classified data.

[1352] Step 6:

[1353] The server integrates the classified data using an integration means, and combines multiple pieces of data into a single piece of information. The input data is the classified data, and the output data is the integrated data.

[1354] Step 7:

[1355] The server generates real-time information based on the integrated data using a generation means. The server uses a generative AI model to generate the information required by the user. The input data is the integrated data, and the output data is the generated information.

[1356] Step 8:

[1357] The server analyzes the user's facial expression and voice data using an emotion analysis means, and identifies the user's emotion using an emotion analysis algorithm. The input data is the user's facial expression and voice data, and the output data is the analyzed emotion data.

[1358] Step 9:

[1359] The server adjusts the information based on the analyzed emotion data using the adjustment means, personalizing the information according to the user's emotion. The input data is the analyzed emotion data and the generated information, and the output data is the adjusted information.

[1360] Step 10:

[1361] The server provides the adjusted information to the user using the providing means, and displays the information according to the user's search words. The input data is the adjusted information, and the 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1363] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1364] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1365] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1366] [Third embodiment]

[1367] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1368] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1369] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1370] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1371] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1372] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1373] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1374] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1375] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1376] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1377] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1378] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1379] "Example 1"

[1380] One embodiment of the present invention is a system including a receiving means for receiving earth observation data transmitted from an earth observation satellite, the receiving means being capable of receiving data from the satellite in real time.

[1381] "Example 2"

[1382] Next, there is an analysis method that includes a generation AI for analyzing the received Earth observation data. This generation AI uses AI techniques such as deep learning to analyze the Earth observation data. For example, it can detect climate change or the occurrence of natural disasters in a specific area from satellite images.

[1383] "Example 3"

[1384] Furthermore, there are classification means for classifying the analyzed data and integration means for integrating the classified data. These means allow the analyzed data to be appropriately classified and integrated, making it possible to efficiently provide the information required by users.

[1385] "Example 4"

[1386] Finally, there is a provision method that generates real-time information based on the integrated data and provides the information generated in response to the user's search terms. For example, if a user searches for "weather in Tokyo," the system generates information about the weather in Tokyo from the data analyzed, classified, and integrated by the generative AI and provides it to the user.

[1387] The processing flow of each embodiment will be described below.

[1388] "Example 1"

[1389] Step 1: Receive earth observation data transmitted from an earth observation satellite. This reception is performed in real time using a specific receiving device.

[1390] "Example 2"

[1391] Step 1: The received Earth observation data is analyzed by the generated AI. This analysis is carried out using AI techniques such as deep learning, and it is possible to detect climate change and the occurrence of natural disasters in specific areas from satellite images.

[1392] "Example 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, and the analyzed data is appropriately classified and integrated.

[1395] "Example 4"

[1396] Step 1: Generate real-time information based on the integrated data. This generation is performed using a specific generation algorithm.

[1397] Step 2: Provide information generated based on the user's search terms. For example, if a user searches for "weather in Tokyo," the system will generate information about the weather in Tokyo from the data analyzed, classified, and integrated by the generative AI and provide it to the user.

[1398] Example 1

[1399] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1400] Conventional systems for receiving and analyzing Earth observation data have not been able to efficiently receive and analyze data in real time, making it difficult to quickly provide users with the information they need. Furthermore, the methods for storing and providing analyzed data are inadequate, resulting in the problem of users having to wait a long time to access the data they need.

[1401] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a receiving means for receiving earth observation data, an analyzing means for analyzing the received earth observation data, a storing means for storing the data analyzed by the analyzing means, a providing means for providing the stored data, and an accessing means for a user to access the data through the providing means. This makes it possible to receive data transmitted from earth observation satellites in real time and quickly store and provide the analyzed data.

[1402] "Receiving means" refers to a device or system for receiving data transmitted from an Earth observation satellite.

[1403] "Analysis means" refers to a device or system that analyzes the received Earth observation data and extracts necessary information.

[1404] "Storage means" refers to a device or system for storing analyzed data in a database or the like.

[1405] The "providing means" is a device or system for providing the stored data to users or other systems.

[1406] "Access means" refers to a device or system that allows a user to access stored data through a provision means.

[1407] The present invention relates to a system for receiving, analyzing, storing, and providing data transmitted from an earth observation satellite in real time. Specific embodiments of this system will be described below.

[1408] The server uses a highly sensitive antenna and receiver to receive Earth observation data. This allows it to receive data transmitted from Earth observation satellites in real time. The received data is sent to the server through data reception software.

[1409] The server then uses data analysis software to analyze the data it receives. This analysis process involves shaping, filtering, and extracting the necessary information, such as temperature, humidity, and wind speed in the case of weather data.

[1410] The analyzed data is stored in a database on the server. When the data is saved, metadata such as the data type and timestamp is also saved, making it easier to search for the data later.

[1411] The stored data is provided to users or other systems through a provisioning method. Users can access the API using their terminal to obtain 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 specific example, the process by which a server receives, analyzes, stores, and provides weather data transmitted from an Earth observation satellite is shown below.

[1413] Example: A server receives weather data transmitted from an Earth observation satellite using a high-sensitivity antenna and receiver. The received data is sent to the server via data reception software, and information such as temperature, humidity, and wind speed is extracted using data analysis software. The analyzed data is stored in a database, and users can access the API using their devices to obtain the required data.

[1414] Example prompt sentence:

[1415] Your goal is to create a program that receives real-time weather data transmitted from Earth observation satellites and extracts information such as temperature, humidity, and wind speed. The hardware required is a high-sensitivity antenna and receiver, and the software required is data reception software and data analysis software. The analyzed data will be stored in a database and provided to users via an API.

[1416] In this way, a system can be constructed in which the server can efficiently receive, analyze, store, and provide data from Earth observation satellites.

[1417] The flow of the identification process in the first embodiment will be described with reference to FIG.

[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 launches the data reception software and configures it to receive signals from the satellite. The input is the installation and connection of the high-sensitivity antenna and receiver, and the output is the launch of the data reception software and completion of the configuration.

[1420] Step 2: Receiving data

[1421] The server uses a highly sensitive antenna and receiver to receive data transmitted from Earth observation satellites in real time. The received data is transferred to the server through data reception software. The input is data from the Earth observation satellite, 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 the data analysis software and begins the analysis. This analysis process involves shaping and filtering the data and extracting the necessary information. 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 saves the parsed data in a database. When saving, it also saves metadata such as the data type and timestamp. The input is the parsed data, and the output is the data saved in the database. Specifically, the server connects to the database and inserts data using an SQL query.

[1426] Step 5: Provide data

[1427] The server makes the stored data accessible through an API to provide it to users and other systems. Users can access the API using their terminals to obtain the data they need. The input is the request from the user, and the output is the provided data. Specifically, the server receives the API request, retrieves the required data from the database, and returns it to the user.

[1428] In this way, the server creates a system that can efficiently receive, analyze, store, and provide data from Earth observation satellites.

[1429] (Application example 1)

[1430] Next, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal."

[1431] There is a need for a system that can quickly detect natural disasters and suspicious activities using real-time data from Earth observation satellites and issue appropriate warnings to users. However, conventional systems have issues with the process from receiving data to analyzing it and issuing warnings being inefficient and lacking real-time capabilities. This means that users are unable to receive information at the appropriate time, making it difficult to respond quickly.

[1432] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1433] In this invention, the server includes a receiving means for receiving earth observation data, an analyzing means including a generating AI for analyzing the received earth observation data, and a classifying means for classifying the data analyzed by the analyzing 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 containing information about the Earth's surface and atmosphere transmitted from Earth observation satellites.

[1435] The "receiving means" is a device or system for receiving earth observation data transmitted from an earth observation satellite in real time.

[1436] "Analysis means" means a device or system that includes a generation AI for analyzing received Earth observation data.

[1437] "Generative AI" is an artificial intelligence technology that analyzes incoming data and detects specific patterns and anomalies.

[1438] The "classification means" is a device or system for classifying the data analyzed by the analysis means into specific categories.

[1439] An "integration means" is a device or system for integrating classified data and generating overall information.

[1440] A "generator" is a device or system for generating real-time information based on the integrated data.

[1441] The "provision means" is a device or system for providing the generated information to the user in accordance with the search word.

[1442] An "alert means" is a device or system for issuing an alert to a user based on the generated information.

[1443] A system for carrying out the present invention receives and analyzes earth observation data transmitted from an earth observation satellite in real time, and issues a warning to a user. A specific embodiment of the system will be 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, it uses a dedicated receiving antenna or data receiving server.

[1447] 2. Analysis means: A device or system that includes generative AI for analyzing received Earth observation data. The data is analyzed using a generative AI model such as Python or TensorFlow.

[1448] 3. Classification means: A device or system that classifies the data analyzed by the analysis means into specific categories. A database management system (DBMS) is used to efficiently classify data.

[1449] 4. Integration means: A device or system that integrates classified data and generates comprehensive information. The integrated data is updated in real time.

[1450] 5. Generator: A device or system that generates real-time information based on the integrated data. The generated information is provided to users.

[1451] 6. Means of provision: A device or system that provides the generated information to users in response to search words. Information is provided through smartphone apps or web applications.

[1452] 7. Alerting means: A device or system that issues an alert to the user based on the generated information. Alerts are issued using push notifications or email notifications.

[1453] Program processing

[1454] The server receives Earth observation data and analyzes it using the AI ​​generator. The analyzed data is classified into specific categories and integrated. Real-time information is generated based on the integrated data and provided to users. Furthermore, warnings are issued to users based on the generated information.

[1455] Hardware and software used

[1456] Hardware: Receiving antenna, data receiving server, smartphone

[1457] Software: Python, TensorFlow, database management system (DBMS), smartphone app, web application

[1458] Specific examples

[1459] For example, it receives data transmitted from Earth observation satellites and uses generative AI models to detect natural disasters (e.g., earthquakes, floods) and suspicious activities (e.g., forest fires, illegal fishing). The detected information is provided to users in real time, and warnings are issued as needed.

[1460] Example prompts for generative AI models

[1461] Your goal is to write a Python program that analyzes real-time data from Earth observation satellites to detect natural disasters and suspicious activity. The data should be retrieved from an API and the program should include the ability to alert users based on the analysis results.

[1462] In this way, the system can efficiently utilize Earth observation data and provide users with prompt and appropriate information and warnings.

[1463] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1464] Step 1:

[1465] The server receives Earth observation data transmitted from Earth observation satellites. Using a receiving means, the server acquires data through a dedicated receiving antenna or a data receiving server. The input is data from the Earth observation satellite, and the output is the received raw data.

[1466] Step 2:

[1467] The server analyzes the received Earth observation data using a generative AI model. It uses Python and TensorFlow as the analytical 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, input to the model, and obtaining the analysis results.

[1468] Step 3:

[1469] The server classifies the analyzed data into specific categories. It uses a database management system (DBMS) as a classification tool 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 the classified data to generate comprehensive information. It uses an integration means to update the data in real time and generate the integrated information. The input is the classified data, and the output is the 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 the integrated data. Using the generation means, it generates information to provide to users. The input is the integrated data, and the output is the generated information. Specific operations include data format conversion and information generation.

[1474] Step 6:

[1475] The server provides the generated information to the user according to the search terms. The server uses a smartphone app or web application to provide the information to the user. The input is the generated information and the user's search terms, and the output is the information provided to the user. Specific operations include receiving search terms, filtering the information, and providing the information.

[1476] Step 7:

[1477] The server issues a warning to the user based on the generated information. The warning method is push notification or email notification. The input is the generated information, and the output is the warning issued to the user. Specific operations include checking the warning conditions, generating the notification, and sending the notification.

[1478] Example 2

[1479] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1480] In conventional Earth observation data analysis systems, preprocessing and analysis of received data is often done manually, which is inefficient. Furthermore, classification and integration of analysis results are insufficient, making it difficult to provide information in real time. Furthermore, information provided based on user search terms may not be appropriate.

[1481] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 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 provision means for providing the generated information in accordance with a search term. This enables efficient preprocessing, analysis, classification, and integration of Earth observation data, and realizes real-time information provision. It also enables appropriate information to be provided based on the user's search term.

[1482] "Receiving means" means any device or facility for receiving Earth observation data from a satellite or other data source.

[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 that include generative AI for analyzing preprocessed data.

[1485] "Generative AI" is an artificial intelligence that uses deep learning technology to analyze Earth observation data and detect climate change and natural disasters in specific regions.

[1486] The "classification means" is a device or function that classifies the data analyzed by the analysis means into specific categories or patterns.

[1487] An "integration means" is a device or function that integrates classified data and generates overall information.

[1488] "Generation means" refers to a device or function that generates real-time information based on the integrated data.

[1489] "Providing means" refers to a device or function that provides the generated information in response to the user's search words.

[1490] A "search term" is a keyword or phrase that a user enters to search for specific information.

[1491] MODE FOR CARRYING OUT THE INVENTION

[1492] The present invention relates to a system for efficiently analyzing earth observation data and providing real-time information. Specific embodiments of this system will be described below.

[1493] 1. System Configuration

[1494] The system includes the following main means:

[1495] Reception means: Receives Earth observation data from satellites and other data sources.

[1496] Preprocessing means: Converting received Earth observation data into a format suitable for analysis.

[1497] Analysis tools: Includes generative AI for analyzing pre-processed data.

[1498] Classification means: Classifying data analyzed by analytical means into specific categories or patterns.

[1499] Integration means: Integrates classified data to generate overall information.

[1500] Generation method: Generate real-time information based on the integrated data.

[1501] Provision method: The generated information is provided according to the user's search terms.

[1502] 2. Hardware and Software Used

[1503] Server: A server with high-performance computing power is used to receive, preprocess, analyze, classify, integrate, generate, and provide data.

[1504] Generative AI model: Earth observation data is analyzed using a generative AI model that uses deep learning technology.

[1505] Database: Use a database to store the analysis results and integrated data.

[1506] User device: The user uses a device (PC, smartphone, etc.) to enter search terms and check the analysis results.

[1507] 3. Specific Examples

[1508] If a user wants to analyze climate change in a particular region, they follow these steps:

[1509] 1. Data reception:

[1510] Users upload satellite image data of the area they wish 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] For numerical data, the server imputes missing values ​​and performs normalization.

[1515] 3. Applying generative AI models:

[1516] The server inputs the preprocessed data into a generative 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 generative AI model.

[1520] The user checks the analysis results provided by the server and takes necessary measures.

[1521] Prompt Sentence Examples

[1522] "Use the satellite imagery data below to analyze climate change in a specific region. Preprocess the data and apply a generative AI model to detect patterns of climate change."

[1523] In this way, the server, terminals, and users each play their respective roles, making this a system that enables efficient analysis of Earth observation data and the provision of real-time information.

[1524] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1525] Step 1: Receiving data

[1526] The server receives Earth observation data from satellites and other data sources. The input is satellite image data and numerical data. The output is 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 pre-processes the received Earth observation data. The input is the received raw data. The output is the pre-processed data.

[1530] Specific behavior:

[1531] For image data: The server applies a noise reduction filter and adjusts the resolution.

[1532] For numeric data: The server imputes missing values ​​and normalizes the data.

[1533] Step 3: Applying the generative AI model

[1534] The server inputs the preprocessed data into the generative AI model. The input is the preprocessed data. The output is the analysis result.

[1535] How it works: The server feeds the preprocessed data into a generative AI model, which then analyzes the data to detect patterns of climate change and natural disasters.

[1536] Step 4: Data Classification

[1537] The server classifies the analysis results obtained from the generative AI model. The input is the analysis results. The output is the classified data.

[1538] What happens: The server applies an algorithm to classify the analysis results into specific categories or patterns.

[1539] Step 5: Data Integration

[1540] The server aggregates the classified data. The input is the classified data. The output is the aggregated 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 the integrated data. The input is the integrated data. The output is the generated real-time information.

[1544] What it does: The server analyzes the combined data and applies algorithms to generate real-time information.

[1545] Step 7: Provide information

[1546] The server provides the generated information according to the user's search words. The input is the user's search words 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 them, and provides it to the user's device.

[1548] In this way, the server, terminals, and users each play their respective roles, making this a system that enables efficient analysis of Earth observation data and the provision of real-time information.

[1549] (Application example 2)

[1550] Next, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal."

[1551] Conventional Earth observation data analysis systems have had difficulty predicting climate change and natural disasters in real time and notifying users promptly. Furthermore, they lacked the functionality to provide users with the information they needed based on search terms, making it difficult to obtain information. Therefore, in today's world, where rapid responses to climate change and natural disasters are required, a more efficient and prompt information provision system is needed.

[1552] The identification processing by the identification 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 analyzing means including a generation AI for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analyzing means, an integration means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information in accordance with a search word, a prediction means for receiving satellite image data and predicting the occurrence of climate change or 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 or natural disasters and notify the user in real time.

[1553] "Earth observation data" is data collected to observe the state of the Earth's surface, atmosphere, oceans, etc.

[1554] "Receiving means" refers to a device or system for receiving Earth observation data.

[1555] "Generative AI" is an artificial intelligence model that uses artificial intelligence techniques such as deep learning to analyze data and generate new information.

[1556] "Analysis means" refers to a device or system for analyzing received Earth observation data.

[1557] A "classification means" is a device or system for classifying analyzed data into specific categories.

[1558] An "integration means" is a device or system for integrating classified data and generating overall information.

[1559] "Generation means" refers to a device or system for generating real-time information based on the integrated data.

[1560] The "provision means" refers to a device or system for providing the generated information in response to the user's search words.

[1561] "Satellite image data" is image data of the Earth taken from a satellite.

[1562] "Prediction means" refers to devices or systems that analyze satellite image data and predict climate change and the occurrence of natural disasters.

[1563] The "notification means" is a device or system for notifying the user of predicted information in real time.

[1564] A system for implementing the present invention includes a series of processes for receiving, analyzing, classifying, and integrating Earth observation data, generating real-time information, and providing it to users. A specific embodiment of this system will be described below.

[1565] First, the server receives image data transmitted from the satellite using a receiving means for receiving Earth observation data, the receiving means including a communication module for obtaining the data via an internet connection.

[1566] The received Earth observation data is then analyzed by analytical means, including generative AI models using deep learning frameworks such as TensorFlow and Keras. The generative AI models receive satellite image data as input and predict climate change and the occurrence of natural disasters.

[1567] The analyzed data is classified into specific categories using a classification mechanism, such as data on climate change or data on natural disasters, etc. This classification mechanism is implemented using a database management system (DBMS).

[1568] The classified data is then integrated to generate the overall information by an integration method, which is implemented using a data integration tool or script.

[1569] Based on the integrated data, the generating means generates real-time information, which is then provided to the user based on the results of the data analysis.

[1570] The generated information is provided to the user in response to the user's search words by the providing means, which provides the information to the user through a web server or a mobile application.

[1571] Furthermore, a prediction means for receiving satellite image data and predicting the occurrence of climate change and natural disasters is included. This prediction means predicts the occurrence of climate change and natural disasters from the satellite image data using a generative AI model.

[1572] The predicted information is notified to the user in real time via a notification method implemented using the push notification function or email notification function of the smartphone.

[1573] As a concrete example, consider a scenario in which a user launches an application on their smartphone to retrieve and analyze the latest satellite imagery. An example of a prompt to be input to the generative AI model is as follows:

[1574] "Analyze the latest satellite images to predict climate change and natural disasters in specific regions."

[1575] By inputting this prompt into the generative AI model, the model will analyze satellite images and output predictions for climate change and the occurrence of natural disasters.

[1576] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1577] Step 1:

[1578] The server receives Earth observation data, specifically image data transmitted from satellites via an internet connection. The input is image data from the satellites, and the output is the received image data.

[1579] Step 2:

[1580] The server analyzes the received Earth observation data using analytical methods including generative AI. Specifically, image data is input into a generative AI model using deep learning frameworks such as TensorFlow and Keras to predict climate change and the occurrence of 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 a classification method. Specifically, it uses a database management system (DBMS) to classify the data into categories such as data on climate change and data on natural disasters. The input is the analysis results, and the output is the classified data.

[1583] Step 4:

[1584] The server integrates the classified data using an integration method. Specifically, it uses a data integration tool or script to generate a holistic information. The input is the classified data, and the output is the integrated data.

[1585] Step 5:

[1586] The server generates real-time information based on the integrated data using a generation means. Specifically, it generates information to provide to users based on the results of data analysis. The input is the integrated data, and the output is the generated information.

[1587] Step 6:

[1588] The server provides the generated information to the user in response to the user's search words through a provisioning means. Specifically, the server provides the information to the user through a web server or mobile application. The input is the generated information and the user's search words, and the output is the information provided to the user.

[1589] Step 7:

[1590] The server receives satellite image data and uses a prediction means to predict climate change and the occurrence of natural disasters. Specifically, a generative AI model is used to predict climate change and the occurrence of 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 of the predicted information in real time via a notification method. Specifically, this is implemented using the push notification function or email notification function of a smartphone. The input is the prediction result, and the output is the information to be notified to the user.

[1593] Example 3

[1594] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1595] In conventional Earth observation data analysis systems, data analysis, classification, and integration were often done manually, making it difficult to provide information in real time. Furthermore, information was not provided promptly in response to user search terms, making it impossible for users to obtain the information they needed in a timely manner. This resulted in issues such as reduced user convenience and a loss of the usefulness of the information.

[1596] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1597] In this invention, the server includes a receiving means for receiving Earth observation data, an analyzing means including a generative AI model for analyzing the received Earth observation data, a classification means for classifying the data analyzed by the analyzing means, an integration means for integrating the classified data, a generating means for generating real-time information based on the integrated data, and a providing means for providing the generated information in response to search terms. This automates the analysis, classification, and integration of data, enabling real-time information provision. Furthermore, information in response to a user's search terms can be quickly provided, improving user convenience.

[1598] "Receiving means" refers to a device or function for receiving Earth observation data.

[1599] "Analysis means" means 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] The "classification means" is a device or function that classifies the data analyzed by the analysis means into specific categories.

[1602] An "integration means" is a device or function that integrates classified data into a single information set.

[1603] A "generation means" is a device or function that generates real-time information based on the integrated data.

[1604] The "providing means" is a device or function that provides the generated information in response to the user's search words.

[1605] A "search word" is a keyword or phrase that a user enters when searching for information.

[1606] "Real-time information" is information generated based on current conditions and the latest data.

[1607] This invention is a system that efficiently analyzes Earth observation data and provides real-time information according to user search terms. This system operates in cooperation with a server, a terminal, and a user.

[1608] First, a user inputs a search term using a device (e.g., a smartphone or a PC). For example, they input "weather in Tokyo." The device then sends this search term to the server.

[1609] The server receives the Earth observation data using the receiving means. The received data is analyzed by the analyzing means. A generative AI model (e.g., a model using natural language processing technology) is used for this analysis. Specifically, a general natural language processing model can be used as the generative AI model.

[1610] The analyzed data is then classified into specific categories using classification techniques. For example, weather data is classified into categories such as "temperature," "precipitation probability," and "wind speed." This classification is achieved using machine learning algorithms (e.g., k-means clustering).

[1611] The classified data is then integrated into a single information set using an integration tool, such as a database management system (e.g., MySQL or Amazon Redshift), which brings together related data into a single information set.

[1612] Based on the integrated data, the generation means generates real-time information. This generation uses a generative AI model. For example, if a user searches for "weather in Tokyo," 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 provision means. The user can check the latest weather information for Tokyo on the screen of their device. An example of a specific prompt sentence is, "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 provision. In addition, information corresponding to the user's search words can be provided quickly, improving user convenience. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[1615] Step 1:

[1616] The user enters a search term

[1617] A user uses a terminal to input a search term. For example, the user inputs "Weather in Tokyo." 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 the search words sent from the terminal. Using the receiving means, it also receives the Earth observation data at the same time. The input data is the search words and the Earth observation data, and the output is the data passed to the analyzing means.

[1621] Step 3:

[1622] The server analyzes the data

[1623] The server uses an analysis means to analyze the received Earth observation data. A generative AI model is used for this analysis. Specifically, a search term is input as a prompt into the generative AI model, and related data is extracted. The input data is the Earth observation data and the search term, and the output is the analyzed data. Specifically, the server inputs the following prompt sentence into the generative AI model: "A user has searched for 'weather in Tokyo.' Please provide the latest weather information for Tokyo based on this search term."

[1624] Step 4:

[1625] The server classifies the data

[1626] The server classifies the data analyzed by the analysis means using the classification means. For example, the server classifies the weather data into categories such as "temperature," "precipitation probability," and "wind speed." A machine learning algorithm is 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 by category.

[1627] Step 5:

[1628] The server consolidates the data

[1629] The server integrates the categorized data using an integration method. For example, it uses a database management system to compile the data from each category into a single information set. The input data is the categorized data, and the output is the 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 the integrated data. A generative AI model is used for this generation. The input data is the 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 the providing means. The user can check the latest weather information for 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. In concrete terms, 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, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal."

[1638] Conventional information provision systems have difficulty providing users with the information they search for 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. This means that the information users need cannot be provided in a timely manner, resulting in a decline in the accuracy and reliability of the information.

[1639] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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 analyzing means including a generating AI for analyzing the received Earth observation data, a classifying means for classifying the data analyzed by the analyzing means, an integrating means for integrating the classified data, a generating means for generating real-time information based on the integrated data, a providing means for providing the generated information according to a search word, a generating AI model for generating appropriate information based on a user's search query, and a prompt input means for inputting a prompt sentence into the generating AI model to generate information. This makes it possible to provide information searched by a user in real time, thereby improving the accuracy and reliability of the information.

[1641] "Earth observation data" is data collected to observe various phenomena and environments on Earth.

[1642] "Receiving means" refers to a device or system for receiving data from the outside.

[1643] The "analysis means" is a device or system for analyzing received data.

[1644] "Generative AI" is a system for generating data using artificial intelligence technology.

[1645] A "classification means" is a device or system for classifying analyzed data into specific categories.

[1646] An "integration means" is a device or system for consolidating classified data into one.

[1647] A "generation means" is a device or system for generating new information based on the integrated data.

[1648] "Providing means" refers to a device or system for providing the generated information to the user.

[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 a prompt sentence into a generative AI model to generate information.

[1651] A system for carrying out this invention has the following configuration: A server includes receiving means for receiving Earth observation data, analyzing means including a generation AI for analyzing the received Earth observation data, classification means for classifying 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 in accordance with search words, a generation AI model for generating appropriate information based on a user's search query, and prompt input means for inputting a prompt sentence into the generation AI model to generate information.

[1652] Hardware and software used

[1653] Hardware: Server, user device (smartphone, 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 calculation

[1656] 1. Data collection: The server collects real-time data from a cloud database, for example, from a weather API or a news API.

[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, into weather information, news, traffic information, etc.

[1659] 4. Data Integration: The server integrates the classified data and generates appropriate information according to the user's query.

[1660] 5. Information provision: The server generates real-time information based on the integrated data and provides it to the user's terminal.

[1661] Specific examples

[1662] For example, if a user searches for "weather in Tokyo" on their smartphone, the system will operate as follows:

[1663] 1. Data collection: The server retrieves the latest weather data for Tokyo from the 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 generative AI model (GPT-4).

[1666] 4. Data integration: The server integrates the categorized weather information and generates appropriate information according to the user's search query.

[1667] 5. Information provision: The server provides the latest weather information for Tokyo to the user's device based on the integrated data.

[1668] Prompt Sentence Examples

[1669] User search query: "Tokyo weather"

[1670] Prompt for generative AI model: "What is the latest weather information for Tokyo?"

[1671] When this prompt sentence is input into the generative AI model, the model will generate the latest weather information for Tokyo from the analyzed, classified, and integrated data and provide it to the user.

[1672] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[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 of 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 to extract the necessary information. Specifically, it cleans and preprocesses the data and converts it into an analyzable format. The input of 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 it into weather information, news, traffic information, etc. The input of this step is the data analyzed in step 2, and the output is the classified data.

[1679] Step 4:

[1680] The server integrates the categorized data and generates appropriate information according to the user's search query. Specifically, it aggregates related data and generates information corresponding to the user's search query. The input of this step is the categorized data from 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's device. Specifically, it inputs a prompt into the generative AI model and provides the generated information to the user. The input of this step is the information integrated in step 4 and the prompt, and the output is the final information provided to the user.

[1683] Specific examples

[1684] User search query: "Tokyo weather"

[1685] Prompt for generative AI model: "What is the latest weather information for Tokyo?"

[1686] When this prompt sentence is input into the generative AI model, the model will generate the latest weather information for Tokyo from the analyzed, classified, and integrated data and provide it to the user.

[1687] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1688] "Example 1"

[1689] In one embodiment of the present invention, the system includes a receiving means for receiving Earth observation data, an analyzing means including a generating AI for analyzing the received Earth observation data, a classifying means for cl...

Claims

1. A receiving means for receiving earth observation data transmitted from an earth observation satellite; An analysis means including a generation AI for analyzing the received Earth observation data; A classification means for classifying the data analyzed by the analysis means into a predetermined category; an integration means for integrating the classified data; a generating means for generating real-time information based on the integrated data; The system includes a providing means for providing the generated information in response to the search word.

2. The system of claim 1 , wherein said generating means generates information relating to current events occurring on Earth.

3. An emotion recognition means for recognizing an emotion of a user who inputs the search word; an adjustment means for adjusting the generated information based on the emotion recognized by the emotion recognition means; The system of claim 1 further comprising:

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