system

Abstract

The system according to this embodiment aims to predict the location of mineral deposits with high accuracy and to conduct efficient exploration of mineral resources. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a supply unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit and predicts the location of the ore deposit. The generation unit generates a report based on the analysis results obtained by the analysis unit. The supply unit provides the report generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to predict the ore deposit location and efficient exploration of mineral resources was not carried out.

[0005] The system according to the embodiment aims to accurately predict the ore deposit location and perform efficient exploration of mineral resources.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit and predicts the ore deposit location. The generation unit reports the analysis result obtained by the analysis unit. The provision unit provides the report generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict the location of ore deposits with high accuracy and efficiently explore mineral resources. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The mineral resource exploration AI platform according to an embodiment of the present invention is a system that efficiently explores undeveloped mineral resources lying dormant in Japan and surrounding sea areas by utilizing the latest AI technology. This mineral resource exploration AI platform integrates geological data, satellite imagery, and oceanographic data from the Geospatial Information Authority of Japan, and the geological analysis AI predicts the location of mineral deposits with high accuracy. The generation AI reports the analysis results and provides a UI that allows users without specialized knowledge to utilize the expert analysis results. Furthermore, drones and AI-controlled robots are used during mining to reduce costs and environmental impact. For example, the data collection module collects geological data from the Geospatial Information Authority of Japan, ALOS satellite data from JAXA, Landsat data from NASA, and seabed topography data from the Japan Coast Guard. Next, the geological analysis AI uses GCNN (Geospatial Convolutional Neural Network) to recognize geological patterns with high accuracy and predict the location of mineral deposits. The satellite image analysis AI uses CNN to detect mineral distribution. The generation AI automatically generates expert-level reports from these analysis results. The user interface is provided via a web browser or smartphone app, visually displaying resource distribution using GIS maps. It also features a function to generate and display reports based on AI analysis results. During mining, drones and AI-controlled robots are utilized to reduce costs and environmental impact. This enables reduced exploration costs, increased efficiency, and the supplementation of expertise, thereby reducing reliance on domestic and international imports, building an economic foundation resilient to resource price fluctuations, and revitalizing local industries and creating new jobs. As a result, the mineral resource exploration AI platform can efficiently collect, analyze, generate, and provide data.

[0029] The mineral resource exploration AI platform according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The collection unit collects, for example, geological data, satellite image data, and ocean data from the Geospatial Information Authority of Japan. The collection unit can collect, for example, ALOS satellite data from JAXA. The collection unit can also collect Landsat data from NASA. Furthermore, the collection unit can also collect seabed topography data from the Japan Coast Guard. The analysis unit analyzes the data collected by the collection unit and predicts the location of mineral deposits. The analysis unit can, for example, use a GCNN (Geospatial Convolutional Neural Network) to recognize geological patterns and predict the location of mineral deposits. The analysis unit can also, for example, use a CNN to detect mineral distribution. The generation unit reports the analysis results obtained by the analysis unit. The generation unit automatically generates expert-level reports from the analysis results using, for example, a generation AI. The generation unit, for example, receives a prompt from the generation AI saying "Please summarize these analysis results in a report" and generates a report from the analysis results. The provisioning unit provides reports generated by the generation unit. The provisioning unit provides reports, for example, via a web browser or smartphone application. The provisioning unit can also visually display resource distribution using, for example, a GIS map. The provisioning unit has a function to generate and display reports based on AI analysis results. The provisioning unit can utilize drones or AI-controlled robots during mining to reduce costs and environmental impact. As a result, the mineral resource exploration AI platform according to this embodiment can efficiently collect, analyze, generate, and provide data.

[0030] The data collection unit collects data. For example, it collects geological data, satellite image data, and oceanographic data from the Geospatial Information Authority of Japan (GSI). Specifically, the GSI's geological data includes geological maps, geological cross-sections, and geological survey reports; this data is crucial for understanding geological structure and mineral distribution. Satellite image data covers a wide area of ​​the Earth's surface, allowing observation of changes in topography and vegetation. For example, JAXA's ALOS satellite data provides high-resolution topographic data, useful for detailed analysis of geological structure. NASA's Landsat data allows observation of long-term surface changes and is an important source of information in mineral resource exploration. Furthermore, the Japan Coast Guard's seabed topographic data is used to understand seabed topography and geological structure, and is indispensable in marine mineral resource exploration. The data collection unit collects data from these diverse data sources and manages it centrally to build a comprehensive database for mineral resource exploration. The collected data is updated in real time and stored on a cloud server for access by the analysis and generation units. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit and predicts the location of ore deposits. For example, the analysis unit uses a Geospatial Convolutional Neural Network (GCNN) to recognize geological patterns and predict the location of ore deposits. GCNN is a convolutional neural network that takes into account the characteristics of geospatial data and is excellent at recognizing geological patterns. Specifically, GCNN takes geological data and satellite image data as input, analyzes the geological structure and mineral distribution, and identifies areas where ore deposits are likely to exist. The analysis unit can also detect mineral distribution using a Convolutional Neural Network (CNN). CNNs are excellent at extracting features from image data and can detect mineral distribution from satellite image data with high accuracy. Furthermore, the analysis unit can use AI to utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical ore deposit data, it can predict the probability of ore deposit occurrence in specific areas and geological conditions and formulate future exploration plans. This allows the analysis unit to quickly and accurately analyze the collected data and predict the location of ore deposits with high accuracy.

[0032] The generation unit reports the analysis results obtained by the analysis unit. For example, the generation unit automatically generates expert-level reports from the analysis results using generation AI. The generation AI uses natural language processing technology to organize the analysis results in an easy-to-understand format, creating reports that are accessible even to users without specialized knowledge. Specifically, the generation AI receives a prompt such as "Please summarize these analysis results in a report," and based on the analysis results, generates a detailed report including the location of the mineral deposit, prediction accuracy, and related geological information. The generated report uses charts and graphs to visually convey information, making it easy for users to understand intuitively. Furthermore, the generation unit can collect user feedback and continuously improve the content and format of the reports. This allows the generation unit to quickly and accurately report analysis results and provide them to users.

[0033] The service provider provides reports generated by the generation unit. The service provider delivers these reports, for example, through a web browser or smartphone app. Specifically, users can access the generated reports using a web browser or smartphone app to check the location of mineral deposits, prediction accuracy, and related geological information. The service provider can also visually display resource distribution using GIS maps, for example. GIS maps are tools that visually display geospatial data, allowing for an intuitive understanding of mineral deposit locations and surrounding geological information. Furthermore, the service provider has the functionality to generate and display reports based on AI analysis results. This allows users to check analysis results updated in real time and make decisions based on the latest information. The service provider can, for example, utilize drones and AI-controlled robots during mining to reduce costs and environmental impact. Drones can accurately locate mineral deposits and perform mining operations efficiently. AI-controlled robots automate mining operations, improving work efficiency and ensuring worker safety. This allows the service provider to provide users with rapid and accurate information, improving the efficiency of mineral resource exploration.

[0034] The data collection unit can collect geological data, satellite image data, and oceanographic data from the Geospatial Information Authority of Japan. For example, the data collection unit can collect geological data from the Geospatial Information Authority of Japan. For example, the data collection unit can collect geological maps and geological survey data. For example, the data collection unit can collect satellite image data. For example, the data collection unit can collect multiple satellite image data with different resolutions and capture dates. For example, the data collection unit can collect oceanographic data. For example, the data collection unit can collect oceanographic data such as water temperature, salinity, and ocean current data. By collecting diverse data, the accuracy of the analysis is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geological data from the Geospatial Information Authority of Japan into AI, and the AI ​​can perform data collection.

[0035] The analysis unit can recognize geological patterns and predict the location of ore deposits using GCNN. For example, the analysis unit uses GCNN to recognize geological patterns with high accuracy. The analysis unit can predict the location of ore deposits using GCNN. For example, the analysis unit uses GCNN to recognize geological patterns and predict the location of ore deposits using training data. This improves the accuracy of geological pattern recognition by using GCNN. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input GCNN into AI, which can then perform geological pattern recognition and ore deposit location prediction.

[0036] The generation unit can automatically generate expert-level reports from analysis results. For example, the generation unit uses a generation AI to generate reports from analysis results. For example, the generation unit receives a prompt from the generation AI saying, "Please summarize these analysis results in a report," and generates a report from the analysis results. For example, the generation unit has the generation AI automatically generate an expert-level report based on the analysis results. This allows users to utilize the analysis results even without specialized knowledge by automatically generating expert-level reports. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input analysis results into a generation AI, and the generation AI can perform report generation.

[0037] The service provider can provide reports via web browsers and smartphone apps. For example, the service provider can provide reports via web browsers. For example, the service provider can provide reports via web browsers such as Chrome, Firefox, and Safari. For example, the service provider can provide reports via smartphone apps. For example, the service provider can provide reports via iOS apps and Android apps. This allows users to view analysis results anywhere by providing reports via web browsers and smartphone apps. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the report into AI, and the AI ​​can provide the report via web browsers and smartphone apps.

[0038] The service provider can visually display resource distribution using a GIS map. The service provider can, for example, visually display resource distribution using a GIS map. The service provider can, for example, display resource distribution using a Geographic Information System (GIS). The service provider can, for example, display resource distribution on a map so that users can understand it intuitively. This allows users to intuitively understand the location of resources by visually displaying resource distribution using a GIS map. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input resource distribution data into AI, and the AI ​​can display resource distribution using a GIS map.

[0039] The service provider may have a function to generate and display reports based on AI analysis results. For example, the service provider may use AI to generate reports based on analysis results. For example, the service provider may have AI generate and display reports based on analysis results. For example, the service provider may have AI summarize the analysis results in a report and display it to the user. This allows users to easily check the analysis results by providing a function to generate and display reports based on AI analysis results. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input analysis results into AI, and the AI ​​may perform report generation and display.

[0040] The supply unit can reduce costs and environmental impact by utilizing drones and AI-controlled robots during mining. For example, the supply unit can perform mining using drones. For example, the supply unit can perform mining using AI-controlled robots. For example, the supply unit can reduce mining costs by using drones and AI-controlled robots. For example, the supply unit can reduce environmental impact by using drones and AI-controlled robots. As a result, by utilizing drones and AI-controlled robots, both mining costs and environmental impact can be reduced. Some or all of the above processes in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input drones and AI-controlled robots into AI, and the AI ​​can perform the mining control.

[0041] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method from past data collection history. For example, the data collection unit can analyze past data collection history and optimize the timing of data collection. For example, the data collection unit can determine the priority of data collection based on past data collection history. This allows the optimal data collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, and the AI ​​can perform the selection of the optimal data collection method.

[0042] The data collection unit can perform filtering based on specific geological and environmental conditions during data collection. For example, the data collection unit can limit the target of data collection based on specific geological conditions. For example, the data collection unit can filter the target of data collection based on environmental conditions. For example, the data collection unit can optimize the target of data collection by combining geological and environmental conditions. This improves the accuracy of data collection by filtering based on specific geological and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input specific geological and environmental conditions into the AI, and the AI ​​can perform the filtering of data collection.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on geographical location information. For example, the data collection unit can optimize the target of data collection by considering geographical location information. For example, the data collection unit can determine the priority of data collection based on geographical location information. This improves the efficiency of data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, and the AI ​​can perform the determination of data collection priorities.

[0044] The data collection unit can integrate and collect information from social media and public databases during data collection. For example, the data collection unit can integrate information from social media to collect data. For example, the data collection unit can integrate information from public databases to collect data. For example, the data collection unit can combine information from social media and public databases to collect data. This improves data diversity by integrating and collecting information from social media and public databases. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information from social media and public databases into AI, and the AI ​​can perform data integration and collection.

[0045] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis. For example, the analysis unit can optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit can adjust the parameters of the analysis algorithm based on past analysis results. For example, the analysis unit can improve the accuracy of the analysis algorithm by analyzing past analysis results. As a result, the accuracy of the analysis is improved by optimizing the analysis algorithm by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis results into AI, and the AI ​​can perform the optimization of the analysis algorithm.

[0046] The analysis unit can apply analytical methods that focus on specific minerals or resources during analysis. For example, the analysis unit can apply analytical methods that focus on specific minerals. For example, the analysis unit can apply analytical methods that focus on specific resources. The analysis unit can optimize the analytical methods according to the type of mineral or resource. This improves the accuracy of the analysis by applying analytical methods that focus on specific minerals or resources. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data about specific minerals or resources into the AI, and the AI ​​can perform the application of analytical methods.

[0047] The analysis unit can improve the accuracy of the analysis by considering geographical data during the analysis. For example, the analysis unit can improve the accuracy of the analysis by considering geographical data. For example, the analysis unit can optimize the analysis algorithm based on geographical data. For example, the analysis unit can improve the reliability of the analysis results by referring to geographical data. In this way, by improving the accuracy of the analysis by considering geographical data, reliable results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input geographical data into AI, and the AI ​​can perform the improvement of the accuracy of the analysis.

[0048] The analysis unit can improve the accuracy of its analysis by referring to relevant scientific literature and research data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant scientific literature. For example, the analysis unit can optimize its analysis algorithm based on research data. For example, the analysis unit can improve the reliability of its analysis results by combining scientific literature and research data. As a result, the accuracy of the analysis is improved by referring to relevant scientific literature and research data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input scientific literature and research data into AI, which can then perform the improvement of the analysis accuracy.

[0049] The generation unit can adjust the level of detail in the report based on the importance of the analysis results when generating the report. For example, the generation unit can adjust the level of detail in the report based on the importance of the analysis results. For example, the generation unit can generate a report that highlights important analysis results. For example, the generation unit can optimize the content of the report according to the importance of the analysis results. This allows important information to be highlighted by adjusting the level of detail in the report based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the analysis results into the generation AI, and the generation AI can perform the adjustment of the level of detail in the report.

[0050] The generation unit can generate customized reports for specific user segments when generating reports. For example, the generation unit can generate customized reports for specific user segments. For example, the generation unit can optimize the content of the report according to the user's attributes. For example, the generation unit can provide customized reports tailored to the user's needs. This makes it possible to provide information that meets the user's needs by generating customized reports for specific user segments. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user attribute data into a generation AI, and the generation AI can execute the generation of a customized report.

[0051] The generation unit can determine the priority of reports based on the submission timing of the analysis results when generating reports. For example, the generation unit can determine the priority of reports based on the submission timing of the analysis results. For example, the generation unit can prioritize the creation of reports for analysis results with approaching submission deadlines. For example, the generation unit can optimize the content of reports according to the submission timing. This enables efficient report generation according to submission deadlines by determining the priority of reports based on the submission timing of the analysis results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input submission timing data into a generation AI, and the generation AI can perform the determination of report priorities.

[0052] The generation unit can adjust the order of reports based on the relevance of the analysis results when generating reports. For example, the generation unit adjusts the order of reports based on the relevance of the analysis results. For example, the generation unit can prioritize reporting highly relevant analysis results. For example, the generation unit can optimize the content of reports according to the relevance of the analysis results. This allows for the priority provision of highly relevant information by adjusting the order of reports based on the relevance of the analysis results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input relevance data of the analysis results into a generation AI, and the generation AI can perform the adjustment of the report order.

[0053] The service provider can select the optimal display method by referring to the user's past operation history when providing reports. For example, the service provider can select the optimal display method by referring to the user's past operation history. For example, the service provider can adjust the display method parameters based on past operation history. For example, the service provider can propose the optimal display method by analyzing the user's operation history. In this way, by selecting the optimal display method by referring to the user's past operation history, a user-friendly display method can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input user operation history data into AI, and the AI ​​can perform the selection of the optimal display method.

[0054] The service provider can apply display methods optimized for specific devices or platforms when providing reports. For example, the service provider can provide a display method optimized for smartphones. For example, the service provider can provide a display method optimized for tablets. For example, the service provider can provide a display method optimized for desktops. By applying a display method optimized for specific devices or platforms, users can comfortably view reports on any device. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input device and platform information into AI, and the AI ​​can perform the application of the optimized display method.

[0055] The service provider can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. For example, if the user is using a desktop, the service provider can provide a display method optimized for a larger screen. By selecting the optimal display method considering the user's device information, the service provider can ensure that users can comfortably view reports on any device. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI, which can then select the optimal display method.

[0056] The service provider can collect user feedback when providing reports and continuously improve the service delivery method. For example, the service provider can collect user feedback and improve the service delivery method. For example, the service provider can adjust display parameters based on the feedback. For example, the service provider can optimize the service delivery method by reflecting user opinions. This allows for the realization of the optimal service delivery method that meets user needs by collecting user feedback and continuously improving the service delivery method. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user feedback data into AI, and the AI ​​can perform improvements to the service delivery method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can monitor environmental data in real time during data collection and interrupt data collection if an anomaly is detected. For example, the data collection unit can interrupt data collection in the event of a natural disaster such as an earthquake or tsunami to ensure safety. The data collection unit can also interrupt data collection in the event of equipment failure or communication failure to maintain data reliability. The data collection unit can resume data collection after the anomaly has been resolved, maintaining data consistency. This enables safe and reliable data collection by monitoring environmental data in real time.

[0059] The service provider can select the optimal display method when providing reports by referring to the user's past browsing history. For example, the service provider can provide the optimal display method based on the display method the user has preferred to use in the past. The service provider can analyze past browsing history and propose a display method that allows the user to obtain information most efficiently. The service provider can adjust the display method parameters based on the user's browsing history to ensure that the user can comfortably view the report. In this way, by selecting the optimal display method by referring to the user's past browsing history, a user-friendly display method can be provided.

[0060] The data collection unit can adjust the timing of data collection based on specific seasons and weather conditions. For example, the unit can suspend data collection in winter to avoid the effects of snow and ice. The unit can also suspend data collection to ensure safety in the event of severe weather conditions such as typhoons or heavy rain. The unit can resume data collection after weather conditions have stabilized, maintaining data consistency. By adjusting the timing of data collection based on specific seasons and weather conditions, safe and reliable data collection becomes possible.

[0061] The service provider can select the optimal display method when providing reports, taking into account the battery level of the user's device. For example, if the battery level is low, the service provider can provide an energy-efficient display method. If the battery level is sufficient, the service provider can provide a display method that includes detailed information. The service provider adjusts the display method parameters according to the battery level to ensure that the user can comfortably view the report. In this way, by selecting the optimal display method considering the battery level of the user's device, the user can comfortably view the report in any situation.

[0062] The data collection unit can adjust its data collection methods to take into account the culture and social background of a specific region. For example, the unit can suspend data collection during local cultural events or festivals. The unit can limit the scope of data collection based on social context. The unit can optimize data collection methods according to regional characteristics and maintain data reliability. This enables region-sensitive data collection by adjusting data collection methods to take into account the culture and social background of a specific region.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects data. The data collection unit collects, for example, geological data, satellite image data, and ocean data from the Geospatial Information Authority of Japan. The data collection unit can also collect, for example, ALOS satellite data from JAXA. In addition, the data collection unit can collect Landsat data from NASA. Furthermore, the data collection unit can also collect seabed topography data from the Japan Coast Guard. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the location of the ore deposit. The analysis unit can, for example, use a GCNN (Geospatial Convolutional Neural Network) to recognize geological patterns and predict the location of the ore deposit. The analysis unit can also, for example, use a CNN to detect mineral distribution. Step 3: The generation unit reports the analysis results obtained by the analysis unit. The generation unit automatically generates expert-level reports of the analysis results, for example, using a generation AI. The generation unit, for example, receives a prompt from the generation AI saying, "Please compile these analysis results into a report," and then generates a report of the analysis results. Step 4: The provisioning unit provides the report generated by the generation unit. The provisioning unit provides the report, for example, via a web browser or smartphone app. The provisioning unit can also visually display resource distribution using, for example, a GIS map. The provisioning unit has the function to generate and display reports based on AI analysis results, for example. The provisioning unit can utilize drones and AI-controlled robots during mining to reduce costs and environmental impact, for example.

[0065] (Example of form 2) The mineral resource exploration AI platform according to an embodiment of the present invention is a system that efficiently explores undeveloped mineral resources lying dormant in Japan and surrounding sea areas by utilizing the latest AI technology. This mineral resource exploration AI platform integrates geological data, satellite imagery, and oceanographic data from the Geospatial Information Authority of Japan, and the geological analysis AI predicts the location of mineral deposits with high accuracy. The generation AI reports the analysis results and provides a UI that allows users without specialized knowledge to utilize the expert analysis results. Furthermore, drones and AI-controlled robots are used during mining to reduce costs and environmental impact. For example, the data collection module collects geological data from the Geospatial Information Authority of Japan, ALOS satellite data from JAXA, Landsat data from NASA, and seabed topography data from the Japan Coast Guard. Next, the geological analysis AI uses GCNN (Geospatial Convolutional Neural Network) to recognize geological patterns with high accuracy and predict the location of mineral deposits. The satellite image analysis AI uses CNN to detect mineral distribution. The generation AI automatically generates expert-level reports from these analysis results. The user interface is provided via a web browser or smartphone app, visually displaying resource distribution using GIS maps. It also features a function to generate and display reports based on AI analysis results. During mining, drones and AI-controlled robots are utilized to reduce costs and environmental impact. This enables reduced exploration costs, increased efficiency, and the supplementation of expertise, thereby reducing reliance on domestic and international imports, building an economic foundation resilient to resource price fluctuations, and revitalizing local industries and creating new jobs. As a result, the mineral resource exploration AI platform can efficiently collect, analyze, generate, and provide data.

[0066] The mineral resource exploration AI platform according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The collection unit collects, for example, geological data, satellite image data, and ocean data from the Geospatial Information Authority of Japan. The collection unit can collect, for example, ALOS satellite data from JAXA. The collection unit can also collect Landsat data from NASA. Furthermore, the collection unit can also collect seabed topography data from the Japan Coast Guard. The analysis unit analyzes the data collected by the collection unit and predicts the location of mineral deposits. The analysis unit can, for example, use a GCNN (Geospatial Convolutional Neural Network) to recognize geological patterns and predict the location of mineral deposits. The analysis unit can also, for example, use a CNN to detect mineral distribution. The generation unit reports the analysis results obtained by the analysis unit. The generation unit automatically generates expert-level reports from the analysis results using, for example, a generation AI. The generation unit, for example, receives a prompt from the generation AI saying "Please summarize these analysis results in a report" and generates a report from the analysis results. The provisioning unit provides reports generated by the generation unit. The provisioning unit provides reports, for example, via a web browser or smartphone application. The provisioning unit can also visually display resource distribution using, for example, a GIS map. The provisioning unit has a function to generate and display reports based on AI analysis results. The provisioning unit can utilize drones or AI-controlled robots during mining to reduce costs and environmental impact. As a result, the mineral resource exploration AI platform according to this embodiment can efficiently collect, analyze, generate, and provide data.

[0067] The data collection unit collects data. For example, it collects geological data, satellite image data, and oceanographic data from the Geospatial Information Authority of Japan (GSI). Specifically, the GSI's geological data includes geological maps, geological cross-sections, and geological survey reports; this data is crucial for understanding geological structure and mineral distribution. Satellite image data covers a wide area of ​​the Earth's surface, allowing observation of changes in topography and vegetation. For example, JAXA's ALOS satellite data provides high-resolution topographic data, useful for detailed analysis of geological structure. NASA's Landsat data allows observation of long-term surface changes and is an important source of information in mineral resource exploration. Furthermore, the Japan Coast Guard's seabed topographic data is used to understand seabed topography and geological structure, and is indispensable in marine mineral resource exploration. The data collection unit collects data from these diverse data sources and manages it centrally to build a comprehensive database for mineral resource exploration. The collected data is updated in real time and stored on a cloud server for access by the analysis and generation units. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0068] The analysis unit analyzes the data collected by the collection unit and predicts the location of ore deposits. For example, the analysis unit uses a Geospatial Convolutional Neural Network (GCNN) to recognize geological patterns and predict the location of ore deposits. GCNN is a convolutional neural network that takes into account the characteristics of geospatial data and is excellent at recognizing geological patterns. Specifically, GCNN takes geological data and satellite image data as input, analyzes the geological structure and mineral distribution, and identifies areas where ore deposits are likely to exist. The analysis unit can also detect mineral distribution using a Convolutional Neural Network (CNN). CNNs are excellent at extracting features from image data and can detect mineral distribution from satellite image data with high accuracy. Furthermore, the analysis unit can use AI to utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical ore deposit data, it can predict the probability of ore deposit occurrence in specific areas and geological conditions and formulate future exploration plans. This allows the analysis unit to quickly and accurately analyze the collected data and predict the location of ore deposits with high accuracy.

[0069] The generation unit reports the analysis results obtained by the analysis unit. For example, the generation unit automatically generates expert-level reports from the analysis results using generation AI. The generation AI uses natural language processing technology to organize the analysis results in an easy-to-understand format, creating reports that are accessible even to users without specialized knowledge. Specifically, the generation AI receives a prompt such as "Please summarize these analysis results in a report," and based on the analysis results, generates a detailed report including the location of the mineral deposit, prediction accuracy, and related geological information. The generated report uses charts and graphs to visually convey information, making it easy for users to understand intuitively. Furthermore, the generation unit can collect user feedback and continuously improve the content and format of the reports. This allows the generation unit to quickly and accurately report analysis results and provide them to users.

[0070] The service provider provides reports generated by the generation unit. The service provider delivers these reports, for example, through a web browser or smartphone app. Specifically, users can access the generated reports using a web browser or smartphone app to check the location of mineral deposits, prediction accuracy, and related geological information. The service provider can also visually display resource distribution using GIS maps, for example. GIS maps are tools that visually display geospatial data, allowing for an intuitive understanding of mineral deposit locations and surrounding geological information. Furthermore, the service provider has the functionality to generate and display reports based on AI analysis results. This allows users to check analysis results updated in real time and make decisions based on the latest information. The service provider can, for example, utilize drones and AI-controlled robots during mining to reduce costs and environmental impact. Drones can accurately locate mineral deposits and perform mining operations efficiently. AI-controlled robots automate mining operations, improving work efficiency and ensuring worker safety. This allows the service provider to provide users with rapid and accurate information, improving the efficiency of mineral resource exploration.

[0071] The data collection unit can collect geological data, satellite image data, and oceanographic data from the Geospatial Information Authority of Japan. For example, the data collection unit can collect geological data from the Geospatial Information Authority of Japan. For example, the data collection unit can collect geological maps and geological survey data. For example, the data collection unit can collect satellite image data. For example, the data collection unit can collect multiple satellite image data with different resolutions and capture dates. For example, the data collection unit can collect oceanographic data. For example, the data collection unit can collect oceanographic data such as water temperature, salinity, and ocean current data. By collecting diverse data, the accuracy of the analysis is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geological data from the Geospatial Information Authority of Japan into AI, and the AI ​​can perform data collection.

[0072] The analysis unit can recognize geological patterns and predict the location of ore deposits using GCNN. For example, the analysis unit uses GCNN to recognize geological patterns with high accuracy. The analysis unit can predict the location of ore deposits using GCNN. For example, the analysis unit uses GCNN to recognize geological patterns and predict the location of ore deposits using training data. This improves the accuracy of geological pattern recognition by using GCNN. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input GCNN into AI, which can then perform geological pattern recognition and ore deposit location prediction.

[0073] The generation unit can automatically generate expert-level reports from analysis results. For example, the generation unit uses a generation AI to generate reports from analysis results. For example, the generation unit receives a prompt from the generation AI saying, "Please summarize these analysis results in a report," and generates a report from the analysis results. For example, the generation unit has the generation AI automatically generate an expert-level report based on the analysis results. This allows users to utilize the analysis results even without specialized knowledge by automatically generating expert-level reports. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input analysis results into a generation AI, and the generation AI can perform report generation.

[0074] The service provider can provide reports via web browsers and smartphone apps. For example, the service provider can provide reports via web browsers. For example, the service provider can provide reports via web browsers such as Chrome, Firefox, and Safari. For example, the service provider can provide reports via smartphone apps. For example, the service provider can provide reports via iOS apps and Android apps. This allows users to view analysis results anywhere by providing reports via web browsers and smartphone apps. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the report into AI, and the AI ​​can provide the report via web browsers and smartphone apps.

[0075] The service provider can visually display resource distribution using a GIS map. The service provider can, for example, visually display resource distribution using a GIS map. The service provider can, for example, display resource distribution using a Geographic Information System (GIS). The service provider can, for example, display resource distribution on a map so that users can understand it intuitively. This allows users to intuitively understand the location of resources by visually displaying resource distribution using a GIS map. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input resource distribution data into AI, and the AI ​​can display resource distribution using a GIS map.

[0076] The service provider may have a function to generate and display reports based on AI analysis results. For example, the service provider may use AI to generate reports based on analysis results. For example, the service provider may have AI generate and display reports based on analysis results. For example, the service provider may have AI summarize the analysis results in a report and display it to the user. This allows users to easily check the analysis results by providing a function to generate and display reports based on AI analysis results. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input analysis results into AI, and the AI ​​may perform report generation and display.

[0077] The supply unit can reduce costs and environmental impact by utilizing drones and AI-controlled robots during mining. For example, the supply unit can perform mining using drones. For example, the supply unit can perform mining using AI-controlled robots. For example, the supply unit can reduce mining costs by using drones and AI-controlled robots. For example, the supply unit can reduce environmental impact by using drones and AI-controlled robots. As a result, by utilizing drones and AI-controlled robots, both mining costs and environmental impact can be reduced. Some or all of the above processes in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input drones and AI-controlled robots into AI, and the AI ​​can perform the mining control.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can quickly adjust the timing of data collection to collect data efficiently. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can perform emotion estimation and adjust the timing of data collection.

[0079] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method from past data collection history. For example, the data collection unit can analyze past data collection history and optimize the timing of data collection. For example, the data collection unit can determine the priority of data collection based on past data collection history. This allows the optimal data collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, and the AI ​​can perform the selection of the optimal data collection method.

[0080] The data collection unit can perform filtering based on specific geological and environmental conditions during data collection. For example, the data collection unit can limit the target of data collection based on specific geological conditions. For example, the data collection unit can filter the target of data collection based on environmental conditions. For example, the data collection unit can optimize the target of data collection by combining geological and environmental conditions. This improves the accuracy of data collection by filtering based on specific geological and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input specific geological and environmental conditions into the AI, and the AI ​​can perform the filtering of data collection.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This enables efficient data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can perform emotion estimation and data prioritization.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on geographical location information. For example, the data collection unit can optimize the target of data collection by considering geographical location information. For example, the data collection unit can determine the priority of data collection based on geographical location information. This improves the efficiency of data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, and the AI ​​can perform the determination of data collection priorities.

[0083] The data collection unit can integrate and collect information from social media and public databases during data collection. For example, the data collection unit can integrate information from social media to collect data. For example, the data collection unit can integrate information from public databases to collect data. For example, the data collection unit can combine information from social media and public databases to collect data. This improves data diversity by integrating and collecting information from social media and public databases. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information from social media and public databases into AI, and the AI ​​can perform data integration and collection.

[0084] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the accuracy of the analysis to provide reliable results. For example, if the user is relaxed, the analysis unit can adjust the accuracy of the analysis to provide detailed results. For example, if the user is in a hurry, the analysis unit can quickly adjust the accuracy of the analysis to provide results efficiently. In this way, reliable results can be provided by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can perform emotion estimation and adjust the accuracy of the analysis.

[0085] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis. For example, the analysis unit can optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit can adjust the parameters of the analysis algorithm based on past analysis results. For example, the analysis unit can improve the accuracy of the analysis algorithm by analyzing past analysis results. As a result, the accuracy of the analysis is improved by optimizing the analysis algorithm by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis results into AI, and the AI ​​can perform the optimization of the analysis algorithm.

[0086] The analysis unit can apply analytical methods that focus on specific minerals or resources during analysis. For example, the analysis unit can apply analytical methods that focus on specific minerals. For example, the analysis unit can apply analytical methods that focus on specific resources. The analysis unit can optimize the analytical methods according to the type of mineral or resource. This improves the accuracy of the analysis by applying analytical methods that focus on specific minerals or resources. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data about specific minerals or resources into the AI, and the AI ​​can perform the application of analytical methods.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, the results become easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI, which can then perform emotion estimation and adjust the display method.

[0088] The analysis unit can improve the accuracy of the analysis by considering geographical data during the analysis. For example, the analysis unit can improve the accuracy of the analysis by considering geographical data. For example, the analysis unit can optimize the analysis algorithm based on geographical data. For example, the analysis unit can improve the reliability of the analysis results by referring to geographical data. In this way, by improving the accuracy of the analysis by considering geographical data, reliable results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input geographical data into AI, and the AI ​​can perform the improvement of the accuracy of the analysis.

[0089] The analysis unit can improve the accuracy of its analysis by referring to relevant scientific literature and research data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant scientific literature. For example, the analysis unit can optimize its analysis algorithm based on research data. For example, the analysis unit can improve the reliability of its analysis results by combining scientific literature and research data. As a result, the accuracy of the analysis is improved by referring to relevant scientific literature and research data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input scientific literature and research data into AI, which can then perform the improvement of the analysis accuracy.

[0090] The generation unit can estimate the user's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a simple and easy-to-read report. For example, if the user is relaxed, the generation unit can generate a report with detailed information. For example, if the user is in a hurry, the generation unit can generate a report that gets straight to the point. By adjusting the presentation of the report according to the user's emotions, the report becomes easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI, which can then perform emotion estimation and adjust the presentation of the report.

[0091] The generation unit can adjust the level of detail in the report based on the importance of the analysis results when generating the report. For example, the generation unit can adjust the level of detail in the report based on the importance of the analysis results. For example, the generation unit can generate a report that highlights important analysis results. For example, the generation unit can optimize the content of the report according to the importance of the analysis results. This allows important information to be highlighted by adjusting the level of detail in the report based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the analysis results into the generation AI, and the generation AI can perform the adjustment of the level of detail in the report.

[0092] The generation unit can generate customized reports for specific user segments when generating reports. For example, the generation unit can generate customized reports for specific user segments. For example, the generation unit can optimize the content of the report according to the user's attributes. For example, the generation unit can provide customized reports tailored to the user's needs. This makes it possible to provide information that meets the user's needs by generating customized reports for specific user segments. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user attribute data into a generation AI, and the generation AI can execute the generation of a customized report.

[0093] The generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a short, concise report. If the user is relaxed, for example, the generation unit can generate a longer report with detailed explanations. If the user is in a hurry, for example, the generation unit can generate a short report that can be read quickly. By adjusting the length of the report according to the user's emotions, the report becomes easier for the user to read. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, for example, or not using a generative AI. For example, the generation unit can input user emotion data into a generative AI, which can perform emotion estimation and adjust the length of the report.

[0094] The generation unit can determine the priority of reports based on the submission timing of the analysis results when generating reports. For example, the generation unit can determine the priority of reports based on the submission timing of the analysis results. For example, the generation unit can prioritize the creation of reports for analysis results with approaching submission deadlines. For example, the generation unit can optimize the content of reports according to the submission timing. This enables efficient report generation according to submission deadlines by determining the priority of reports based on the submission timing of the analysis results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input submission timing data into a generation AI, and the generation AI can perform the determination of report priorities.

[0095] The generation unit can adjust the order of reports based on the relevance of the analysis results when generating reports. For example, the generation unit adjusts the order of reports based on the relevance of the analysis results. For example, the generation unit can prioritize reporting highly relevant analysis results. For example, the generation unit can optimize the content of reports according to the relevance of the analysis results. This allows for the priority provision of highly relevant information by adjusting the order of reports based on the relevance of the analysis results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input relevance data of the analysis results into a generation AI, and the generation AI can perform the adjustment of the report order.

[0096] The service provider can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and easy-to-read display. For example, if the user is relaxed, the service provider can provide a display that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display that gets straight to the point. By adjusting how the report is displayed according to the user's emotions, the report becomes easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then perform emotion estimation and adjust the display method.

[0097] The service provider can select the optimal display method by referring to the user's past operation history when providing reports. For example, the service provider can select the optimal display method by referring to the user's past operation history. For example, the service provider can adjust the display method parameters based on past operation history. For example, the service provider can propose the optimal display method by analyzing the user's operation history. In this way, by selecting the optimal display method by referring to the user's past operation history, a user-friendly display method can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input user operation history data into AI, and the AI ​​can perform the selection of the optimal display method.

[0098] The service provider can apply display methods optimized for specific devices or platforms when providing reports. For example, the service provider can provide a display method optimized for smartphones. For example, the service provider can provide a display method optimized for tablets. For example, the service provider can provide a display method optimized for desktops. By applying a display method optimized for specific devices or platforms, users can comfortably view reports on any device. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input device and platform information into AI, and the AI ​​can perform the application of the optimized display method.

[0099] The service provider can estimate the user's emotions and adjust the report's operation procedures based on the estimated emotions. For example, if the user is stressed, the service provider can simplify the operation procedures. For example, if the user is relaxed, the service provider can provide detailed operation procedures. For example, if the user is in a hurry, the service provider can provide procedures that allow for quick operation. This makes it easier for the user to operate the report by adjusting the operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can perform emotion estimation and adjustment of operation procedures.

[0100] The service provider can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. For example, if the user is using a desktop, the service provider can provide a display method optimized for a larger screen. By selecting the optimal display method considering the user's device information, the service provider can ensure that users can comfortably view reports on any device. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI, which can then select the optimal display method.

[0101] The service provider can collect user feedback when providing reports and continuously improve the service delivery method. For example, the service provider can collect user feedback and improve the service delivery method. For example, the service provider can adjust display parameters based on the feedback. For example, the service provider can optimize the service delivery method by reflecting user opinions. This allows for the realization of the optimal service delivery method that meets user needs by collecting user feedback and continuously improving the service delivery method. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user feedback data into AI, and the AI ​​can perform improvements to the service delivery method.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing high-priority data and provide results quickly. If the user is relaxed, the analysis unit will prioritize analyzing detailed data and provide highly accurate results. If the user is in a hurry, the analysis unit will prioritize processing data that can be analyzed quickly and provide results efficiently. In this way, by determining the priority of analysis according to the user's emotions, analysis results that meet the user's needs can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0104] The data collection unit can monitor environmental data in real time during data collection and interrupt data collection if an anomaly is detected. For example, the data collection unit can interrupt data collection in the event of a natural disaster such as an earthquake or tsunami to ensure safety. The data collection unit can also interrupt data collection in the event of equipment failure or communication failure to maintain data reliability. The data collection unit can resume data collection after the anomaly has been resolved, maintaining data consistency. This enables safe and reliable data collection by monitoring environmental data in real time.

[0105] The generation unit can estimate the user's emotions and adjust the report format based on those emotions. For example, if the user is stressed, the generation unit can generate a report in a simple, easy-to-read format. If the user is relaxed, the generation unit can generate a report in a format that includes detailed information. If the user is in a hurry, the generation unit can generate a report in a format that gets straight to the point. By adjusting the report format according to the user's emotions, the report becomes easier for the user to understand. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0106] The service provider can select the optimal display method when providing reports by referring to the user's past browsing history. For example, the service provider can provide the optimal display method based on the display method the user has preferred to use in the past. The service provider can analyze past browsing history and propose a display method that allows the user to obtain information most efficiently. The service provider can adjust the display method parameters based on the user's browsing history to ensure that the user can comfortably view the report. In this way, by selecting the optimal display method by referring to the user's past browsing history, a user-friendly display method can be provided.

[0107] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible notification method. If the user is relaxed, the analysis unit can provide a notification method that includes detailed information. If the user is in a hurry, the analysis unit can provide a notification method that gets straight to the point. By adjusting the notification method of the analysis results according to the user's emotions, the results become easier for the user to understand. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0108] The data collection unit can adjust the timing of data collection based on specific seasons and weather conditions. For example, the unit can suspend data collection in winter to avoid the effects of snow and ice. The unit can also suspend data collection to ensure safety in the event of severe weather conditions such as typhoons or heavy rain. The unit can resume data collection after weather conditions have stabilized, maintaining data consistency. By adjusting the timing of data collection based on specific seasons and weather conditions, safe and reliable data collection becomes possible.

[0109] The generation unit can estimate the user's emotions and adjust the report content based on those emotions. For example, if the user is stressed, the generation unit can generate a report that highlights key points. If the user is relaxed, the generation unit can generate a report that includes detailed analysis results. If the user is in a hurry, the generation unit can generate a summary report that can be quickly understood. This allows users to effectively utilize the report by adjusting its content according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0110] The service provider can select the optimal display method when providing reports, taking into account the battery level of the user's device. For example, if the battery level is low, the service provider can provide an energy-efficient display method. If the battery level is sufficient, the service provider can provide a display method that includes detailed information. The service provider adjusts the display method parameters according to the battery level to ensure that the user can comfortably view the report. In this way, by selecting the optimal display method considering the battery level of the user's device, the user can comfortably view the report in any situation.

[0111] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand feedback method. If the user is relaxed, the analysis unit can provide a feedback method that includes detailed information. If the user is in a hurry, the analysis unit can provide a feedback method that gets straight to the point. By adjusting the feedback method of the analysis results according to the user's emotions, it makes it easier for the user to understand the results. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0112] The data collection unit can adjust its data collection methods to take into account the culture and social background of a specific region. For example, the unit can suspend data collection during local cultural events or festivals. The unit can limit the scope of data collection based on social context. The unit can optimize data collection methods according to regional characteristics and maintain data reliability. This enables region-sensitive data collection by adjusting data collection methods to take into account the culture and social background of a specific region.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects data. The data collection unit collects, for example, geological data, satellite image data, and ocean data from the Geospatial Information Authority of Japan. The data collection unit can also collect, for example, ALOS satellite data from JAXA. In addition, the data collection unit can collect Landsat data from NASA. Furthermore, the data collection unit can also collect seabed topography data from the Japan Coast Guard. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the location of the ore deposit. The analysis unit can, for example, use a GCNN (Geospatial Convolutional Neural Network) to recognize geological patterns and predict the location of the ore deposit. The analysis unit can also, for example, use a CNN to detect mineral distribution. Step 3: The generation unit reports the analysis results obtained by the analysis unit. The generation unit automatically generates expert-level reports of the analysis results, for example, using a generation AI. The generation unit, for example, receives a prompt from the generation AI saying, "Please compile these analysis results into a report," and then generates a report of the analysis results. Step 4: The provisioning unit provides the report generated by the generation unit. The provisioning unit provides the report, for example, via a web browser or smartphone app. The provisioning unit can also visually display resource distribution using, for example, a GIS map. The provisioning unit has the function to generate and display reports based on AI analysis results, for example. The provisioning unit can utilize drones and AI-controlled robots during mining to reduce costs and environmental impact, for example.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects geological data from the Geospatial Information Authority of Japan, ALOS satellite data from JAXA, Landsat data from NASA, and seabed topography data from the Japan Coast Guard via the communication I / F 44 of the smart device 14. The analysis unit, for example, uses GCNN to recognize geological patterns and predict the location of mineral deposits by the specific processing unit 290 of the data processing unit 12. The generation unit, for example, uses generation AI by the specific processing unit 290 of the data processing unit 12 to automatically generate expert-level reports from the analysis results. The provision unit, for example, uses the control unit 46A of the smart device 14 to provide reports via a web browser or smartphone app and visually display resource distribution using a GIS map. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects geological data from the Geospatial Information Authority of Japan, ALOS satellite data from JAXA, Landsat data from NASA, and seabed topography data from the Japan Coast Guard via the communication I / F 44 of the smart glasses 214. The analysis unit, for example, uses GCNN to recognize geological patterns and predict the location of mineral deposits by the identification processing unit 290 of the data processing unit 12. The generation unit, for example, uses generation AI by the identification processing unit 290 of the data processing unit 12 to automatically generate expert-level reports from the analysis results. The provision unit, for example, uses the control unit 46A of the smart glasses 214 to provide reports via a web browser or smartphone app and visually display resource distribution using a GIS map. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects geological data from the Geospatial Information Authority of Japan, ALOS satellite data from JAXA, Landsat data from NASA, and seabed topography data from the Japan Coast Guard via the communication I / F 44 of the headset terminal 314. The analysis unit, for example, uses GCNN to recognize geological patterns and predict the location of mineral deposits by the specific processing unit 290 of the data processing unit 12. The generation unit, for example, uses generation AI by the specific processing unit 290 of the data processing unit 12 to automatically generate expert-level reports from the analysis results. The provision unit, for example, uses the control unit 46A of the headset terminal 314 to provide reports via a web browser or smartphone application and visually display resource distribution using a GIS map. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects geological data from the Geospatial Information Authority of Japan, ALOS satellite data from JAXA, Landsat data from NASA, and seabed topography data from the Japan Coast Guard via the communication I / F 44 of the robot 414. The analysis unit, for example, uses GCNN by the specific processing unit 290 of the data processing unit 12 to recognize geological patterns and predict the location of mineral deposits. The generation unit, for example, uses generation AI by the specific processing unit 290 of the data processing unit 12 to automatically generate expert-level reports from the analysis results. The provision unit, for example, uses the control unit 46A of the robot 414 to provide reports via a web browser or smartphone application and visually display resource distribution using a GIS map. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts the location of the ore deposit, A generation unit that generates a report based on the analysis results obtained by the analysis unit, The system comprises a providing unit that provides the report generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect geological data, satellite image data, and oceanographic data from the Geospatial Information Authority of Japan. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Recognize geological patterns using GCNN and predict mineral deposit locations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Automatically generate expert-level reports based on analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Reports are provided via web browsers and smartphone apps. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Visually display resource distribution using GIS maps. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, It features a function to generate and display reports based on AI-generated analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, By utilizing drones and AI-controlled robots during mining, we aim to reduce costs and minimize environmental impact. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze past data collection history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, filtering is performed based on specific geological and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, information is integrated and collected from social media and public databases. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, we apply analytical methods that focus on specific minerals or resources. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, geographical data is taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, we refer to relevant scientific literature and research data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a report, adjust the level of detail in the report based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating reports, create customized reports tailored to specific user segments. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's sentiment and adjusts the length of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating reports, the priority of reports is determined based on the submission date of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating reports, the order of reports is adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing reports, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing reports, we apply display methods optimized for specific devices and platforms. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and adjusts the report's operation steps based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing reports, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing reports, we collect user feedback and continuously improve the delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts the location of the ore deposit, A generation unit that generates a report based on the analysis results obtained by the analysis unit, The system comprises a providing unit that provides the report generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect geological data, satellite image data, and oceanographic data from the Geospatial Information Authority of Japan. The system according to feature 1.

3. The aforementioned analysis unit, Recognize geological patterns using GCNN and predict mineral deposit locations. The system according to feature 1.

4. The generating unit is Automatically generate expert-level reports based on analysis results. The system according to feature 1.

5. The aforementioned supply unit is, Reports are provided via web browsers and smartphone apps. The system according to feature 1.

6. The aforementioned supply unit is, Visually display resource distribution using GIS maps. The system according to feature 1.

7. The aforementioned supply unit is, It has a function to generate and display reports based on AI-generated analysis results. The system according to feature 1.

8. The aforementioned supply unit is, By utilizing drones and AI-controlled robots during mining, we aim to reduce costs and minimize environmental impact. The system according to feature 1.

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