System

Abstract

To provide a system capable of effectively using earth observation data.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a generation unit, and a providing unit. The collection unit collects earth observation data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a piece of information based on the analysis results obtained by the analysis unit. The providing unit provides the information generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not fully utilize Earth observation data, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize Earth observation data. [Means for solving the problem]

[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 Earth observation data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates information based on the analysis result obtained by the analysis unit. The provision unit provides the information generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can effectively utilize Earth observation data. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

[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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The Metaverse Earth system according to the embodiment of the present invention is a system that integrates data obtained from earth observation satellites and uses a generation AI to provide users with the information they want to know about "now". This system collects a wide variety of data obtained from earth observation satellites and uses this as training data to build the Metaverse Earth. Next, the generation AI provides information in real time according to the user's request. For example, this includes disaster information predictions and risk assessment information for insurance companies. The generation AI analyzes the earth observation data and generates information according to the user's request. Furthermore, the Metaverse Earth provides an interface that users can intuitively operate. Users can select any point on the earth in virtual space and check the observation data. This mechanism is expected to lead to an explosive spread of utilization of earth observation data and contribute to society. For example, improved accuracy in disaster information predictions will enable rapid response and reduce damage. In addition, insurance companies can provide appropriate insurance products based on risk assessment information. This improves the safety and security of society as a whole. This allows the Metaverse Earth system to efficiently collect, analyze, generate, and provide earth observation data.

[0029] The Metaverse Earth system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects earth observation data. The earth observation data includes, for example, satellite data, weather data, earthquake data, and the like, but is not limited to such examples. The collection unit, for example, acquires satellite data in real time and periodically collects weather data. The collection unit can also acquire earthquake data immediately when an earthquake occurs. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, analyzes data using AI to perform anomaly detection and pattern recognition. The analysis unit, for example, analyzes weather data to generate a weather forecast. The analysis unit can also analyze earthquake data to predict the probability of occurrence of an earthquake. The generation unit generates information based on the analysis result obtained by the analysis unit. The generation unit, for example, uses a generation AI to generate information in real time according to a user request. The generation unit, for example, generates disaster information and provides it to a user. The generation unit can also generate risk assessment information for insurance companies. The provision unit provides the information generated by the generation unit. The providing unit provides information using, for example, AI and provides feedback to the user. The providing unit provides information through, for example, a web application. The providing unit can also provide information through a mobile application. This allows the Metaverse Earth system according to the embodiment to efficiently collect, analyze, generate, and provide Earth observation data.

[0030] The collection unit collects earth observation data. The earth observation data includes, for example, satellite data, weather data, earthquake data, etc., but is not limited to such examples. For example, the collection unit acquires satellite data in real time and periodically collects weather data. The collection unit can also immediately acquire earthquake data when an earthquake occurs. Specifically, the collection unit integrates data from multiple satellites to grasp the weather conditions and surface changes of the entire earth with high accuracy. This includes various satellite data such as optical satellites, radar satellites, and infrared satellites. Optical satellites provide detailed images of the earth's surface, and radar satellites can detect surface changes regardless of the weather. Infrared satellites help understand the temperature distribution of the earth's surface. The weather data also includes data from ground weather stations and marine buoys, which allows detailed weather information such as temperature, humidity, wind speed, and precipitation to be collected. The earthquake data is acquired from seismometers and accelerometers in real time to quickly grasp the location and scale of an earthquake. This allows the collection unit to efficiently collect various data from the entire earth and update it in real time. In addition, the collection unit will build a database to centrally manage these data and make them easily accessible to the analysis unit and generation unit. The frequency and accuracy of data collection can be adjusted according to specific situations and needs, allowing the collection unit to collect data flexibly and efficiently and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and perform anomaly detection and pattern recognition. Specifically, the AI ​​uses machine learning algorithms and deep learning technology to extract useful information from large amounts of data. For example, when analyzing weather data to generate a weather forecast, past weather data and current observation data are combined to build a model to predict future weather. This includes time series analysis using multivariate data such as temperature, humidity, wind speed, and precipitation. In addition, when analyzing earthquake data to predict the probability of an earthquake occurring, the risk of an earthquake occurring in a specific area is evaluated based on past earthquake data and geological information. The AI ​​can analyze these data in real time and detect abnormal patterns and signs. For example, it detects sudden temperature changes and abnormal precipitation patterns in the weather data, suggesting that this may be a precursor to abnormal weather. It also detects an increase in micro-seismic activity in the earthquake data, and evaluates the possibility that this is a precursor to a large-scale earthquake. The analysis unit provides the generation unit with fast and accurate information based on these analysis results, and supports the provision of appropriate information to users. In addition, the analysis unit can use past data and statistical information to perform long-term risk assessments and trend analysis, allowing the analysis unit to handle not only real-time situation assessments but also long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The generation unit generates information based on the analysis results obtained by the analysis unit. The generation unit generates information in real time according to the user's request, for example, using a generation AI. Specifically, the generation AI generates information in a form that makes the analysis results easy for the user to understand, using natural language processing technology. For example, when generating disaster information, the generation AI generates sentences that explain the current situation and predicted risks in detail based on weather data and earthquake data. The generation AI can quickly provide necessary information in response to a user's prompt. For example, when a user inputs "Please tell me the current weather forecast," the generation AI explains the current weather and future forecasts in detail based on the latest weather data. In addition, when generating risk assessment information for insurance companies, the generation AI generates a report that explains the risk assessment in a specific area or building in detail based on the analysis results. The generation unit generates this information in real time and provides it to the user, thereby realizing prompt and appropriate information provision. Furthermore, the generation unit can continuously improve the accuracy and content of the generated information based on user feedback. For example, by analyzing user feedback and updating the generation AI model, more accurate and useful information can be provided. This enables the generation unit to provide high-quality information that meets the needs of users, thereby improving the value of the entire system.

[0033] The providing unit provides the information generated by the generating unit. The providing unit provides information using, for example, AI and provides feedback to the user. Specifically, the providing unit provides information through a web application or a mobile application. In the web application, the user can access through a browser and view the latest weather information, earthquake information, risk assessment information, and the like. In the mobile application, the user can receive information in real time through a smartphone or tablet. The providing unit provides an interface for visually displaying the information in an easy-to-understand manner, allowing the user to intuitively understand the information. For example, the weather information is displayed on a map, and the current weather and the predicted precipitation are displayed in different colors. In addition, earthquake information is displayed on a map, and the location and scale of the earthquake are visually indicated. The providing unit can collect feedback from the user and continuously improve the accuracy and content of the information provided. For example, the user can evaluate and comment on the provided information, and the providing unit can provide information that reflects the user's needs and opinions. In addition, the providing unit can reliably transmit information using multiple communication means. For example, important information is quickly and reliably delivered to the user by using not only web applications or mobile applications, but also email, SMS, push notifications, and the like. This enables the providing unit to provide information to the user quickly and reliably, thereby improving the reliability and convenience of the entire system.

[0034] The Metaverse Earth system includes an interface unit that provides a user interface. The interface unit provides an interface that can be intuitively operated by the user. For example, the interface unit provides a graphical user interface (GUI) to enable the user to visually check information. The interface unit can also provide a command line interface (CLI) to enable the user to operate by inputting commands. For example, the interface unit provides a map display function for selecting any point on the Earth and checking observation data. The interface unit can also provide a function for filtering and displaying data that is of interest to the user. This allows the user to quickly obtain the information he or she needs through an interface that can be intuitively operated.

[0035] The Metaverse Earth system includes an evaluation unit that performs risk evaluation for insurance companies. The evaluation unit performs risk evaluation for insurance companies. Risk evaluation includes, for example, disaster risk, weather risk, earthquake risk, and the like, but is not limited to such examples. The evaluation unit evaluates disaster risk, for example, using AI. The evaluation unit can also evaluate weather risk. Furthermore, the evaluation unit can evaluate earthquake risk. For example, the evaluation unit analyzes disaster data and evaluates disaster risk. Analyzes weather data and evaluates weather risk. Analyzes earthquake data and evaluates earthquake risk. This allows insurance companies to perform appropriate risk evaluation. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI.

[0036] The collection unit can collect at least one of weather data, earthquake data, and flood data. The collection unit, for example, collects weather data. The weather data includes, for example, temperature, humidity, wind speed, and the like, but is not limited to such examples. The collection unit, for example, collects earthquake data. The earthquake data includes, for example, seismic intensity, epicenter, and occurrence time, but is not limited to such examples. The collection unit, for example, collects flood data. The flood data includes, for example, water level, flow rate, flooded area, and the like, but is not limited to such examples. By collecting a wide variety of data, it becomes possible to provide more comprehensive information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0037] The generating unit can generate information in response to a user request in real time. The generating unit generates information in response to a user request in real time, for example, using a generating AI. The generating unit generates, for example, disaster information and provides it to the user. The generating unit can also generate risk assessment information for an insurance company. For example, when a user requests disaster information, the generating AI analyzes disaster data and generates disaster information in real time. When an insurance company requests risk assessment information, the generating AI analyzes risk data and generates risk assessment information in real time. This makes it possible to provide information in response to a user request in real time. Some or all of the above-mentioned processing in the generating unit may be performed, for example, using an AI, or may be performed without using an AI.

[0038] The collection unit can analyze past data collection history and select the optimal collection method. The collection unit, for example, analyzes past data collection history and selects the optimal collection method. For example, the collection unit selects the most effective data collection method for a specific time period based on the past data collection history. The collection unit can also analyze past data collection history and select the most efficient collection method under specific conditions. Furthermore, the collection unit can also refer to the past data collection history and select the optimal collection method at the time of a specific event. In this way, the optimal collection method can be selected by analyzing the past data collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0039] The collection unit can perform filtering based on the user's current field of interest when collecting data. The collection unit performs filtering based on the user's current field of interest when collecting data, for example. For example, if the user is interested in disaster information, the collection unit preferentially collects disaster-related data. Also, if the user is interested in weather information, the collection unit can also preferentially collect weather data. Furthermore, if the user is interested in earthquake information, the collection unit can also preferentially collect earthquake data. This makes it possible to provide more relevant information by filtering data based on the user's field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0040] The collection unit can preferentially collect highly relevant data in consideration of geographical location information when collecting data. For example, the collection unit preferentially collects highly relevant data in consideration of geographical location information when collecting data. For example, if a user is interested in a specific area, the collection unit preferentially collects data about that area. In addition, if the user is traveling, the collection unit can also preferentially collect data about the current location. Furthermore, if the user is in a disaster area, the collection unit can also preferentially collect disaster information about that area. In this way, by considering geographical location information, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0041] The collection unit can analyze social media activities and collect related data when collecting data. The collection unit, for example, analyzes social media activities and collects related data when collecting data. For example, if a user frequently shares disaster information on social media, the collection unit preferentially collects disaster-related data. In addition, if a user frequently posts weather information, the collection unit can also preferentially collect weather data. Furthermore, if a user frequently retweets earthquake information, the collection unit can also preferentially collect earthquake data. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. Also, the analysis unit can perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0043] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a weather forecasting algorithm to weather data. Also, the analysis unit can apply an earthquake forecasting algorithm to earthquake data. Furthermore, the analysis unit can apply a flood forecasting algorithm to flood data. In this way, by applying an appropriate analysis algorithm depending on the category of data, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The analysis unit can determine the analysis priority based on the collection time of the data during the analysis. The analysis unit, for example, determines the analysis priority based on the collection time of the data during the analysis. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also analyze the latest data while referring to past data. Furthermore, the analysis unit can also prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the analysis priority based on the collection time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. Also, it is possible to analyze less relevant data later. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0046] The generating unit can adjust the level of detail of the generation based on the importance of the data at the time of generation. The generating unit adjusts the level of detail of the generation based on the importance of the data at the time of generation, for example. For example, the generating unit generates detailed information for data of high importance. Also, simplified information can be generated for data of low importance. Furthermore, information of moderate detail can be generated for data of medium importance. This enables efficient information generation by adjusting the level of detail of the generation based on the importance of the data. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The generation unit can apply different generation algorithms depending on the category of data at the time of generation. The generation unit, for example, applies different generation algorithms depending on the category of data at the time of generation. For example, the generation unit applies a weather forecasting algorithm to weather data to generate information. Also, the generation unit can apply an earthquake forecasting algorithm to earthquake data to generate information. Furthermore, the generation unit can apply a flood forecasting algorithm to flood data to generate information. In this way, by applying an appropriate generation algorithm depending on the category of data, the generation accuracy is improved. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0048] The generating unit can determine the priority of generation based on the collection time of data at the time of generation. The generating unit determines the priority of generation based on the collection time of data at the time of generation, for example. For example, the generating unit generates the latest data preferentially. The generating unit can also generate the latest data while referring to past data. Furthermore, the generating unit can also generate data collected in a specific period preferentially. This enables efficient information generation by determining the priority of generation based on the collection time of data. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The generating unit can adjust the order of generation based on the relevance of data at the time of generation. The generating unit adjusts the order of generation based on the relevance of data at the time of generation, for example. For example, the generating unit generates highly relevant data preferentially. Also, data with low relevance can be generated later. Furthermore, the generating unit can dynamically adjust the order of generation according to the relevance of data. This enables efficient information generation by adjusting the order of generation based on the relevance of data. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI.

[0050] The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method based on the method used by the user in the past. The providing unit can also select the most effective providing method from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and select the optimal providing method under specific conditions. In this way, the optimal providing method can be selected by referring to the user's past usage history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0051] The providing unit can customize information based on the user's current field of interest at the time of providing. The providing unit customizes information based on the user's current field of interest at the time of providing, for example. For example, if the user is interested in disaster information, the providing unit can provide disaster-related information preferentially. Also, if the user is interested in weather information, the providing unit can also provide weather-related information preferentially. Furthermore, if the user is interested in earthquake information, the providing unit can also provide earthquake-related information preferentially. This allows more relevant information to be provided by customizing information based on the user's field of interest. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The providing unit can provide optimal information in consideration of the geographical location information of the user at the time of providing. The providing unit provides optimal information in consideration of the geographical location information of the user at the time of providing, for example. For example, when the user is in a specific area, the providing unit provides information of that area preferentially. In addition, when the user is traveling, the providing unit can also provide information about the current location preferentially. Furthermore, when the user is in a disaster area, the providing unit can also provide disaster information about that area preferentially. In this way, optimal information can be provided by considering the geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0053] The providing unit can provide related information by analyzing the social media activity of the user at the time of providing. The providing unit, for example, provides related information by analyzing the social media activity of the user at the time of providing. For example, if the user frequently shares disaster information on social media, the providing unit can provide disaster-related information preferentially. In addition, if the user frequently posts weather information, the providing unit can also provide weather-related information preferentially. Furthermore, if the user frequently retweetes earthquake information, the providing unit can also provide earthquake-related information preferentially. This makes it possible to efficiently provide related information by analyzing social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0054] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit selects the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit selects the optimal display method based on the display method used by the user in the past. The interface unit can also select the most effective display method from the user's past operation history. Furthermore, the interface unit can analyze the user's past operation history and select the optimal display method under specific conditions. In this way, the optimal display method can be selected by referring to the user's past operation history. A part or all of the above-mentioned processing in the interface unit may be performed, for example, using AI, or may be performed without using AI.

[0055] The interface unit can select the optimal display method in consideration of the device information of the user when displaying the interface. For example, the interface unit selects the optimal display method in consideration of the device information of the user when displaying the interface. For example, when the user is using a smartphone, the interface unit provides a display method that matches the screen size. Also, when the user is using a tablet, the interface unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the interface unit can also provide a simple and highly visible display method. This makes it possible to provide the optimal display method by considering the device information of the user. A part or all of the above-mentioned processing in the interface unit may be performed, for example, using AI, or may be performed without using AI.

[0056] The prediction unit can optimize the prediction algorithm by referring to past data at the time of prediction. The prediction unit optimizes the prediction algorithm by referring to past data at the time of prediction, for example. For example, the prediction unit selects an optimal prediction algorithm based on past data. The prediction unit can also analyze past data and select the most effective prediction algorithm under specific conditions. Furthermore, the prediction unit can dynamically adjust the prediction algorithm by referring to past data. This allows the prediction algorithm to be optimized by referring to past data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0057] The prediction unit can apply different prediction methods depending on the category of data at the time of prediction. The prediction unit applies different prediction methods depending on the category of data at the time of prediction, for example. For example, the prediction unit applies a weather forecasting method to weather data. Also, it can apply an earthquake forecasting method to earthquake data. Furthermore, it can apply a flood forecasting method to flood data. In this way, prediction accuracy is improved by applying an appropriate forecasting method depending on the category of data. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

[0058] The prediction unit can weight the prediction based on the collection time of the data at the time of prediction. The prediction unit, for example, weights the prediction based on the collection time of the data at the time of prediction. For example, the prediction unit makes a prediction by placing emphasis on the latest data. The prediction unit can also make a prediction by placing emphasis on the latest data while referring to past data. Furthermore, the prediction unit can also make a prediction by placing emphasis on data collected during a specific period. As a result, prediction accuracy is improved by weighting the prediction based on the collection time of the data. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI.

[0059] The prediction unit can improve the accuracy of the prediction by referring to the related market data at the time of prediction. The prediction unit can improve the accuracy of the prediction by referring to the related market data at the time of prediction, for example. For example, the prediction unit improves the accuracy of the prediction based on the related market data. The prediction unit can also analyze the related market data and improve the accuracy of the prediction under specific conditions. Furthermore, the prediction unit can dynamically adjust the prediction algorithm by referring to the related market data. Thereby, the accuracy of the prediction can be improved by referring to the related market data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0060] The evaluation unit can optimize the evaluation algorithm by referring to past data during evaluation. The evaluation unit can optimize the evaluation algorithm by referring to past data during evaluation, for example. For example, the evaluation unit selects an optimal evaluation algorithm based on past data. The evaluation unit can also analyze past data and select an evaluation algorithm that is most effective under specific conditions. Furthermore, the evaluation unit can dynamically adjust the evaluation algorithm by referring to past data. In this way, the evaluation algorithm can be optimized by referring to past data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI.

[0061] The evaluation unit can apply different evaluation methods depending on the category of data during evaluation. The evaluation unit, for example, applies different evaluation methods depending on the category of data during evaluation. For example, the evaluation unit applies a meteorological evaluation method to meteorological data. Also, the evaluation unit can apply an earthquake evaluation method to earthquake data. Furthermore, the evaluation unit can apply a flood evaluation method to flood data. In this way, the evaluation accuracy is improved by applying an appropriate evaluation method depending on the category of data. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI.

[0062] The evaluation unit can weight the evaluation based on the collection time of the data at the time of evaluation. The evaluation unit, for example, weights the evaluation based on the collection time of the data at the time of evaluation. For example, the evaluation unit performs the evaluation by placing emphasis on the latest data. The evaluation unit can also perform the evaluation by placing emphasis on the latest data while referring to past data. Furthermore, the evaluation unit can also perform the evaluation by placing emphasis on data collected in a specific period. Thereby, the evaluation accuracy is improved by weighting the evaluation based on the collection time of the data. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI.

[0063] The evaluation unit can improve the accuracy of the evaluation by referring to the related market data at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to the related market data at the time of evaluation, for example. For example, the evaluation unit improves the accuracy of the evaluation based on the related market data. The evaluation unit can also analyze the related market data and improve the accuracy of the evaluation under specific conditions. Furthermore, the evaluation unit can dynamically adjust the evaluation algorithm by referring to the related market data. Thereby, the accuracy of the evaluation can be improved by referring to the related market data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The Metaverse Earth system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and transmits it to the analysis unit. The analysis unit can analyze these data and evaluate the user's health condition. For example, if the heart rate is abnormally high, the analysis unit can generate a notification encouraging the user to rest. Also, if the blood pressure is high, the analysis unit can generate a notification encouraging the user to see a doctor. Furthermore, if the body temperature is high, the analysis unit can generate a notification encouraging the user to drink water. This makes it possible to monitor the user's health condition in real time and provide appropriate advice.

[0066] The Metaverse Earth system may further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit collects the user's past behavioral data and transmits it to the analysis unit. The analysis unit can analyze these data and identify the user's behavioral patterns. For example, if the user is often in a specific location at a specific time, the analysis unit can provide information related to that time with priority. Also, if the user frequently participates in a specific event, the analysis unit can provide information related to that event. Furthermore, if the user has a specific interest, the analysis unit can provide information related to that interest. This makes it possible to provide more personalized information by analyzing the user's behavioral history.

[0067] The Metaverse Earth system can further include a learning analysis unit that analyzes the user's learning history. The learning analysis unit collects the user's past learning data and transmits it to the analysis unit. The analysis unit can analyze these data and identify the user's learning patterns. For example, if the user is interested in a particular field, the analysis unit can provide information related to that field preferentially. Also, if the user prefers a particular learning method, the analysis unit can provide learning content based on that method. Furthermore, if the user often studies during a particular time period, the analysis unit can send learning notifications during that time period. This makes it possible to provide more effective learning support by analyzing the user's learning history.

[0068] The Metaverse Earth system can further include a purchase analysis unit that analyzes the user's purchase history. The purchase analysis unit collects the user's past purchase data and transmits it to the analysis unit. The analysis unit can analyze these data and identify the user's purchase patterns. For example, if the user likes a particular brand, the analysis unit can provide information related to that brand preferentially. Also, if the user frequently purchases products of a particular category, the analysis unit can provide information related to that category. Furthermore, if the user often purchases a particular product at a particular time, the analysis unit can provide information related to that time. This makes it possible to provide more personalized information by analyzing the user's purchase history.

[0069] The Metaverse Earth system may further include a social analysis unit that analyzes the user's social network. The social analysis unit collects the user's social network data and transmits it to the analysis unit. The analysis unit may analyze these data and identify the structure of the user's social network. For example, if the user belongs to a particular group, the analysis unit may provide information related to that group preferentially. Also, if the user frequently interacts with a particular person, the analysis unit may provide information related to that person. Furthermore, if the user participates in a particular event, the analysis unit may provide information related to that event. This allows for more relevant information to be provided by analyzing the user's social network.

[0070] The Metaverse Earth system may further include a feedback collection unit that collects user feedback. The feedback collection unit collects user feedback data and transmits it to the analysis unit. The analysis unit can analyze these data and identify improvements to the system. For example, if a user is dissatisfied with a particular function, the analysis unit can generate a proposal to improve that function. Also, if a user gives a high rating to a particular function, the analysis unit can generate a proposal to enhance that function. Furthermore, if a user requests a new function, the analysis unit can generate a proposal to add that function. This allows for continuous improvement of the system by collecting user feedback.

[0071] The process flow of the first embodiment will be briefly described below.

[0072] Step 1: The collection unit collects earth observation data. The earth observation data includes, but is not limited to, satellite data, meteorological data, and earthquake data. For example, the collection unit acquires satellite data in real time and periodically collects meteorological data. The collection unit can also immediately acquire earthquake data when an earthquake occurs. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using AI to detect anomalies and recognize patterns. For example, the analysis unit analyzes meteorological data to generate a weather forecast. The analysis unit can also analyze earthquake data to predict the probability of an earthquake occurring. Step 3: The generation unit generates information based on the analysis results obtained by the analysis unit. The generation unit generates information in real time according to a user request, for example, using a generation AI. The generation unit generates disaster information, for example, and provides it to the user. The generation unit can also generate risk assessment information for insurance companies. Step 4: The providing unit provides the information generated by the generating unit. The providing unit provides the information using, for example, AI and provides feedback to the user. The providing unit provides the information through, for example, a web application. The providing unit can also provide the information through a mobile application.

[0073] (Example 2) The Metaverse Earth system according to the embodiment of the present invention is a system that integrates data obtained from earth observation satellites and uses a generation AI to provide users with the information they want to know about "now". This system collects a wide variety of data obtained from earth observation satellites and uses this as training data to build the Metaverse Earth. Next, the generation AI provides information in real time according to the user's request. For example, this includes disaster information predictions and risk assessment information for insurance companies. The generation AI analyzes the earth observation data and generates information according to the user's request. Furthermore, the Metaverse Earth provides an interface that users can intuitively operate. Users can select any point on the earth in virtual space and check the observation data. This mechanism is expected to lead to an explosive spread of utilization of earth observation data and contribute to society. For example, improved accuracy in disaster information predictions will enable rapid response and reduce damage. In addition, insurance companies can provide appropriate insurance products based on risk assessment information. This improves the safety and security of society as a whole. This allows the Metaverse Earth system to efficiently collect, analyze, generate, and provide earth observation data.

[0074] The Metaverse Earth system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects earth observation data. The earth observation data includes, for example, satellite data, weather data, earthquake data, and the like, but is not limited to such examples. The collection unit, for example, acquires satellite data in real time and periodically collects weather data. The collection unit can also acquire earthquake data immediately when an earthquake occurs. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, analyzes data using AI to perform anomaly detection and pattern recognition. The analysis unit, for example, analyzes weather data to generate a weather forecast. The analysis unit can also analyze earthquake data to predict the probability of occurrence of an earthquake. The generation unit generates information based on the analysis result obtained by the analysis unit. The generation unit, for example, uses a generation AI to generate information in real time according to a user request. The generation unit, for example, generates disaster information and provides it to a user. The generation unit can also generate risk assessment information for insurance companies. The provision unit provides the information generated by the generation unit. The providing unit provides information using, for example, AI and provides feedback to the user. The providing unit provides information through, for example, a web application. The providing unit can also provide information through a mobile application. This allows the Metaverse Earth system according to the embodiment to efficiently collect, analyze, generate, and provide Earth observation data.

[0075] The collection unit collects earth observation data. The earth observation data includes, for example, satellite data, weather data, earthquake data, etc., but is not limited to such examples. For example, the collection unit acquires satellite data in real time and periodically collects weather data. The collection unit can also immediately acquire earthquake data when an earthquake occurs. Specifically, the collection unit integrates data from multiple satellites to grasp the weather conditions and surface changes of the entire earth with high accuracy. This includes various satellite data such as optical satellites, radar satellites, and infrared satellites. Optical satellites provide detailed images of the earth's surface, and radar satellites can detect surface changes regardless of the weather. Infrared satellites help understand the temperature distribution of the earth's surface. The weather data also includes data from ground weather stations and marine buoys, which allows detailed weather information such as temperature, humidity, wind speed, and precipitation to be collected. The earthquake data is acquired from seismometers and accelerometers in real time to quickly grasp the location and scale of an earthquake. This allows the collection unit to efficiently collect various data from the entire earth and update it in real time. In addition, the collection unit will build a database to centrally manage these data and make them easily accessible to the analysis unit and generation unit. The frequency and accuracy of data collection can be adjusted according to specific situations and needs, allowing the collection unit to collect data flexibly and efficiently and improve the performance of the entire system.

[0076] The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and perform anomaly detection and pattern recognition. Specifically, the AI ​​uses machine learning algorithms and deep learning technology to extract useful information from large amounts of data. For example, when analyzing weather data to generate a weather forecast, past weather data and current observation data are combined to build a model to predict future weather. This includes time series analysis using multivariate data such as temperature, humidity, wind speed, and precipitation. In addition, when analyzing earthquake data to predict the probability of an earthquake occurring, the risk of an earthquake occurring in a specific area is evaluated based on past earthquake data and geological information. The AI ​​can analyze these data in real time and detect abnormal patterns and signs. For example, it detects sudden temperature changes and abnormal precipitation patterns in the weather data, suggesting that this may be a precursor to abnormal weather. It also detects an increase in micro-seismic activity in the earthquake data, and evaluates the possibility that this is a precursor to a large-scale earthquake. The analysis unit provides the generation unit with fast and accurate information based on these analysis results, and supports the provision of appropriate information to users. In addition, the analysis unit can use past data and statistical information to perform long-term risk assessments and trend analysis, allowing the analysis unit to handle not only real-time situation assessments but also long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0077] The generation unit generates information based on the analysis results obtained by the analysis unit. The generation unit generates information in real time according to the user's request, for example, using a generation AI. Specifically, the generation AI generates information in a form that makes the analysis results easy for the user to understand, using natural language processing technology. For example, when generating disaster information, the generation AI generates sentences that explain the current situation and predicted risks in detail based on weather data and earthquake data. The generation AI can quickly provide necessary information in response to a user's prompt. For example, when a user inputs "Please tell me the current weather forecast," the generation AI explains the current weather and future forecasts in detail based on the latest weather data. In addition, when generating risk assessment information for insurance companies, the generation AI generates a report that explains the risk assessment in a specific area or building in detail based on the analysis results. The generation unit generates this information in real time and provides it to the user, thereby realizing prompt and appropriate information provision. Furthermore, the generation unit can continuously improve the accuracy and content of the generated information based on user feedback. For example, by analyzing user feedback and updating the generation AI model, more accurate and useful information can be provided. This enables the generation unit to provide high-quality information that meets the needs of users, thereby improving the value of the entire system.

[0078] The providing unit provides the information generated by the generating unit. The providing unit provides information using, for example, AI and provides feedback to the user. Specifically, the providing unit provides information through a web application or a mobile application. In the web application, the user can access through a browser and view the latest weather information, earthquake information, risk assessment information, and the like. In the mobile application, the user can receive information in real time through a smartphone or tablet. The providing unit provides an interface for visually displaying the information in an easy-to-understand manner, allowing the user to intuitively understand the information. For example, the weather information is displayed on a map, and the current weather and the predicted precipitation are displayed in different colors. In addition, earthquake information is displayed on a map, and the location and scale of the earthquake are visually indicated. The providing unit can collect feedback from the user and continuously improve the accuracy and content of the information provided. For example, the user can evaluate and comment on the provided information, and the providing unit can provide information that reflects the user's needs and opinions. In addition, the providing unit can reliably transmit information using multiple communication means. For example, important information is quickly and reliably delivered to the user by using not only web applications or mobile applications, but also email, SMS, push notifications, and the like. This enables the providing unit to provide information to the user quickly and reliably, thereby improving the reliability and convenience of the entire system.

[0079] The Metaverse Earth system includes an interface unit that provides a user interface. The interface unit provides an interface that can be intuitively operated by the user. For example, the interface unit provides a graphical user interface (GUI) to enable the user to visually check information. The interface unit can also provide a command line interface (CLI) to enable the user to operate by inputting commands. For example, the interface unit provides a map display function for selecting any point on the Earth and checking observation data. The interface unit can also provide a function for filtering and displaying data that is of interest to the user. This allows the user to quickly obtain the information he or she needs through an interface that can be intuitively operated.

[0080] The Metaverse Earth system includes a prediction unit that predicts disaster information. The prediction unit predicts disaster information. The disaster information includes, but is not limited to, earthquakes, floods, typhoons, and the like. The prediction unit predicts the probability of an earthquake, for example, using AI. The prediction unit can also predict the risk of flooding. The prediction unit can also predict the path of a typhoon. For example, the prediction unit analyzes earthquake data to predict the probability of an earthquake. The prediction unit analyzes flood data to predict the risk of flooding. The prediction unit analyzes typhoon data to predict the path of the typhoon. This improves the prediction accuracy of disaster information and enables rapid response. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0081] The Metaverse Earth system includes an evaluation unit that performs risk evaluation for insurance companies. The evaluation unit performs risk evaluation for insurance companies. Risk evaluation includes, for example, disaster risk, weather risk, earthquake risk, and the like, but is not limited to such examples. The evaluation unit evaluates disaster risk, for example, using AI. The evaluation unit can also evaluate weather risk. Furthermore, the evaluation unit can evaluate earthquake risk. For example, the evaluation unit analyzes disaster data and evaluates disaster risk. Analyzes weather data and evaluates weather risk. Analyzes earthquake data and evaluates earthquake risk. This allows insurance companies to perform appropriate risk evaluation. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The collection unit can collect at least one of weather data, earthquake data, and flood data. The collection unit, for example, collects weather data. The weather data includes, for example, temperature, humidity, wind speed, and the like, but is not limited to such examples. The collection unit, for example, collects earthquake data. The earthquake data includes, for example, seismic intensity, epicenter, and occurrence time, but is not limited to such examples. The collection unit, for example, collects flood data. The flood data includes, for example, water level, flow rate, flooded area, and the like, but is not limited to such examples. By collecting a wide variety of data, it becomes possible to provide more comprehensive information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0083] The generating unit can generate information in response to a user request in real time. The generating unit generates information in response to a user request in real time, for example, using a generating AI. The generating unit generates, for example, disaster information and provides it to the user. The generating unit can also generate risk assessment information for an insurance company. For example, when a user requests disaster information, the generating AI analyzes disaster data and generates disaster information in real time. When an insurance company requests risk assessment information, the generating AI analyzes risk data and generates risk assessment information in real time. This makes it possible to provide information in response to a user request in real time. Some or all of the above-mentioned processing in the generating unit may be performed, for example, using an AI, or may be performed without using an AI.

[0084] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of data collection based on the estimated user's emotion. For example, when the user is nervous, the collection unit increases the frequency of data collection and enhances real-time information provision. Also, when the user is relaxed, the collection unit can reduce the frequency of data collection and provide the minimum necessary information. Furthermore, when the user is excited, the collection unit can also preferentially collect data related to a specific field of interest. This makes it possible to provide more appropriate information by adjusting the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] The collection unit can analyze past data collection history and select the optimal collection method. The collection unit, for example, analyzes past data collection history and selects the optimal collection method. For example, the collection unit selects the most effective data collection method for a specific time period based on the past data collection history. The collection unit can also analyze past data collection history and select the most efficient collection method under specific conditions. Furthermore, the collection unit can also refer to the past data collection history and select the optimal collection method at the time of a specific event. In this way, the optimal collection method can be selected by analyzing the past data collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0086] The collection unit can perform filtering based on the user's current field of interest when collecting data. The collection unit performs filtering based on the user's current field of interest when collecting data, for example. For example, if the user is interested in disaster information, the collection unit preferentially collects disaster-related data. Also, if the user is interested in weather information, the collection unit can also preferentially collect weather data. Furthermore, if the user is interested in earthquake information, the collection unit can also preferentially collect earthquake data. This makes it possible to provide more relevant information by filtering data based on the user's field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0087] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. For example, when the user is nervous, the collection unit preferentially collects data with high urgency. In addition, when the user is relaxed, the collection unit can also preferentially collect general information. Furthermore, when the user is excited, the collection unit can also preferentially collect data related to a specific field of interest. This makes it possible to provide more appropriate information by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] The collection unit can preferentially collect highly relevant data in consideration of geographical location information when collecting data. For example, the collection unit preferentially collects highly relevant data in consideration of geographical location information when collecting data. For example, if a user is interested in a specific area, the collection unit preferentially collects data about that area. In addition, if the user is traveling, the collection unit can also preferentially collect data about the current location. Furthermore, if the user is in a disaster area, the collection unit can also preferentially collect disaster information about that area. In this way, by considering geographical location information, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0089] The collection unit can analyze social media activities and collect related data when collecting data. The collection unit, for example, analyzes social media activities and collects related data when collecting data. For example, if a user frequently shares disaster information on social media, the collection unit preferentially collects disaster-related data. In addition, if a user frequently posts weather information, the collection unit can also preferentially collect weather data. Furthermore, if a user frequently retweets earthquake information, the collection unit can also preferentially collect earthquake data. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0090] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, by adjusting the expression method of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. Also, the analysis unit can perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0092] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a weather forecasting algorithm to weather data. Also, the analysis unit can apply an earthquake forecasting algorithm to earthquake data. Furthermore, the analysis unit can apply a flood forecasting algorithm to flood data. In this way, by applying an appropriate analysis algorithm depending on the category of data, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0093] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, if the user is in a hurry, the analysis unit provides a short and to the point analysis result. In addition, if the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, by adjusting the length of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The analysis unit can determine the analysis priority based on the collection time of the data during the analysis. The analysis unit, for example, determines the analysis priority based on the collection time of the data during the analysis. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also analyze the latest data while referring to past data. Furthermore, the analysis unit can also prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the analysis priority based on the collection time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0095] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. Also, it is possible to analyze less relevant data later. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0096] The generation unit can estimate the user's emotion and adjust the expression method of the information to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the information to be generated based on the estimated user's emotion. For example, when the user is nervous, the generation unit generates simple and highly visible information. When the user is relaxed, the generation unit can also generate detailed information. Furthermore, when the user is excited, the generation unit can also generate visually stimulating information. This makes it possible to provide more appropriate information by adjusting the expression method of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The generating unit can adjust the level of detail of the generation based on the importance of the data at the time of generation. The generating unit adjusts the level of detail of the generation based on the importance of the data at the time of generation, for example. For example, the generating unit generates detailed information for data of high importance. Also, simplified information can be generated for data of low importance. Furthermore, information of moderate detail can be generated for data of medium importance. This enables efficient information generation by adjusting the level of detail of the generation based on the importance of the data. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI.

[0098] The generation unit can apply different generation algorithms depending on the category of data at the time of generation. The generation unit, for example, applies different generation algorithms depending on the category of data at the time of generation. For example, the generation unit applies a weather forecasting algorithm to weather data to generate information. Also, the generation unit can apply an earthquake forecasting algorithm to earthquake data to generate information. Furthermore, the generation unit can apply a flood forecasting algorithm to flood data to generate information. In this way, by applying an appropriate generation algorithm depending on the category of data, the generation accuracy is improved. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0099] The generation unit can estimate the user's emotion and adjust the length of the information to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the information to be generated based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates short, to-the-point information. When the user is relaxed, the generation unit can also generate detailed information. Furthermore, when the user is excited, the generation unit can also generate visually stimulating information. This makes it possible to provide more appropriate information by adjusting the length of the information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The generating unit can determine the priority of generation based on the collection time of data at the time of generation. The generating unit determines the priority of generation based on the collection time of data at the time of generation, for example. For example, the generating unit generates the latest data preferentially. The generating unit can also generate the latest data while referring to past data. Furthermore, the generating unit can also generate data collected in a specific period preferentially. This enables efficient information generation by determining the priority of generation based on the collection time of data. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI.

[0101] The generating unit can adjust the order of generation based on the relevance of data at the time of generation. The generating unit adjusts the order of generation based on the relevance of data at the time of generation, for example. For example, the generating unit generates highly relevant data preferentially. Also, data with low relevance can be generated later. Furthermore, the generating unit can dynamically adjust the order of generation according to the relevance of data. This enables efficient information generation by adjusting the order of generation based on the relevance of data. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI.

[0102] The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing information based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides information in a simple and highly visible manner. In addition, when the user is relaxed, the providing unit can also provide detailed information. Furthermore, when the user is excited, the providing unit can also provide information in a visually stimulating manner. This allows for more appropriate information provision by adjusting the method of providing information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method based on the method used by the user in the past. The providing unit can also select the most effective providing method from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and select the optimal providing method under specific conditions. In this way, the optimal providing method can be selected by referring to the user's past usage history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0104] The providing unit can customize information based on the user's current field of interest at the time of providing. The providing unit customizes information based on the user's current field of interest at the time of providing, for example. For example, if the user is interested in disaster information, the providing unit can provide disaster-related information preferentially. Also, if the user is interested in weather information, the providing unit can also provide weather-related information preferentially. Furthermore, if the user is interested in earthquake information, the providing unit can also provide earthquake-related information preferentially. This allows more relevant information to be provided by customizing information based on the user's field of interest. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0105] The providing unit can estimate the user's emotion and determine the priority of information provision based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of information provision based on the estimated user's emotion. For example, when the user is nervous, the providing unit can provide information with high urgency with priority. In addition, when the user is relaxed, the providing unit can also provide general information with priority. Furthermore, when the user is excited, the providing unit can also provide information related to a specific field of interest with priority. This enables more appropriate information provision by determining the priority of information provision according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0106] The providing unit can provide optimal information in consideration of the geographical location information of the user at the time of providing. The providing unit provides optimal information in consideration of the geographical location information of the user at the time of providing, for example. For example, when the user is in a specific area, the providing unit provides information of that area preferentially. In addition, when the user is traveling, the providing unit can also provide information about the current location preferentially. Furthermore, when the user is in a disaster area, the providing unit can also provide disaster information about that area preferentially. In this way, optimal information can be provided by considering the geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0107] The providing unit can provide related information by analyzing the social media activity of the user at the time of providing. The providing unit, for example, provides related information by analyzing the social media activity of the user at the time of providing. For example, if the user frequently shares disaster information on social media, the providing unit can provide disaster-related information preferentially. In addition, if the user frequently posts weather information, the providing unit can also provide weather-related information preferentially. Furthermore, if the user frequently retweetes earthquake information, the providing unit can also provide earthquake-related information preferentially. This makes it possible to efficiently provide related information by analyzing social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0108] The interface unit can estimate the user's emotion and adjust the display method of the interface based on the estimated user's emotion. The interface unit, for example, estimates the user's emotion and adjusts the display method of the interface based on the estimated user's emotion. For example, when the user is nervous, the interface unit provides an interface with a calm color scheme to reduce visual stress. Also, when the user is having fun, the interface unit can provide an interface with a bright color scheme to make input work enjoyable. Furthermore, when the user is tired, the interface unit can provide a simple and highly visible interface to facilitate input work. This allows a more appropriate interface to be provided by adjusting the display method of the interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0109] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit selects the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit selects the optimal display method based on the display method used by the user in the past. The interface unit can also select the most effective display method from the user's past operation history. Furthermore, the interface unit can analyze the user's past operation history and select the optimal display method under specific conditions. In this way, the optimal display method can be selected by referring to the user's past operation history. A part or all of the above-mentioned processing in the interface unit may be performed, for example, using AI, or may be performed without using AI.

[0110] The interface unit can estimate the user's emotion and adjust the operation procedure of the interface based on the estimated user's emotion. The interface unit, for example, estimates the user's emotion and adjusts the operation procedure of the interface based on the estimated user's emotion. For example, when the user is nervous, the interface unit provides a simple and intuitive operation procedure. Also, when the user is relaxed, the interface unit can provide detailed operation procedures. Furthermore, when the user is excited, the interface unit can provide visually stimulating operation procedures. In this way, by adjusting the operation procedure of the interface according to the user's emotion, a more appropriate operation procedure can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0111] The interface unit can select the optimal display method in consideration of the device information of the user when displaying the interface. For example, the interface unit selects the optimal display method in consideration of the device information of the user when displaying the interface. For example, when the user is using a smartphone, the interface unit provides a display method that matches the screen size. Also, when the user is using a tablet, the interface unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the interface unit can also provide a simple and highly visible display method. This makes it possible to provide the optimal display method by considering the device information of the user. A part or all of the above-mentioned processing in the interface unit may be performed, for example, using AI, or may be performed without using AI.

[0112] The prediction unit can estimate the user's emotion and adjust the prediction method based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion and adjusts the prediction method based on the estimated user's emotion. For example, if the user is nervous, the prediction unit provides a simple and highly visible prediction result. In addition, if the user is relaxed, the prediction unit can also provide a detailed prediction result. Furthermore, if the user is excited, the prediction unit can also provide a visually stimulating prediction result. In this way, by adjusting the prediction method according to the user's emotion, a more appropriate prediction result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0113] The prediction unit can optimize the prediction algorithm by referring to past data at the time of prediction. The prediction unit optimizes the prediction algorithm by referring to past data at the time of prediction, for example. For example, the prediction unit selects an optimal prediction algorithm based on past data. The prediction unit can also analyze past data and select the most effective prediction algorithm under specific conditions. Furthermore, the prediction unit can dynamically adjust the prediction algorithm by referring to past data. This allows the prediction algorithm to be optimized by referring to past data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0114] The prediction unit can apply different prediction methods depending on the category of data at the time of prediction. The prediction unit applies different prediction methods depending on the category of data at the time of prediction, for example. For example, the prediction unit applies a weather forecasting method to weather data. Also, it can apply an earthquake forecasting method to earthquake data. Furthermore, it can apply a flood forecasting method to flood data. In this way, prediction accuracy is improved by applying an appropriate forecasting method depending on the category of data. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

[0115] The prediction unit can estimate the user's emotion and determine the priority of predictions based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion and determines the priority of predictions based on the estimated user's emotion. For example, if the user is nervous, the prediction unit can provide a prediction with high urgency with priority. In addition, if the user is relaxed, the prediction unit can also provide a general prediction with priority. Furthermore, if the user is excited, the prediction unit can also provide a prediction related to a specific field of interest with priority. In this way, by determining the priority of predictions according to the user's emotion, more appropriate prediction results can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0116] The prediction unit can weight the prediction based on the collection time of the data at the time of prediction. The prediction unit, for example, weights the prediction based on the collection time of the data at the time of prediction. For example, the prediction unit makes a prediction by placing emphasis on the latest data. The prediction unit can also make a prediction by placing emphasis on the latest data while referring to past data. Furthermore, the prediction unit can also make a prediction by placing emphasis on data collected during a specific period. As a result, prediction accuracy is improved by weighting the prediction based on the collection time of the data. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI.

[0117] The prediction unit can improve the accuracy of the prediction by referring to the related market data at the time of prediction. The prediction unit can improve the accuracy of the prediction by referring to the related market data at the time of prediction, for example. For example, the prediction unit improves the accuracy of the prediction based on the related market data. The prediction unit can also analyze the related market data and improve the accuracy of the prediction under specific conditions. Furthermore, the prediction unit can dynamically adjust the prediction algorithm by referring to the related market data. Thereby, the accuracy of the prediction can be improved by referring to the related market data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0118] The evaluation unit can estimate the user's emotion and adjust the evaluation method based on the estimated user's emotion. The evaluation unit, for example, estimates the user's emotion and adjusts the evaluation method based on the estimated user's emotion. For example, when the user is nervous, the evaluation unit provides a simple and highly visible evaluation result. When the user is relaxed, the evaluation unit can also provide a detailed evaluation result. Furthermore, when the user is excited, the evaluation unit can also provide a visually stimulating evaluation result. In this way, by adjusting the evaluation method according to the user's emotion, a more appropriate evaluation result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0119] The evaluation unit can optimize the evaluation algorithm by referring to past data during evaluation. The evaluation unit can optimize the evaluation algorithm by referring to past data during evaluation, for example. For example, the evaluation unit selects an optimal evaluation algorithm based on past data. The evaluation unit can also analyze past data and select an evaluation algorithm that is most effective under specific conditions. Furthermore, the evaluation unit can dynamically adjust the evaluation algorithm by referring to past data. In this way, the evaluation algorithm can be optimized by referring to past data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI.

[0120] The evaluation unit can apply different evaluation methods depending on the category of data during evaluation. The evaluation unit, for example, applies different evaluation methods depending on the category of data during evaluation. For example, the evaluation unit applies a meteorological evaluation method to meteorological data. Also, the evaluation unit can apply an earthquake evaluation method to earthquake data. Furthermore, the evaluation unit can apply a flood evaluation method to flood data. In this way, the evaluation accuracy is improved by applying an appropriate evaluation method depending on the category of data. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI.

[0121] The evaluation unit can estimate the user's emotion and determine the priority of the evaluation based on the estimated user's emotion. The evaluation unit, for example, estimates the user's emotion and determines the priority of the evaluation based on the estimated user's emotion. For example, if the user is nervous, the evaluation unit can provide a high urgency evaluation with priority. In addition, if the user is relaxed, the evaluation unit can also provide a general evaluation with priority. Furthermore, if the user is excited, the evaluation unit can also provide an evaluation related to a specific field of interest with priority. In this way, by determining the priority of the evaluation according to the user's emotion, a more appropriate evaluation result can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0122] The evaluation unit can weight the evaluation based on the collection time of the data at the time of evaluation. The evaluation unit, for example, weights the evaluation based on the collection time of the data at the time of evaluation. For example, the evaluation unit performs the evaluation by placing emphasis on the latest data. The evaluation unit can also perform the evaluation by placing emphasis on the latest data while referring to past data. Furthermore, the evaluation unit can also perform the evaluation by placing emphasis on data collected in a specific period. Thereby, the evaluation accuracy is improved by weighting the evaluation based on the collection time of the data. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI.

[0123] The evaluation unit can improve the accuracy of the evaluation by referring to the related market data at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to the related market data at the time of evaluation, for example. For example, the evaluation unit improves the accuracy of the evaluation based on the related market data. The evaluation unit can also analyze the related market data and improve the accuracy of the evaluation under specific conditions. Furthermore, the evaluation unit can dynamically adjust the evaluation algorithm by referring to the related market data. Thereby, the accuracy of the evaluation can be improved by referring to the related market data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The Metaverse Earth system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and transmits it to the analysis unit. The analysis unit can analyze these data and evaluate the user's health condition. For example, if the heart rate is abnormally high, the analysis unit can generate a notification encouraging the user to rest. Also, if the blood pressure is high, the analysis unit can generate a notification encouraging the user to see a doctor. Furthermore, if the body temperature is high, the analysis unit can generate a notification encouraging the user to drink water. This makes it possible to monitor the user's health condition in real time and provide appropriate advice.

[0126] The Metaverse Earth system may further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit collects the user's past behavioral data and transmits it to the analysis unit. The analysis unit can analyze these data and identify the user's behavioral patterns. For example, if the user is often in a specific location at a specific time, the analysis unit can provide information related to that time with priority. Also, if the user frequently participates in a specific event, the analysis unit can provide information related to that event. Furthermore, if the user has a specific interest, the analysis unit can provide information related to that interest. This makes it possible to provide more personalized information by analyzing the user's behavioral history.

[0127] The Metaverse Earth system may further include an emotion interaction unit that estimates the user's emotion and adjusts the interaction based on the estimated emotion. The emotion interaction unit estimates the emotion from the user's facial expression and voice and transmits it to the analysis unit. The analysis unit can analyze these data and evaluate the user's emotional state. For example, if the user is stressed, the analysis unit can provide relaxing content. Also, if the user is excited, the analysis unit can provide energetic content. Furthermore, if the user is sad, the analysis unit can provide comforting content. This allows for a better user experience by providing interactions according to the user's emotion.

[0128] The Metaverse Earth system can further include a learning analysis unit that analyzes the user's learning history. The learning analysis unit collects the user's past learning data and transmits it to the analysis unit. The analysis unit can analyze these data and identify the user's learning patterns. For example, if the user is interested in a particular field, the analysis unit can provide information related to that field preferentially. Also, if the user prefers a particular learning method, the analysis unit can provide learning content based on that method. Furthermore, if the user often studies during a particular time period, the analysis unit can send learning notifications during that time period. This makes it possible to provide more effective learning support by analyzing the user's learning history.

[0129] The Metaverse Earth system may further include a notification adjustment unit that estimates the user's emotions and adjusts the content of the notification based on the estimated emotions. The notification adjustment unit estimates the user's emotions and transmits them to the analysis unit. The analysis unit can analyze these data and generate notification content according to the user's emotional state. For example, if the user is nervous, the analysis unit generates a simple and highly visible notification. Also, if the user is relaxed, the analysis unit can generate a detailed notification. Furthermore, if the user is excited, the analysis unit can generate a visually stimulating notification. This makes it possible to provide more appropriate information by providing notification content according to the user's emotions.

[0130] The Metaverse Earth system can further include a purchase analysis unit that analyzes the user's purchase history. The purchase analysis unit collects the user's past purchase data and transmits it to the analysis unit. The analysis unit can analyze these data and identify the user's purchase patterns. For example, if the user likes a particular brand, the analysis unit can provide information related to that brand preferentially. Also, if the user frequently purchases products of a particular category, the analysis unit can provide information related to that category. Furthermore, if the user often purchases a particular product at a particular time, the analysis unit can provide information related to that time. This makes it possible to provide more personalized information by analyzing the user's purchase history.

[0131] The Metaverse Earth system may further include a display adjustment unit that estimates the user's emotions and adjusts the display method of data based on the estimated emotions. The display adjustment unit estimates the user's emotions and transmits them to the analysis unit. The analysis unit can analyze these data and generate a display method according to the user's emotional state. For example, if the user is nervous, the analysis unit provides a display method with calm colors. Also, if the user is relaxed, the analysis unit can provide a display method with bright colors. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating display method. This makes it possible to provide more appropriate information by providing a display method according to the user's emotions.

[0132] The Metaverse Earth system may further include a social analysis unit that analyzes the user's social network. The social analysis unit collects the user's social network data and transmits it to the analysis unit. The analysis unit may analyze these data and identify the structure of the user's social network. For example, if the user belongs to a particular group, the analysis unit may provide information related to that group preferentially. Also, if the user frequently interacts with a particular person, the analysis unit may provide information related to that person. Furthermore, if the user participates in a particular event, the analysis unit may provide information related to that event. This allows for more relevant information to be provided by analyzing the user's social network.

[0133] The Metaverse Earth system may further include a priority determination unit that estimates the user's emotions and determines the priority of data based on the estimated emotions. The priority determination unit estimates the user's emotions and transmits them to the analysis unit. The analysis unit can analyze these data and determine the priority of data according to the user's emotional state. For example, if the user is nervous, the analysis unit can provide data with high urgency with priority. Also, if the user is relaxed, the analysis unit can provide general data with priority. Furthermore, if the user is excited, the analysis unit can provide data related to a specific field of interest with priority. This makes it possible to provide more appropriate information by determining the priority of data according to the user's emotions.

[0134] The Metaverse Earth system may further include a feedback collection unit that collects user feedback. The feedback collection unit collects user feedback data and transmits it to the analysis unit. The analysis unit can analyze these data and identify improvements to the system. For example, if a user is dissatisfied with a particular function, the analysis unit can generate a proposal to improve that function. Also, if a user gives a high rating to a particular function, the analysis unit can generate a proposal to enhance that function. Furthermore, if a user requests a new function, the analysis unit can generate a proposal to add that function. This allows for continuous improvement of the system by collecting user feedback.

[0135] The process flow of the second embodiment will be briefly described below.

[0136] Step 1: The collection unit collects earth observation data. The earth observation data includes, but is not limited to, satellite data, meteorological data, and earthquake data. For example, the collection unit acquires satellite data in real time and periodically collects meteorological data. The collection unit can also immediately acquire earthquake data when an earthquake occurs. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using AI to detect anomalies and recognize patterns. For example, the analysis unit analyzes meteorological data to generate a weather forecast. The analysis unit can also analyze earthquake data to predict the probability of an earthquake occurring. Step 3: The generation unit generates information based on the analysis results obtained by the analysis unit. The generation unit generates information in real time according to a user request, for example, using a generation AI. The generation unit generates disaster information, for example, and provides it to the user. The generation unit can also generate risk assessment information for insurance companies. Step 4: The providing unit provides the information generated by the generating unit. The providing unit provides the information using, for example, AI and provides feedback to the user. The providing unit provides the information through, for example, a web application. The providing unit can also provide the information through a mobile application.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0139] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, interface unit, prediction unit, and evaluation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects earth observation data using the camera 42 and a sensor of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates information based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated information to the user. The interface unit is realized, for example, by the control unit 46A of the smart device 14, and provides an interface that the user can intuitively operate. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts disaster information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs risk evaluation for insurance companies. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, interface unit, prediction unit, and evaluation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects earth observation data using the camera 42 and a sensor of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates information based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated information to the user. The interface unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides an interface that the user can intuitively operate. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts disaster information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs risk assessment for insurance companies. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0159] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, interface unit, prediction unit, and evaluation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects earth observation data using the camera 42 and sensors of the headset type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates information based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated information to the user. The interface unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides an interface that the user can intuitively operate. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts disaster information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs risk evaluation for the insurance company. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0184] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0188] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, interface unit, prediction unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects earth observation data using the camera 42 and sensors of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates information based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated information to the user. The interface unit is realized, for example, by the control unit 46A of the robot 414, and provides an interface that the user can intuitively operate. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts disaster information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs risk evaluation for insurance companies. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0191] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0195] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] In addition, 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 upon request from the data processing device 12.

[0200] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0202] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0206] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0208] (Appendix 1) a collection unit for collecting earth observation data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates information based on the analysis result obtained by the analysis unit; a providing unit that provides the information generated by the generating unit; Equipped A system characterized by: (Appendix 2) An interface unit that provides a user interface is provided. 2. The system of claim 1, (Appendix 3) Equipped with a prediction unit that predicts disaster information 2. The system of claim 1, (Appendix 4) Equipped with an evaluation department that performs risk assessments for insurance companies 2. The system of claim 1, (Appendix 5) The collecting unit includes: Collect at least one of weather data, earthquake data, and flood data. 2. The system of claim 1, (Appendix 6) The generation unit is Generate information in real time based on user requests 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze past data collection history and select the optimal collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filter data as it is collected based on the user's current interests 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collection of relevant data by taking geographic location into account 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, analyze social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The generation unit is Estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The generation unit is At generation time, adjust the granularity of the generation based on the importance of the data 2. The system of claim 1, (Appendix 21) The generation unit is During generation, different generation algorithms are applied depending on the category of data 2. The system of claim 1, (Appendix 22) The generation unit is The user's emotions are estimated, and the length of the generated information is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The generation unit is At generation time, prioritize generation based on when the data was collected 2. The system of claim 1, (Appendix 24) The generation unit is At generation time, adjust the generation order based on data relevance 2. The system of claim 1, (Appendix 25) The providing unit is Estimate the user's emotions and adjust the way information is provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 26) The providing unit is When providing the service, the optimal method of providing the service is selected by referring to the user's past usage history. 2. The system of claim 1, (Appendix 27) The providing unit is Customize information based on your current interests when provided 2. The system of claim 1, (Appendix 28) The providing unit is Estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, the most appropriate information is provided taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The providing unit is When provided, analyze your social media activity to provide you with relevant information 2. The system of claim 1, (Appendix 31) The interface unit includes: Estimating a user's emotions and adjusting the interface display method based on the estimated user's emotions 3. The system of claim 2, (Appendix 32) The interface unit includes: When displaying an interface, the optimal display method is selected by referring to the user's past operation history. 3. The system of claim 2, (Appendix 33) The interface unit includes: Estimating a user's emotions and adjusting the operation procedure of an interface based on the estimated user's emotions 3. The system of claim 2, (Appendix 34) The interface unit includes: When displaying the interface, the optimal display method is selected taking into account the user's device information. 3. The system of claim 2, (Appendix 35) The prediction unit is Infer user sentiment and adjust prediction methods based on the inferred user sentiment 4. The system of claim 3. (Appendix 36) The prediction unit is When making predictions, look at past data to optimize the prediction algorithm 4. The system of claim 3. (Appendix 37) The prediction unit is When forecasting, apply different forecasting methods depending on the category of data 4. The system of claim 3. (Appendix 38) The prediction unit is Infer user sentiment and prioritize predictions based on the inferred user sentiment 4. The system of claim 3. (Appendix 39) The prediction unit is When making a forecast, weight the forecast based on when the data was collected 4. The system of claim 3. (Appendix 40) The prediction unit is When forecasting, refer to relevant market data to improve the accuracy of your forecasts 4. The system of claim 3. (Appendix 41) The evaluation unit is Inferring user emotions and adjusting the evaluation method based on the estimated user emotions 5. The system of claim 4. (Appendix 42) The evaluation unit is During evaluation, historical data is referenced to optimize the evaluation algorithm 5. The system of claim 4. (Appendix 43) The evaluation unit is During evaluation, different evaluation methods are applied depending on the category of data 5. The system of claim 4. (Appendix 44) The evaluation unit is Estimate user sentiment and prioritize ratings based on the estimated user sentiment 5. The system of claim 4. (Appendix 45) The evaluation unit is During evaluation, weighting of evaluations will be based on when the data was collected 5. The system of claim 4. (Appendix 46) The evaluation unit is During valuation, refer to relevant market data to improve valuation accuracy 5. The system of claim 4. [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting earth observation data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates information based on the analysis result obtained by the analysis unit; a providing unit that provides the information generated by the generating unit; Equipped A system characterized by:

2. An interface unit that provides a user interface is provided.

2. The system of claim 1 .

3. Equipped with a prediction unit that predicts disaster information 2. The system of claim 1 .

4. Equipped with an evaluation department that performs risk assessments for insurance companies 2. The system of claim 1 .

5. The collecting unit includes: Collect at least one of weather data, earthquake data, and flood data.

2. The system of claim 1 .

6. The generation unit is Generate information in real time based on user requests 2. The system of claim 1 .

7. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

8. The collecting unit includes: Analyze past data collection history and select the optimal collection method 2. The system of claim 1 .

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