System

Abstract

An object of a system according to an embodiment is to detect a sign of the occurrence of an earthquake in real time and promptly notify the sign.SOLUTION: A system includes an observation unit, an analysis unit, a detection unit, and a notification unit. The observation unit observes crustal movement or a change in groundwater level. The analysis unit analyzes the data collected by the observation unit in real time. The detector detects the presage of the earthquake occurrence based on the data analyzed by the analyzer. The notification unit notifies the prediction result obtained by the detection 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies are not sufficient in detecting earthquake warning signs in real time and providing prompt notification, so there is room for improvement.

[0005] The system according to the embodiment aims to detect signs of an earthquake in real time and to notify the user promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes an observation unit, an analysis unit, a detection unit, and a notification unit. The observation unit observes crustal movements or changes in groundwater levels. The analysis unit analyzes data collected by the observation unit in real time. The detection unit detects signs of an earthquake based on the data analyzed by the analysis unit. The notification unit notifies the user of the prediction results obtained by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect signs of an earthquake in real time and quickly notify the user. [Brief explanation 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. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An earthquake prediction system according to an embodiment of the present invention observes crustal movements and changes in groundwater levels in real time, and uses AI to analyze these movements and detect and notify signs of an upcoming earthquake. The earthquake prediction system installs sensors to observe crustal movements and changes in groundwater levels and collects data. Next, AI analyzes the collected data in real time to detect signs of an upcoming earthquake. The AI ​​analyzes the correlation between crustal movements and changes in groundwater levels and past earthquake data, and predicts areas where an earthquake is likely to occur and their scale. The prediction results are notified to relevant organizations and the general public as alerts. For example, an earthquake prediction system uses GPS sensors to observe crustal movements and water level gauges to observe changes in groundwater levels. Next, AI analyzes the collected data in real time to detect signs of an upcoming earthquake. For example, patterns of crustal movements and sudden changes in groundwater levels often signal the occurrence of an earthquake. AI analyzes these patterns and predicts areas where an earthquake is likely to occur and their scale. The prediction results are notified to relevant organizations and the general public as alerts. For example, alerts indicating areas with a high probability of earthquake occurrence and their scale are sent via smartphone apps, email, social media, etc. This allows relevant organizations and the general public to prepare for earthquakes and minimize damage. In this way, earthquake prediction systems can minimize damage caused by earthquakes and contribute to society. This allows earthquake prediction systems to detect signs of an impending earthquake early and issue evacuation orders and disaster prevention measures quickly. In addition, by predicting areas with a high probability of earthquake occurrence and their scale, relevant organizations and the general public can take appropriate measures. This minimizes damage caused by earthquakes and improves the safety of society as a whole.

[0029] An earthquake prediction system according to an embodiment includes an observation unit, an analysis unit, a detection unit, and a notification unit. The observation unit observes crustal movements or changes in groundwater levels. The observation unit uses, for example, a GPS sensor for observing crustal movements. The observation unit can also use a water level meter for observing changes in the groundwater level. For example, the observation unit can install multiple GPS sensors to observe crustal movements with high accuracy. The observation unit can also install a water level meter to observe changes in the groundwater level in real time. The analysis unit analyzes data collected by the observation unit in real time. The analysis unit analyzes, for example, patterns of crustal movements. The analysis unit can also analyze sudden changes in the groundwater level. For example, the analysis unit analyzes patterns of crustal movements to detect signs of an earthquake. The analysis unit can also analyze sudden changes in the groundwater level to detect signs of an earthquake. The detection unit detects signs of an earthquake based on the data analyzed by the analysis unit. The detection unit, for example, predicts areas where an earthquake is likely to occur. The detection unit can also predict the magnitude of the earthquake. For example, the detection unit predicts areas where an earthquake is likely to occur and notifies relevant organizations. The detection unit can also predict the magnitude of the earthquake and notify relevant organizations. The notification unit notifies the prediction results obtained by the detection unit. The notification unit, for example, notifies an alert via a smartphone app. The notification unit can also notify an alert via email or social media. For example, the notification unit notifies an alert indicating areas where an earthquake is likely to occur and the magnitude of the earthquake via a smartphone app. The notification unit can also notify an alert via email or social media. As a result, the earthquake prediction system according to the embodiment can minimize damage caused by an earthquake by analyzing crustal movements and changes in groundwater levels in real time, detecting signs of an earthquake, and notifying the users.

[0030] The observation unit can be equipped with a GPS sensor for observing crustal movements or a water level meter for observing changes in the groundwater level. The observation unit, for example, uses a GPS sensor for observing crustal movements. The GPS sensor can observe crustal movements with high accuracy. The observation unit can also use a water level meter for observing changes in the groundwater level. The water level meter can observe changes in the groundwater level in real time. For example, the observation unit can be equipped with multiple GPS sensors for observing crustal movements with high accuracy. The observation unit can also be equipped with a water level meter for observing changes in the groundwater level in real time. This allows for accurate observation of crustal movements and changes in the groundwater level.

[0031] The analysis unit can analyze patterns of crustal movement and sudden changes in groundwater levels to detect signs of an earthquake. The analysis unit, for example, analyzes patterns of crustal movement. For example, it analyzes patterns of crustal movement to detect signs of an earthquake. The analysis unit can also analyze sudden changes in groundwater levels. For example, it analyzes sudden changes in groundwater levels to detect signs of an earthquake. This makes it possible to detect signs of an earthquake with high accuracy.

[0032] The detection unit can predict areas where an earthquake is likely to occur and its scale. The detection unit, for example, predicts areas where an earthquake is likely to occur. For example, it predicts areas where an earthquake is likely to occur and notifies relevant organizations. The detection unit can also predict the scale of an earthquake. For example, it predicts the scale of an earthquake and notifies relevant organizations. In this way, by predicting the area where an earthquake will occur and its scale, appropriate disaster prevention measures can be taken.

[0033] The notification unit can issue alerts via a smartphone app, email, or social media. The notification unit, for example, issues alerts via a smartphone app. For example, an alert indicating the area where an earthquake is likely to occur and its scale can be issued via a smartphone app. The notification unit can also issue alerts via email or social media. For example, an alert can be issued via email or social media. This makes it possible to quickly notify relevant organizations and the general public of signs of an earthquake.

[0034] The analysis unit can analyze the correlation with past earthquake data. The analysis unit, for example, analyzes the correlation with past earthquake data. For example, the analysis unit analyzes the correlation with past earthquake data to detect signs of an earthquake. In this way, by analyzing the correlation with past earthquake data, signs of an earthquake can be detected more accurately.

[0035] The observation unit can analyze past earthquake data to determine the installation locations of sensors for observing crustal movements and changes in groundwater levels. For example, the observation unit can concentrate sensors in areas where earthquakes occur frequently based on past earthquake data. The observation unit can also install sensors over a wide area, taking into account the scale and range of impact of earthquakes. The observation unit can also install sensors in areas where crustal movements and changes in groundwater levels, which are precursors to earthquakes, are significant. In this way, by analyzing past earthquake data, it is possible to optimize the installation locations of sensors and improve observation accuracy.

[0036] The observation unit can be added with a function to automatically detect abnormal values ​​and perform re-observation when acquiring observation data. For example, if an abnormal value is detected in the observation data, the observation unit acquires the data again and checks for the abnormal value. Furthermore, if abnormal values ​​are detected continuously, the observation unit can suspect a sensor failure and perform maintenance. Furthermore, if the abnormal value is temporary, the observation unit can also perform re-observation to ensure the reliability of the data. In this way, by automatically detecting abnormal values ​​and performing re-observation, the reliability of the data can be ensured.

[0037] The observation unit can correct the data when acquiring observation data, taking into account meteorological conditions and seasonal variations. The observation unit corrects data variations due to meteorological conditions (rain, snow, wind, etc.), for example, to provide accurate observation data. The observation unit can also correct data variations due to seasonal variations (temperature, humidity, etc.) to ensure stable data collection. The observation unit can also monitor meteorological conditions and seasonal variations in real time and automatically correct data. This allows for data correction taking into account meteorological conditions and seasonal variations, making it possible to provide accurate observation data.

[0038] When acquiring observation data, the observation unit can combine different sensors taking into account geographical characteristics. For example, the observation unit can be installed by combining a GPS sensor to observe crustal movements and a water level gauge to observe groundwater levels. The observation unit can also combine multiple sensors to simultaneously observe crustal movements and changes in groundwater levels, which are precursors to earthquakes. The observation unit can also optimally position sensors depending on the geographical characteristics (mountainous areas, plains, etc.). In this way, by combining different sensors taking into account geographical characteristics, observation accuracy can be improved.

[0039] When acquiring observation data, the observation unit can simultaneously acquire environmental data other than crustal movements and changes in groundwater levels. For example, the observation unit simultaneously acquires temperature and humidity data in addition to crustal movements and changes in groundwater levels, and performs a comprehensive analysis. Furthermore, by acquiring environmental data (temperature, humidity, etc.), the observation unit can also perform a more detailed analysis of patterns that are precursors to the occurrence of an earthquake. Furthermore, the observation unit can analyze the correlation between crustal movements, changes in groundwater levels, and environmental data, and detect signs of an earthquake. In this way, simultaneous acquisition of environmental data other than crustal movements and changes in groundwater levels makes comprehensive analysis possible.

[0040] The observation unit can be added with a function to automatically detect and notify sensor failures when acquiring observation data. For example, if the sensor data indicates an abnormal value, the observation unit detects the possibility of a failure and notifies the system. Furthermore, if the sensor data indicates abnormal values ​​consecutively, the observation unit can increase the possibility of a failure and notify the system to perform maintenance. Furthermore, if the sensor data does not return to a normal value, the observation unit can determine that a failure has occurred and notify the system. This allows for rapid maintenance by automatically detecting and notifying sensor failures.

[0041] During analysis, the analysis unit can apply a highly accurate algorithm to detect subtle patterns in crustal movements and changes in groundwater levels. For example, the analysis unit applies a highly accurate algorithm to detect subtle patterns in crustal movements, thereby improving the analysis accuracy. The analysis unit can also apply a highly accurate algorithm to detect sudden changes in groundwater levels, thereby improving the analysis accuracy. The analysis unit can also detect subtle patterns in crustal movements and changes in groundwater levels in real time, thereby improving the analysis accuracy. In this way, the application of a highly accurate algorithm can improve the analysis accuracy.

[0042] The analysis unit can add a function to automatically detect abnormal values ​​during analysis and exclude them from the analysis results. For example, if the analysis data contains abnormal values, the analysis unit automatically detects them and excludes them from the analysis results. Furthermore, if abnormal values ​​are detected continuously, the analysis unit can suspect a sensor malfunction and exclude them from the analysis results. Furthermore, if the abnormal values ​​are temporary, the analysis unit can re-analyze the data to ensure the reliability of the data. In this way, the reliability of the data can be ensured by automatically detecting abnormal values ​​and excluding them from the analysis results.

[0043] During analysis, the analysis unit updates the correlation with past earthquake data in real time, thereby improving the accuracy of the analysis. For example, the analysis unit updates the correlation with past earthquake data in real time, thereby improving the accuracy of the analysis. The analysis unit can also analyze crustal movements and changes in groundwater levels, which are precursors to the occurrence of an earthquake, in real time, and update the correlation. The analysis unit can also analyze the correlation with past earthquake data and detect precursors to the occurrence of an earthquake in real time. In this way, by updating the correlation with past earthquake data in real time, the accuracy of the analysis can be improved.

[0044] During the analysis, the analysis unit can also include environmental data other than crustal movement and changes in groundwater levels. For example, the analysis unit can include temperature and humidity data in addition to crustal movement and changes in groundwater levels, to perform a comprehensive analysis. Furthermore, by including environmental data (temperature, humidity, etc.) in the analysis, the analysis unit can also perform a more detailed analysis of patterns that are precursors to the occurrence of an earthquake. Furthermore, the analysis unit can analyze the correlation between crustal movement, changes in groundwater levels, and environmental data, and detect signs of an earthquake. This makes it possible to perform a comprehensive analysis by including environmental data other than crustal movement and changes in groundwater levels in the analysis.

[0045] The analysis unit can improve the accuracy of the analysis by combining different analysis algorithms during the analysis. For example, the analysis unit can improve the accuracy of the analysis by combining an algorithm specialized in analyzing crustal movements with an algorithm specialized in analyzing groundwater levels. Furthermore, by combining different analysis algorithms, the analysis unit can also analyze patterns that are precursors to earthquake occurrence in more detail. Furthermore, the analysis unit can combine multiple analysis algorithms to perform a comprehensive analysis and improve the accuracy of the analysis. In this way, the analysis accuracy can be improved by combining different analysis algorithms.

[0046] During analysis, the analysis unit can provide a dashboard for visually displaying the analysis results. For example, the analysis unit provides a dashboard for visually displaying the analysis results, allowing the user to intuitively understand them. The analysis unit can also visually display patterns of crustal movement and groundwater level changes on the dashboard, making the analysis results easier to understand. The analysis unit can also visually display correlations with past earthquake data on the dashboard, making the analysis results easier to understand. In this way, visually displaying the analysis results allows the user to intuitively understand them.

[0047] At the time of detection, the detection unit can apply a high-precision model to more accurately predict the area where an earthquake is likely to occur and its scale. For example, the detection unit applies a high-precision model to more accurately predict the area where an earthquake is likely to occur. The detection unit can also apply a high-precision model to more accurately predict the scale of an earthquake. The detection unit can also apply a high-precision model to more accurately analyze crustal movements and changes in groundwater levels that are precursors to an earthquake. In this way, by applying a high-precision model, the area where an earthquake will occur and its scale can be more accurately predicted.

[0048] The detection unit can be added with a function to automatically detect abnormal values ​​and exclude them from the detection results during detection. For example, if an abnormal value is included in the detection data, the detection unit automatically detects it and excludes it from the detection results. Furthermore, if abnormal values ​​are detected continuously, the detection unit can suspect a sensor failure and exclude it from the detection results. Furthermore, if the abnormal value is temporary, the detection unit can perform re-detection to ensure the reliability of the data. In this way, by automatically detecting abnormal values ​​and excluding them from the detection results, the reliability of the data can be ensured.

[0049] The detection unit updates the correlation with past earthquake data in real time during detection, thereby improving detection accuracy. The detection unit, for example, updates the correlation with past earthquake data in real time, thereby improving detection accuracy. The detection unit can also analyze crustal movements and changes in groundwater levels, which are precursors to an earthquake, in real time, and update the correlation. The detection unit can also analyze the correlation with past earthquake data and detect precursors to an earthquake in real time. In this way, by updating the correlation with past earthquake data in real time, detection accuracy can be improved.

[0050] During detection, the detection unit can also include environmental data other than crustal movements and changes in groundwater levels in the detection. The detection unit, for example, performs a comprehensive analysis by including temperature and humidity data in addition to crustal movements and changes in groundwater levels. Furthermore, by including environmental data (temperature, humidity, etc.) in the detection, the detection unit can also analyze in more detail patterns that are precursors to the occurrence of an earthquake. Furthermore, the detection unit can analyze the correlation between crustal movements, changes in groundwater levels, and environmental data to detect precursors to the occurrence of an earthquake. In this way, comprehensive analysis becomes possible by including environmental data other than crustal movements and changes in groundwater levels in the detection.

[0051] The detection unit can improve detection accuracy by combining different detection algorithms. For example, the detection unit can improve detection accuracy by combining an algorithm specialized for detecting crustal movements with an algorithm specialized for detecting groundwater levels. Furthermore, by combining different detection algorithms, the detection unit can analyze patterns that are precursors to earthquake occurrence in more detail. Furthermore, the detection unit can combine multiple detection algorithms to perform comprehensive analysis and improve detection accuracy. In this way, detection accuracy can be improved by combining different detection algorithms.

[0052] The detection unit can provide a dashboard for visually displaying the detection results. The detection unit, for example, provides a dashboard for visually displaying the detection results, allowing the user to intuitively understand them. The detection unit can also visually display patterns of crustal movements and changes in groundwater levels on the dashboard, making the detection results easier to understand. The detection unit can also visually display correlations with past earthquake data on the dashboard, making the detection results easier to understand. In this way, visually displaying the detection results allows the user to intuitively understand them.

[0053] At the time of notification, the notification unit can apply a high-precision model to notify in more detail the area where an earthquake is likely to occur and its scale. For example, the notification unit applies a high-precision model to notify in more detail the area where an earthquake is likely to occur. The notification unit can also apply a high-precision model to notify more accurately the scale of the earthquake. The notification unit can also apply a high-precision model to analyze in more detail crustal movements and changes in groundwater levels that are precursors to the occurrence of an earthquake. In this way, by applying a high-precision model, it is possible to more accurately notify the area where an earthquake will occur and its scale.

[0054] The notification unit can be added with a function to automatically detect abnormal values ​​at the time of notification and exclude them from the notification content. For example, if an abnormal value is included in the notification data, the notification unit automatically detects it and excludes it from the notification content. Furthermore, if abnormal values ​​are detected continuously, the notification unit can suspect a sensor failure and exclude it from the notification content. Furthermore, if the abnormal value is temporary, the notification unit can re-notify to ensure the reliability of the data. In this way, by automatically detecting abnormal values ​​and excluding them from the notification content, the reliability of the data can be ensured.

[0055] The notification unit updates the correlation with past earthquake data in real time when notifying, thereby improving the accuracy of notifications. The notification unit, for example, updates the correlation with past earthquake data in real time, thereby improving the accuracy of notifications. The notification unit can also analyze crustal movements and changes in groundwater levels that are precursors to earthquake occurrences in real time, and update the correlation. The notification unit can also analyze the correlation with past earthquake data and notify of precursors to earthquake occurrences in real time. In this way, by updating the correlation with past earthquake data in real time, the accuracy of notifications can be improved.

[0056] When sending a notification, the notification unit can also include environmental data other than crustal movement and changes in groundwater levels in the notification. For example, the notification unit can include temperature and humidity data in addition to crustal movement and changes in groundwater levels in the notification, and perform a comprehensive analysis. Furthermore, by including environmental data (temperature, humidity, etc.) in the notification, the notification unit can also analyze in more detail patterns that are precursors to the occurrence of an earthquake. Furthermore, the notification unit can analyze the correlation between crustal movement, changes in groundwater levels, and environmental data, and notify of signs of an earthquake occurrence. In this way, by including environmental data other than crustal movement and changes in groundwater levels in the notification, a comprehensive analysis becomes possible.

[0057] The notification unit can improve the accuracy of notifications by combining different notification methods (for example, audio alerts and visual alerts). The notification unit can quickly convey important notification content by combining audio alerts and visual alerts, for example. The notification unit can also combine different notification methods to enable the user to intuitively understand. The notification unit can also combine audio alerts and visual alerts to improve the accuracy of notifications. In this way, the accuracy of notifications can be improved by combining different notification methods.

[0058] The notification unit can provide a dashboard for visually displaying the notification content. The notification unit, for example, provides a dashboard for visually displaying the notification content, allowing the user to intuitively understand it. The notification unit can also visually display patterns of crustal movement and groundwater level changes on the dashboard, making the notification content easier to understand. The notification unit can also visually display correlations with past earthquake data on the dashboard, making the notification content easier to understand. In this way, visually displaying the notification content allows the user to intuitively understand it.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] In addition to crustal movements and changes in groundwater levels, the observation unit can also observe geomagnetic fluctuations. For example, the observation unit is equipped with a magnetometer to observe geomagnetic fluctuations and detect geomagnetic fluctuations that are precursors to earthquakes. The observation unit can also analyze the correlation between geomagnetic fluctuations and crustal movements and changes in groundwater levels, enabling it to detect earthquake precursors with greater accuracy. In this way, by observing geomagnetic fluctuations, it is possible to detect earthquake precursors from multiple angles.

[0061] The analysis unit can include abnormal animal behavior data in the analysis in addition to crustal movements and changes in groundwater levels. For example, the analysis unit collects abnormal animal behavior data and analyzes patterns that are precursors to the occurrence of an earthquake. The analysis unit can also analyze the correlation between the abnormal animal behavior data and crustal movements and changes in groundwater levels to detect precursors to the occurrence of an earthquake. In this way, by including abnormal animal behavior data in the analysis, it is possible to detect precursors to the occurrence of an earthquake in more detail.

[0062] When predicting areas with a high probability of earthquake occurrence and their scale, the detection unit can also predict the extent of the impact after the earthquake occurs. For example, the detection unit predicts the risk of building collapse and tsunami occurrence after an earthquake and notifies relevant organizations. The detection unit can also predict the extent of impact on transportation networks after an earthquake, which can be used to secure evacuation routes. This allows more appropriate disaster prevention measures to be taken by predicting the extent of impact after an earthquake occurs.

[0063] The notification unit can provide a customized alert that takes into account the user's location information when notifying the user of an impending earthquake. For example, the notification unit can provide an alert that indicates areas where an earthquake is likely to occur and the magnitude of the earthquake based on the user's current location. The notification unit can also suggest an optimal evacuation route based on the user's location information. This allows for more effective evacuation behavior by providing a customized alert that takes into account the user's location information.

[0064] During analysis, the analysis unit can apply a high-precision algorithm to detect subtle patterns in crustal movements and changes in groundwater levels. For example, applying a high-precision algorithm to detect subtle patterns in crustal movements improves the accuracy of the analysis. The analysis unit can also apply a high-precision algorithm to detect sudden changes in groundwater levels, improving the accuracy of the analysis. The analysis unit can also detect subtle patterns in crustal movements and changes in groundwater levels in real time, improving the accuracy of the analysis. In this way, applying a high-precision algorithm can improve the accuracy of the analysis.

[0065] The detection unit can be added with a function to automatically detect abnormal values ​​and exclude them from the detection results during detection. For example, if an abnormal value is included in the detection data, it is automatically detected and excluded from the detection results. Furthermore, if abnormal values ​​are detected continuously, the detection unit can suspect a sensor failure and exclude the value from the detection results. Furthermore, if the abnormal value is temporary, the detection unit can perform re-detection to ensure the reliability of the data. In this way, by automatically detecting abnormal values ​​and excluding them from the detection results, the reliability of the data can be ensured.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The observation unit observes crustal movements or changes in groundwater levels. The observation unit uses, for example, a GPS sensor to observe crustal movements and a water level gauge to observe changes in groundwater levels. By installing multiple GPS sensors, crustal movements can be observed with high precision, and by installing water level gauges, changes in groundwater levels can be observed in real time. Step 2: The analysis unit analyzes the data collected by the observation unit in real time. The analysis unit analyzes patterns of crustal movement and sudden changes in groundwater levels to detect signs of an impending earthquake. Step 3: The detection unit detects signs of an earthquake based on the data analyzed by the analysis unit. The detection unit predicts areas with a high probability of an earthquake occurring and the magnitude of the earthquake, and notifies the relevant organizations. Step 4: The notification unit notifies the prediction results obtained by the detection unit. The notification unit sends alerts via smartphone apps, email, and social media, indicating the areas and scale of earthquakes that are likely to occur.

[0068] (Example 2) An earthquake prediction system according to an embodiment of the present invention observes crustal movements and changes in groundwater levels in real time, and uses AI to analyze these movements and detect and notify signs of an upcoming earthquake. The earthquake prediction system installs sensors to observe crustal movements and changes in groundwater levels and collects data. Next, AI analyzes the collected data in real time to detect signs of an upcoming earthquake. The AI ​​analyzes the correlation between crustal movements and changes in groundwater levels and past earthquake data, and predicts areas where an earthquake is likely to occur and their scale. The prediction results are notified to relevant organizations and the general public as alerts. For example, an earthquake prediction system uses GPS sensors to observe crustal movements and water level gauges to observe changes in groundwater levels. Next, AI analyzes the collected data in real time to detect signs of an upcoming earthquake. For example, patterns of crustal movements and sudden changes in groundwater levels often signal the occurrence of an earthquake. AI analyzes these patterns and predicts areas where an earthquake is likely to occur and their scale. The prediction results are notified to relevant organizations and the general public as alerts. For example, alerts indicating areas with a high probability of earthquake occurrence and their scale are sent via smartphone apps, email, social media, etc. This allows relevant organizations and the general public to prepare for earthquakes and minimize damage. In this way, earthquake prediction systems can minimize damage caused by earthquakes and contribute to society. This allows earthquake prediction systems to detect signs of an impending earthquake early and issue evacuation orders and disaster prevention measures quickly. In addition, by predicting areas with a high probability of earthquake occurrence and their scale, relevant organizations and the general public can take appropriate measures. This minimizes damage caused by earthquakes and improves the safety of society as a whole.

[0069] An earthquake prediction system according to an embodiment includes an observation unit, an analysis unit, a detection unit, and a notification unit. The observation unit observes crustal movements or changes in groundwater levels. The observation unit uses, for example, a GPS sensor for observing crustal movements. The observation unit can also use a water level meter for observing changes in the groundwater level. For example, the observation unit can install multiple GPS sensors to observe crustal movements with high accuracy. The observation unit can also install a water level meter to observe changes in the groundwater level in real time. The analysis unit analyzes data collected by the observation unit in real time. The analysis unit analyzes, for example, patterns of crustal movements. The analysis unit can also analyze sudden changes in the groundwater level. For example, the analysis unit analyzes patterns of crustal movements to detect signs of an earthquake. The analysis unit can also analyze sudden changes in the groundwater level to detect signs of an earthquake. The detection unit detects signs of an earthquake based on the data analyzed by the analysis unit. The detection unit, for example, predicts areas where an earthquake is likely to occur. The detection unit can also predict the magnitude of the earthquake. For example, the detection unit predicts areas where an earthquake is likely to occur and notifies relevant organizations. The detection unit can also predict the magnitude of the earthquake and notify relevant organizations. The notification unit notifies the prediction results obtained by the detection unit. The notification unit, for example, notifies an alert via a smartphone app. The notification unit can also notify an alert via email or social media. For example, the notification unit notifies an alert indicating areas where an earthquake is likely to occur and the magnitude of the earthquake via a smartphone app. The notification unit can also notify an alert via email or social media. As a result, the earthquake prediction system according to the embodiment can minimize damage caused by an earthquake by analyzing crustal movements and changes in groundwater levels in real time, detecting signs of an earthquake, and notifying the users.

[0070] The observation unit can be equipped with a GPS sensor for observing crustal movements or a water level meter for observing changes in the groundwater level. The observation unit, for example, uses a GPS sensor for observing crustal movements. The GPS sensor can observe crustal movements with high accuracy. The observation unit can also use a water level meter for observing changes in the groundwater level. The water level meter can observe changes in the groundwater level in real time. For example, the observation unit can be equipped with multiple GPS sensors for observing crustal movements with high accuracy. The observation unit can also be equipped with a water level meter for observing changes in the groundwater level in real time. This allows for accurate observation of crustal movements and changes in the groundwater level.

[0071] The analysis unit can analyze patterns of crustal movement and sudden changes in groundwater levels to detect signs of an earthquake. The analysis unit, for example, analyzes patterns of crustal movement. For example, it analyzes patterns of crustal movement to detect signs of an earthquake. The analysis unit can also analyze sudden changes in groundwater levels. For example, it analyzes sudden changes in groundwater levels to detect signs of an earthquake. This makes it possible to detect signs of an earthquake with high accuracy.

[0072] The detection unit can predict areas where an earthquake is likely to occur and its scale. The detection unit, for example, predicts areas where an earthquake is likely to occur. For example, it predicts areas where an earthquake is likely to occur and notifies relevant organizations. The detection unit can also predict the scale of an earthquake. For example, it predicts the scale of an earthquake and notifies relevant organizations. In this way, by predicting the area where an earthquake will occur and its scale, appropriate disaster prevention measures can be taken.

[0073] The notification unit can issue alerts via a smartphone app, email, or social media. The notification unit, for example, issues alerts via a smartphone app. For example, an alert indicating the area where an earthquake is likely to occur and its scale can be issued via a smartphone app. The notification unit can also issue alerts via email or social media. For example, an alert can be issued via email or social media. This makes it possible to quickly notify relevant organizations and the general public of signs of an earthquake.

[0074] The analysis unit can analyze the correlation with past earthquake data. The analysis unit, for example, analyzes the correlation with past earthquake data. For example, the analysis unit analyzes the correlation with past earthquake data to detect signs of an earthquake. In this way, by analyzing the correlation with past earthquake data, signs of an earthquake can be detected more accurately.

[0075] The observation unit can estimate the user's emotions and adjust the timing of acquiring the observation data based on the estimated user emotions. For example, if the user is nervous, the observation unit can increase the frequency of acquiring the observation data and update the data in real time. Furthermore, if the user is relaxed, the observation unit can maintain the normal frequency of acquiring the observation data to ensure stable data collection. Furthermore, if the user is in a hurry, the observation unit can prioritize acquiring only important data and perform quick data analysis. This allows for more appropriate data collection by adjusting the timing of acquiring the observation data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The observation unit can analyze past earthquake data to determine the installation locations of sensors for observing crustal movements and changes in groundwater levels. For example, the observation unit can concentrate sensors in areas where earthquakes occur frequently based on past earthquake data. The observation unit can also install sensors over a wide area, taking into account the scale and range of impact of earthquakes. The observation unit can also install sensors in areas where crustal movements and changes in groundwater levels, which are precursors to earthquakes, are significant. In this way, by analyzing past earthquake data, it is possible to optimize the installation locations of sensors and improve observation accuracy.

[0077] The observation unit can be added with a function to automatically detect abnormal values ​​and perform re-observation when acquiring observation data. For example, if an abnormal value is detected in the observation data, the observation unit acquires the data again and checks for the abnormal value. Furthermore, if abnormal values ​​are detected continuously, the observation unit can suspect a sensor failure and perform maintenance. Furthermore, if the abnormal value is temporary, the observation unit can also perform re-observation to ensure the reliability of the data. In this way, by automatically detecting abnormal values ​​and performing re-observation, the reliability of the data can be ensured.

[0078] The observation unit can correct the data when acquiring observation data, taking into account meteorological conditions and seasonal variations. The observation unit corrects data variations due to meteorological conditions (rain, snow, wind, etc.), for example, to provide accurate observation data. The observation unit can also correct data variations due to seasonal variations (temperature, humidity, etc.) to ensure stable data collection. The observation unit can also monitor meteorological conditions and seasonal variations in real time and automatically correct data. This allows for data correction taking into account meteorological conditions and seasonal variations, making it possible to provide accurate observation data.

[0079] The observation unit can estimate the user's emotions and prioritize the observation data based on the estimated user emotions. For example, if the user is nervous, the observation unit can prioritize acquiring important observation data and analyze it quickly. Furthermore, if the user is relaxed, the observation unit can acquire normal observation data and perform stable data collection. Furthermore, if the user is in a hurry, the observation unit can prioritize acquiring only important data and perform quick data analysis. Thus, by prioritizing the observation data according to the user's emotions, important data can be quickly acquired. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When acquiring observation data, the observation unit can combine different sensors taking into account geographical characteristics. For example, the observation unit can be installed by combining a GPS sensor to observe crustal movements and a water level gauge to observe groundwater levels. The observation unit can also combine multiple sensors to simultaneously observe crustal movements and changes in groundwater levels, which are precursors to earthquakes. The observation unit can also optimally position sensors depending on the geographical characteristics (mountainous areas, plains, etc.). In this way, by combining different sensors taking into account geographical characteristics, observation accuracy can be improved.

[0081] When acquiring observation data, the observation unit can simultaneously acquire environmental data other than crustal movements and changes in groundwater levels. For example, the observation unit simultaneously acquires temperature and humidity data in addition to crustal movements and changes in groundwater levels, and performs a comprehensive analysis. Furthermore, by acquiring environmental data (temperature, humidity, etc.), the observation unit can also perform a more detailed analysis of patterns that are precursors to the occurrence of an earthquake. Furthermore, the observation unit can analyze the correlation between crustal movements, changes in groundwater levels, and environmental data, and detect signs of an earthquake. In this way, simultaneous acquisition of environmental data other than crustal movements and changes in groundwater levels makes comprehensive analysis possible.

[0082] The observation unit can be added with a function to automatically detect and notify sensor failures when acquiring observation data. For example, if the sensor data indicates an abnormal value, the observation unit detects the possibility of a failure and notifies the system. Furthermore, if the sensor data indicates abnormal values ​​consecutively, the observation unit can increase the possibility of a failure and notify the system to perform maintenance. Furthermore, if the sensor data does not return to a normal value, the observation unit can determine that a failure has occurred and notify the system. This allows for rapid maintenance by automatically detecting and notifying sensor failures.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] During analysis, the analysis unit can apply a highly accurate algorithm to detect subtle patterns in crustal movements and changes in groundwater levels. For example, the analysis unit applies a highly accurate algorithm to detect subtle patterns in crustal movements, thereby improving the analysis accuracy. The analysis unit can also apply a highly accurate algorithm to detect sudden changes in groundwater levels, thereby improving the analysis accuracy. The analysis unit can also detect subtle patterns in crustal movements and changes in groundwater levels in real time, thereby improving the analysis accuracy. In this way, the application of a highly accurate algorithm can improve the analysis accuracy.

[0085] The analysis unit can add a function to automatically detect abnormal values ​​during analysis and exclude them from the analysis results. For example, if the analysis data contains abnormal values, the analysis unit automatically detects them and excludes them from the analysis results. Furthermore, if abnormal values ​​are detected continuously, the analysis unit can suspect a sensor malfunction and exclude them from the analysis results. Furthermore, if the abnormal values ​​are temporary, the analysis unit can re-analyze the data to ensure the reliability of the data. In this way, the reliability of the data can be ensured by automatically detecting abnormal values ​​and excluding them from the analysis results.

[0086] During analysis, the analysis unit updates the correlation with past earthquake data in real time, thereby improving the accuracy of the analysis. For example, the analysis unit updates the correlation with past earthquake data in real time, thereby improving the accuracy of the analysis. The analysis unit can also analyze crustal movements and changes in groundwater levels, which are precursors to the occurrence of an earthquake, in real time, and update the correlation. The analysis unit can also analyze the correlation with past earthquake data and detect precursors to the occurrence of an earthquake in real time. In this way, by updating the correlation with past earthquake data in real time, the accuracy of the analysis can be improved.

[0087] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize and display important analysis results and respond quickly. Furthermore, if the user is relaxed, the analysis unit can display normal analysis results and provide stable data. Furthermore, if the user is in a hurry, the analysis unit can prioritize and display only important analysis results and respond quickly. Thus, by prioritizing the analysis results according to the user's emotions, important information can be provided quickly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] During the analysis, the analysis unit can also include environmental data other than crustal movement and changes in groundwater levels. For example, the analysis unit can include temperature and humidity data in addition to crustal movement and changes in groundwater levels, to perform a comprehensive analysis. Furthermore, by including environmental data (temperature, humidity, etc.) in the analysis, the analysis unit can also perform a more detailed analysis of patterns that are precursors to the occurrence of an earthquake. Furthermore, the analysis unit can analyze the correlation between crustal movement, changes in groundwater levels, and environmental data, and detect signs of an earthquake. This makes it possible to perform a comprehensive analysis by including environmental data other than crustal movement and changes in groundwater levels in the analysis.

[0089] The analysis unit can improve the accuracy of the analysis by combining different analysis algorithms during the analysis. For example, the analysis unit can improve the accuracy of the analysis by combining an algorithm specialized in analyzing crustal movements with an algorithm specialized in analyzing groundwater levels. Furthermore, by combining different analysis algorithms, the analysis unit can also analyze patterns that are precursors to earthquake occurrence in more detail. Furthermore, the analysis unit can combine multiple analysis algorithms to perform a comprehensive analysis and improve the accuracy of the analysis. In this way, the analysis accuracy can be improved by combining different analysis algorithms.

[0090] During analysis, the analysis unit can provide a dashboard for visually displaying the analysis results. For example, the analysis unit provides a dashboard for visually displaying the analysis results, allowing the user to intuitively understand them. The analysis unit can also visually display patterns of crustal movement and groundwater level changes on the dashboard, making the analysis results easier to understand. The analysis unit can also visually display correlations with past earthquake data on the dashboard, making the analysis results easier to understand. In this way, visually displaying the analysis results allows the user to intuitively understand them.

[0091] The detection unit can estimate the user's emotion and adjust the display method of the detection results based on the estimated user emotion. For example, if the user is nervous, the detection unit can provide a simple, highly visible display method. If the user is relaxed, the detection unit can also provide a display method including detailed information. If the user is in a hurry, the detection unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the detection results according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] At the time of detection, the detection unit can apply a high-precision model to more accurately predict the area where an earthquake is likely to occur and its scale. For example, the detection unit applies a high-precision model to more accurately predict the area where an earthquake is likely to occur. The detection unit can also apply a high-precision model to more accurately predict the scale of an earthquake. The detection unit can also apply a high-precision model to more accurately analyze crustal movements and changes in groundwater levels that are precursors to an earthquake. In this way, by applying a high-precision model, the area where an earthquake will occur and its scale can be more accurately predicted.

[0093] The detection unit can be added with a function to automatically detect abnormal values ​​and exclude them from the detection results during detection. For example, if an abnormal value is included in the detection data, the detection unit automatically detects it and excludes it from the detection results. Furthermore, if abnormal values ​​are detected continuously, the detection unit can suspect a sensor failure and exclude it from the detection results. Furthermore, if the abnormal value is temporary, the detection unit can perform re-detection to ensure the reliability of the data. In this way, by automatically detecting abnormal values ​​and excluding them from the detection results, the reliability of the data can be ensured.

[0094] The detection unit updates the correlation with past earthquake data in real time during detection, thereby improving detection accuracy. The detection unit, for example, updates the correlation with past earthquake data in real time, thereby improving detection accuracy. The detection unit can also analyze crustal movements and changes in groundwater levels, which are precursors to an earthquake, in real time, and update the correlation. The detection unit can also analyze the correlation with past earthquake data and detect precursors to an earthquake in real time. In this way, by updating the correlation with past earthquake data in real time, detection accuracy can be improved.

[0095] The detection unit can estimate the user's emotions and prioritize the detection results based on the estimated user emotions. For example, if the user is nervous, the detection unit can prioritize displaying important detection results and respond quickly. Furthermore, if the user is relaxed, the detection unit can display normal detection results and provide stable data. Furthermore, if the user is in a hurry, the detection unit can prioritize displaying only important detection results and respond quickly. Thus, by prioritizing the detection results according to the user's emotions, important information can be provided quickly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] During detection, the detection unit can also include environmental data other than crustal movements and changes in groundwater levels in the detection. The detection unit, for example, performs a comprehensive analysis by including temperature and humidity data in addition to crustal movements and changes in groundwater levels. Furthermore, by including environmental data (temperature, humidity, etc.) in the detection, the detection unit can also analyze in more detail patterns that are precursors to the occurrence of an earthquake. Furthermore, the detection unit can analyze the correlation between crustal movements, changes in groundwater levels, and environmental data to detect precursors to the occurrence of an earthquake. In this way, comprehensive analysis becomes possible by including environmental data other than crustal movements and changes in groundwater levels in the detection.

[0097] The detection unit can improve detection accuracy by combining different detection algorithms. For example, the detection unit can improve detection accuracy by combining an algorithm specialized for detecting crustal movements with an algorithm specialized for detecting groundwater levels. Furthermore, by combining different detection algorithms, the detection unit can analyze patterns that are precursors to earthquake occurrence in more detail. Furthermore, the detection unit can combine multiple detection algorithms to perform comprehensive analysis and improve detection accuracy. In this way, detection accuracy can be improved by combining different detection algorithms.

[0098] The detection unit can provide a dashboard for visually displaying the detection results. The detection unit, for example, provides a dashboard for visually displaying the detection results, allowing the user to intuitively understand them. The detection unit can also visually display patterns of crustal movements and changes in groundwater levels on the dashboard, making the detection results easier to understand. The detection unit can also visually display correlations with past earthquake data on the dashboard, making the detection results easier to understand. In this way, visually displaying the detection results allows the user to intuitively understand them.

[0099] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, if the user is nervous, the notification unit can provide a simple, highly visible notification method. If the user is relaxed, the notification unit can also provide a notification method that includes detailed information. If the user is in a hurry, the notification unit can also provide a notification method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] At the time of notification, the notification unit can apply a high-precision model to notify in more detail the area where an earthquake is likely to occur and its scale. For example, the notification unit applies a high-precision model to notify in more detail the area where an earthquake is likely to occur. The notification unit can also apply a high-precision model to notify more accurately the scale of the earthquake. The notification unit can also apply a high-precision model to analyze in more detail crustal movements and changes in groundwater levels that are precursors to the occurrence of an earthquake. In this way, by applying a high-precision model, it is possible to more accurately notify the area where an earthquake will occur and its scale.

[0101] The notification unit can be added with a function to automatically detect abnormal values ​​at the time of notification and exclude them from the notification content. For example, if an abnormal value is included in the notification data, the notification unit automatically detects it and excludes it from the notification content. Furthermore, if abnormal values ​​are detected continuously, the notification unit can suspect a sensor failure and exclude it from the notification content. Furthermore, if the abnormal value is temporary, the notification unit can re-notify to ensure the reliability of the data. In this way, by automatically detecting abnormal values ​​and excluding them from the notification content, the reliability of the data can be ensured.

[0102] The notification unit updates the correlation with past earthquake data in real time when notifying, thereby improving the accuracy of notifications. The notification unit, for example, updates the correlation with past earthquake data in real time, thereby improving the accuracy of notifications. The notification unit can also analyze crustal movements and changes in groundwater levels that are precursors to earthquake occurrences in real time, and update the correlation. The notification unit can also analyze the correlation with past earthquake data and notify of precursors to earthquake occurrences in real time. In this way, by updating the correlation with past earthquake data in real time, the accuracy of notifications can be improved.

[0103] The notification unit can estimate the user's emotions and prioritize notification content based on the estimated user emotions. For example, if the user is nervous, the notification unit can prioritize displaying important notification content and responding quickly. Furthermore, if the user is relaxed, the notification unit can display normal notification content and provide stable data. Furthermore, if the user is in a hurry, the notification unit can prioritize displaying only important notification content and responding quickly. Thus, by prioritizing notification content according to the user's emotions, important information can be provided quickly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] When sending a notification, the notification unit can also include environmental data other than crustal movement and changes in groundwater levels in the notification. For example, the notification unit can include temperature and humidity data in addition to crustal movement and changes in groundwater levels in the notification, and perform a comprehensive analysis. Furthermore, by including environmental data (temperature, humidity, etc.) in the notification, the notification unit can also analyze in more detail patterns that are precursors to the occurrence of an earthquake. Furthermore, the notification unit can analyze the correlation between crustal movement, changes in groundwater levels, and environmental data, and notify of signs of an earthquake occurrence. In this way, by including environmental data other than crustal movement and changes in groundwater levels in the notification, a comprehensive analysis becomes possible.

[0105] The notification unit can improve the accuracy of notifications by combining different notification methods (for example, audio alerts and visual alerts). The notification unit can quickly convey important notification content by combining audio alerts and visual alerts, for example. The notification unit can also combine different notification methods to enable the user to intuitively understand. The notification unit can also combine audio alerts and visual alerts to improve the accuracy of notifications. In this way, the accuracy of notifications can be improved by combining different notification methods.

[0106] The notification unit can provide a dashboard for visually displaying the notification content. The notification unit, for example, provides a dashboard for visually displaying the notification content, allowing the user to intuitively understand it. The notification unit can also visually display patterns of crustal movement and groundwater level changes on the dashboard, making the notification content easier to understand. The notification unit can also visually display correlations with past earthquake data on the dashboard, making the notification content easier to understand. In this way, visually displaying the notification content allows the user to intuitively understand it. === Hard Collateral 1-1 === Each of the multiple elements, including the observation unit, analysis unit, detection unit, and notification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the observation unit can observe crustal movements and changes in groundwater levels using the camera 42 and water level gauge of the smart device 14. The analysis unit analyzes data collected from the observation unit in real time using the specific processing unit 290 of the data processing device 12. The detection unit detects signs of an earthquake based on the data analyzed by the analysis unit, and predicts areas with a high probability of earthquake occurrence and their scale using the specific processing unit 290 of the data processing device 12. The notification unit can use the control unit 46A of the smart device 14 to notify the prediction results via a smartphone app, email, or social media. === Hard Collateral 1-2 === Each of the multiple elements including the observation unit, analysis unit, detection unit, and notification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the observation unit can observe crustal movements and changes in groundwater levels using the camera 42 and water level gauge of the smart glasses 214. The analysis unit analyzes data collected from the observation unit in real time using the specific processing unit 290 of the data processing device 12. The detection unit detects signs of an earthquake based on the data analyzed by the analysis unit, and predicts areas and scales where an earthquake is likely to occur using the specific processing unit 290 of the data processing device 12. The notification unit can notify prediction results via a smartphone app, email, or social media using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the observation unit, analysis unit, detection unit, and notification unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the observation unit can observe crustal movements and changes in groundwater levels using the camera 42 and water level gauge of the headset terminal 314. The analysis unit analyzes data collected from the observation unit in real time using the specific processing unit 290 of the data processing device 12. The detection unit detects signs of an earthquake based on the data analyzed by the analysis unit, and predicts areas with a high probability of earthquake occurrence and their scale using the specific processing unit 290 of the data processing device 12. The notification unit can use the control unit 46A of the headset terminal 314 to notify the prediction results via a smartphone app, email, or social media. === Hard Collateral 1-4 === Each of the multiple elements including the observation unit, analysis unit, detection unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the observation unit can observe crustal movements and changes in groundwater levels using the camera 42 and water level gauge of the robot 414. The analysis unit analyzes data collected from the observation unit in real time using the specific processing unit 290 of the data processing device 12. The detection unit detects signs of an earthquake based on the data analyzed by the analysis unit, and predicts areas with a high probability of earthquake occurrence and their scale using the specific processing unit 290 of the data processing device 12. The notification unit can use the control unit 46A of the robot 414 to notify the prediction results via a smartphone app, email, or social media.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] In addition to crustal movements and changes in groundwater levels, the observation unit can also observe geomagnetic fluctuations. For example, the observation unit is equipped with a magnetometer to observe geomagnetic fluctuations and detect geomagnetic fluctuations that are precursors to earthquakes. The observation unit can also analyze the correlation between geomagnetic fluctuations and crustal movements and changes in groundwater levels, enabling it to detect earthquake precursors with greater accuracy. In this way, by observing geomagnetic fluctuations, it is possible to detect earthquake precursors from multiple angles.

[0109] The analysis unit can include abnormal animal behavior data in the analysis in addition to crustal movements and changes in groundwater levels. For example, the analysis unit collects abnormal animal behavior data and analyzes patterns that are precursors to the occurrence of an earthquake. The analysis unit can also analyze the correlation between the abnormal animal behavior data and crustal movements and changes in groundwater levels to detect precursors to the occurrence of an earthquake. In this way, by including abnormal animal behavior data in the analysis, it is possible to detect precursors to the occurrence of an earthquake in more detail.

[0110] When predicting areas with a high probability of earthquake occurrence and their scale, the detection unit can also predict the extent of the impact after the earthquake occurs. For example, the detection unit predicts the risk of building collapse and tsunami occurrence after an earthquake and notifies relevant organizations. The detection unit can also predict the extent of impact on transportation networks after an earthquake, which can be used to secure evacuation routes. This allows more appropriate disaster prevention measures to be taken by predicting the extent of impact after an earthquake occurs.

[0111] The notification unit can provide a customized alert that takes into account the user's location information when notifying the user of an impending earthquake. For example, the notification unit can provide an alert that indicates areas where an earthquake is likely to occur and the magnitude of the earthquake based on the user's current location. The notification unit can also suggest an optimal evacuation route based on the user's location information. This allows for more effective evacuation behavior by providing a customized alert that takes into account the user's location information.

[0112] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate information to be provided by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The observation unit can estimate the user's emotions and adjust the timing of acquiring the observation data based on the estimated user emotions. For example, if the user is nervous, the observation unit can increase the frequency of acquiring the observation data and update the data in real time. Furthermore, if the user is relaxed, the observation unit can maintain the normal frequency of acquiring the observation data to ensure stable data collection. Furthermore, if the user is in a hurry, the observation unit can prioritize acquiring only important data and perform quick data analysis. This allows for more appropriate data collection by adjusting the timing of acquiring the observation data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] The detection unit can estimate the user's emotion and adjust the display method of the detection results based on the estimated user emotion. For example, if the user is nervous, a simple, highly visible display method is provided. The detection unit can also provide a display method including detailed information if the user is relaxed. The detection unit can also provide a display method that focuses on the main points if the user is in a hurry. This allows for more appropriate information provision by adjusting the display method of the detection results according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible notification method is provided. Furthermore, if the user is relaxed, the notification unit can provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a notification method that focuses on the main points. This allows for more appropriate information provision by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] During analysis, the analysis unit can apply a high-precision algorithm to detect subtle patterns in crustal movements and changes in groundwater levels. For example, applying a high-precision algorithm to detect subtle patterns in crustal movements improves the accuracy of the analysis. The analysis unit can also apply a high-precision algorithm to detect sudden changes in groundwater levels, improving the accuracy of the analysis. The analysis unit can also detect subtle patterns in crustal movements and changes in groundwater levels in real time, improving the accuracy of the analysis. In this way, applying a high-precision algorithm can improve the accuracy of the analysis.

[0117] The detection unit can be added with a function to automatically detect abnormal values ​​and exclude them from the detection results during detection. For example, if an abnormal value is included in the detection data, it is automatically detected and excluded from the detection results. Furthermore, if abnormal values ​​are detected continuously, the detection unit can suspect a sensor failure and exclude the value from the detection results. Furthermore, if the abnormal value is temporary, the detection unit can perform re-detection to ensure the reliability of the data. In this way, by automatically detecting abnormal values ​​and excluding them from the detection results, the reliability of the data can be ensured.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The observation unit observes crustal movements or changes in groundwater levels. The observation unit uses, for example, a GPS sensor to observe crustal movements and a water level gauge to observe changes in groundwater levels. By installing multiple GPS sensors, crustal movements can be observed with high precision, and by installing water level gauges, changes in groundwater levels can be observed in real time. Step 2: The analysis unit analyzes the data collected by the observation unit in real time. The analysis unit analyzes patterns of crustal movement and sudden changes in groundwater levels to detect signs of an impending earthquake. Step 3: The detection unit detects signs of an earthquake based on the data analyzed by the analysis unit. The detection unit predicts areas with a high probability of an earthquake occurring and the magnitude of the earthquake, and notifies the relevant organizations. Step 4: The notification unit notifies the prediction results obtained by the detection unit. The notification unit sends alerts via smartphone apps, email, and social media, indicating the areas and scale of earthquakes that are likely to occur.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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, RAM 30, and 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.

[0127] 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.

[0128] 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 audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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, RAM 30, and 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.

[0143] 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.

[0144] 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 audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] 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.

[0148] 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.

[0149] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0151] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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, RAM 30, and 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.

[0159] 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.

[0160] 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 audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0164] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] 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.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 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. an observation unit for observing crustal movements or changes in groundwater levels; an analysis unit that analyzes the data collected by the observation unit in real time; a detection unit that detects signs of an earthquake based on the data analyzed by the analysis unit; a notification unit that notifies the prediction result obtained by the detection unit; Equipped with A system characterized by:

2. The observation unit Equipped with a GPS sensor to observe crustal movements or a water level meter to observe changes in groundwater levels 2. The system of claim 1.

3. The analysis unit Analyzing patterns of crustal movement and sudden changes in groundwater levels to detect signs of upcoming earthquakes 2. The system of claim 1.

4. The detection unit Predicting areas where earthquakes may occur and their magnitude 2. The system of claim 1.

5. The notification unit Alerts are sent via smartphone app, email, or social media 2. The system of claim 1.

6. The analysis unit Analyzing correlations with past earthquake data 2. The system of claim 1.

7. The observation unit Estimate the user's emotions and adjust the timing of acquiring observation data based on the estimated user emotions.

2. The system of claim 1.

8. The observation unit Analyzing past earthquake data to determine where to install sensors to monitor crustal movements and changes in groundwater levels 2. The system of claim 1.

9. The observation unit Add a function to automatically detect abnormal values ​​and perform re-observation when acquiring observation data.

2. The system of claim 1.

10. The observation unit When acquiring observation data, the data is corrected taking into account meteorological conditions and seasonal variations.

2. The system of claim 1.

Images