System

Abstract

The system according to the embodiment aims to issue an early warning against a natural disaster and increase the evacuation time of residents.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, an alarm unit, and a notification unit. The collection unit collects data related to a natural disaster from a sensor or a database. The analysis unit analyzes the data collected by the collection unit and identifies patterns of natural disasters. The alarm unit issues an alarm based on the pattern identified by the analysis unit. The notification unit notifies the residents of the alarm issued by the alarm 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 technology does not provide sufficient early warnings for natural disasters, and there is room for improvement in ensuring residents have enough time to evacuate.

[0005] The system according to the embodiment aims to issue early warnings for natural disasters, thereby increasing the time for residents to evacuate. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an alarm unit, and a notification unit. The collection unit collects data related to natural disasters from sensors or a database. The analysis unit analyzes the data collected by the collection unit and identifies patterns of natural disasters. The alarm unit issues an alert based on the pattern identified by the analysis unit. The notification unit notifies residents of the alert issued by the alert unit. [Effects of the Invention]

[0007] The system according to the embodiment can issue early warnings for natural disasters, giving residents more time to evacuate. [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) A natural disaster warning system according to an embodiment of the present invention utilizes AI-based machine learning algorithms to identify patterns associated with natural disasters such as earthquakes, floods, and typhoons and issue early warnings. The system collects data related to natural disasters from sensors and databases, analyzes the data using AI, identifies patterns, and issues early warnings. For example, the system collects data from seismometers, weather observation devices, satellite data, and drones. The system then analyzes the collected data using AI and identifies natural disaster patterns using deep learning and recurrent neural networks (RNNs). Examples of patterns include minute vibrations that signal an earthquake or a sudden increase in rainfall that signals a flood. The system then issues early warnings using AI based on the identified patterns. For example, if an earthquake signal is detected, the AI ​​immediately issues an alert and urges residents to evacuate via voice or text message. This allows residents to evacuate early and minimizes damage. The system also analyzes damage after a natural disaster occurs and prioritizes rescue efforts. For example, when an earthquake occurs, AI analyzes the damage situation and determines the priority of rescue efforts, enabling rapid and efficient rescue efforts. This enables natural disaster warning systems to provide early warnings and rapid responses to natural disasters, ensuring the safety of residents. For example, when precursors to an earthquake are detected, residents can evacuate with a few minutes to a few tens of minutes to spare. Similarly, when precursors to a flood are detected, residents can evacuate with a few hours to spare. This makes it possible to minimize damage.

[0029] A natural disaster warning system according to an embodiment includes a collection unit, an analysis unit, a warning unit, and a notification unit. The collection unit collects data related to natural disasters from sensors or a database. Examples of data related to natural disasters include, but are not limited to, earthquake data, meteorological data, and flood data. The collection unit collects data from, for example, seismometers, meteorological observation devices, satellite data, and drones. Examples of seismometers include short-period seismometers and long-period seismometers. Examples of meteorological observation devices include anemometers and rain gauges. Examples of satellite data include meteorological satellite data and remote sensing data. Examples of drones include fixed-wing drones and multi-rotor drones. The analysis unit analyzes the data collected by the collection unit and identifies patterns related to natural disasters. The analysis unit uses deep learning or recurrent neural networks (RNNs) to identify patterns related to natural disasters from the data. Deep learning can use algorithms such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). The recurrent neural network (RNN) may use algorithms such as a long short-term memory (LSTM) or a gated recurrent unit (GRU). The warning unit issues an alert based on the pattern identified by the analysis unit. The warning unit issues an early alert by voice or text message based on the identified pattern. The voice or text message may include, for example, the language of the voice message and the format of the text message. The notification unit notifies residents of the alert issued by the warning unit. The notification unit notifies residents of the alert using means such as SMS, email, or app notification. As a result, the natural disaster warning system according to the embodiment can issue an early alert for natural disasters and increase the evacuation time for residents.

[0030] The collection unit can collect data from seismometers, meteorological observation equipment, satellite data, and drones. The collection unit, for example, collects earthquake data using a seismometer. Seismometers include short-period seismometers and long-period seismometers. The collection unit, for example, collects weather data using a weather observation equipment. Weather observation equipment includes anemometers and rain gauges. The collection unit, for example, collects weather data using satellite data. Satellite data includes meteorological satellite data and remote sensing data. The collection unit, for example, collects data related to natural disasters using a drone. Drones include fixed-wing drones and multi-rotor drones. This makes it possible to collect data related to natural disasters from a variety of data sources. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from a seismometer or meteorological observation equipment into a generation AI and have the generation AI perform preprocessing of the data.

[0031] The analysis unit can identify patterns of natural disasters using deep learning or a recurrent neural network (RNN). The analysis unit can identify patterns of natural disasters using, for example, deep learning. Deep learning includes algorithms such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). The analysis unit can identify patterns of natural disasters using, for example, a recurrent neural network (RNN). Recurrent neural networks include algorithms such as long short-term memory (LSTM) and gated recurrent units (GRUs). The analysis unit can identify minute vibrations that are precursors to earthquakes using, for example, deep learning. The analysis unit can also identify sudden increases in rainfall that are precursors to floods using, for example, a recurrent neural network. The analysis unit can also predict the path of typhoons using, for example, deep learning. This allows for highly accurate identification of patterns of natural disasters using advanced machine learning algorithms. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data obtained from the collection unit into the generation AI and have the generation AI perform pattern recognition of natural disasters.

[0032] The warning unit can issue a warning by voice or text message based on the identified pattern. For example, the warning unit can issue a warning by voice message based on the identified pattern. The voice message includes the content of the warning and evacuation instructions. For example, the warning unit can issue a warning by text message based on the identified pattern. The text message includes the content of the warning and evacuation instructions. For example, if an earthquake precursor is detected, the warning unit can immediately issue a warning by voice message to urge residents to evacuate. For example, if a flood precursor is detected, the warning unit can immediately issue a warning by text message to urge residents to evacuate. For example, if a typhoon path is predicted, the warning unit can issue a warning by both voice message and text message. This allows for prompt and effective warning to residents by using voice and text messages. Some or all of the above-described processing in the warning unit can be performed using, for example, AI. For example, the warning unit can input the pattern identification results obtained from the analysis unit to a generation AI and cause the generation AI to issue a warning.

[0033] The notification unit can notify residents of the alert. The notification unit can notify residents of the alert, for example, by SMS. The SMS includes the content of the alert, evacuation instructions, etc. The notification unit can notify residents of the alert, for example, by email. The email includes the content of the alert, evacuation instructions, etc. The notification unit can notify residents of the alert, for example, by app notification. The app notification includes the content of the alert, evacuation instructions, etc. The notification unit can immediately notify residents of the alert by SMS, for example, if an earthquake precursor is detected. The notification unit can also immediately notify residents of the alert by email, for example, if a flood precursor is detected. The notification unit can also notify residents of the alert by app notification, for example, if a typhoon path is predicted. This allows residents to be notified of the alert quickly. Some or all of the above-described processing in the notification unit can be performed, for example, using AI, or can be performed without AI. For example, the notification unit can input the alert obtained from the alert unit into a generation AI and cause the generation AI to issue a notification.

[0034] The analysis unit can analyze the damage situation of a natural disaster and determine the priority of rescue operations. The analysis unit, for example, analyzes the damage situation after an earthquake. The damage situation includes the collapse of buildings and human casualties. The analysis unit, for example, analyzes the damage situation after a flood. The damage situation includes the flooded area and the number of evacuees. The analysis unit, for example, analyzes the damage situation after a typhoon. The damage situation includes wind speed and rainfall. The analysis unit, for example, analyzes the damage situation after an earthquake and determine the priority of rescue operations. The analysis unit can also analyze the damage situation after a flood and determine the priority of rescue operations. The analysis unit can also analyze the damage situation after a typhoon and determine the priority of rescue operations. This allows the damage situation to be analyzed quickly and rescue operations to be carried out efficiently. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input damage situation data to a generation AI and cause the generation AI to determine the priority of rescue operations.

[0035] The collection unit can analyze past natural disaster data and select the optimal data collection method. The collection unit, for example, analyzes past earthquake data and optimizes the placement of seismometers. The collection unit, for example, analyzes past flood data and optimizes the placement of meteorological observation devices. The collection unit, for example, analyzes past typhoon data and optimizes the method of collecting satellite data. This enables efficient data collection by utilizing past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past natural disaster data into the generation AI and have the generation AI select the optimal data collection method.

[0036] The collection unit can perform filtering based on specific regional or environmental conditions when collecting data. For example, the collection unit prioritizes collecting seismometer data in areas where earthquakes occur frequently. For example, the collection unit prioritizes collecting meteorological observation device data in areas prone to flooding. For example, the collection unit prioritizes collecting satellite data in areas in the path of typhoons. This allows for prioritized collection of necessary data based on specific regional or environmental conditions. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data based on specific regional or environmental conditions into the generation AI and have the generation AI perform filtering.

[0037] The collection unit can select the optimal collection means depending on the type of sensor when collecting data. The collection unit, for example, collects earthquake data using a seismometer. The collection unit, for example, collects flood data using a meteorological observation device. The collection unit, for example, collects typhoon data using satellite data. This makes it possible to select the optimal data collection means depending on the type of sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data depending on the type of sensor to the generation AI and have the generation AI select the optimal collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data from areas where an earthquake has occurred. For example, the collection unit prioritizes collecting data from areas where a flood has occurred. For example, the collection unit prioritizes collecting data from areas where a typhoon is approaching. This makes it possible to prioritize collecting highly relevant data by taking geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze social media activity and collect related data when collecting data. For example, the collection unit collects data on areas where there are many social media posts about earthquakes. For example, the collection unit collects data on areas where there are many social media posts about floods. For example, the collection unit collects data on areas where there are many social media posts about typhoons. This makes it possible to analyze social media activity and collect related data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activity data to a generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting past feedback when collecting data. The collection unit improves the collection method, for example, based on feedback from past earthquake data collection. The collection unit improves the collection method, for example, based on feedback from past flood data collection. The collection unit improves the collection method, for example, based on feedback from past typhoon data collection. This allows the collection method to be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and have the generation AI customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on earthquake data with high importance. For example, the analysis unit performs a detailed analysis on flood data with high importance. For example, the analysis unit performs a detailed analysis on typhoon data with high importance. This makes it possible to optimize the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the type of data. For example, the analysis unit applies an analysis algorithm dedicated to earthquakes to earthquake data. For example, the analysis unit applies an analysis algorithm dedicated to floods to flood data. For example, the analysis unit applies an analysis algorithm dedicated to typhoons to typhoon data. This makes it possible to apply the optimal analysis algorithm depending on the type of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the type of data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, refers to past earthquake analysis results to improve the accuracy of the current analysis. The analysis unit, for example, refers to past flood analysis results to improve the accuracy of the current analysis. The analysis unit, for example, refers to past typhoon analysis results to improve the accuracy of the current analysis. In this way, the accuracy of the current analysis can be improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of data collection. For example, the analysis unit prioritizes analysis of the latest earthquake data. For example, the analysis unit prioritizes analysis of the latest flood data. For example, the analysis unit prioritizes analysis of the latest typhoon data. This makes it possible to optimize the priority of analysis based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the time of data collection to the generation AI and have the generation AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relationships between data during analysis. For example, the analysis unit adjusts the order of analysis based on the relationships between earthquake data. For example, the analysis unit adjusts the order of analysis based on the relationships between flood data. For example, the analysis unit adjusts the order of analysis based on the relationships between typhoon data. This makes it possible to optimize the order of analysis based on the relationships between data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relationships between data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit provides analysis results in simple terms to a user with little technical expertise. For example, the analysis unit provides analysis results in detailed technical terminology to a user with technical expertise. For example, the analysis unit provides analysis results in appropriate terminology according to the user's level of expertise. This makes it possible to provide analysis results in appropriate terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology in the analysis.

[0047] The warning unit can adjust the level of detail of the warning based on the importance of the natural disaster when issuing the warning. For example, the warning unit issues a detailed warning for an earthquake with a high importance. For example, the warning unit issues a detailed warning for a flood with a high importance. For example, the warning unit issues a detailed warning for a typhoon with a high importance. This makes it possible to optimize the level of detail of the warning based on the importance of the natural disaster. Some or all of the above-mentioned processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input the importance of the natural disaster to the generation AI and cause the generation AI to adjust the level of detail of the warning.

[0048] When issuing an alert, the alert unit can apply different alert algorithms depending on the type of natural disaster. For example, the alert unit applies an alert algorithm dedicated to earthquakes to earthquakes. For example, the alert unit applies an alert algorithm dedicated to floods to floods. For example, the alert unit applies an alert algorithm dedicated to typhoons to typhoons. This makes it possible to apply the optimal alert algorithm depending on the type of natural disaster. Some or all of the above-mentioned processing in the alert unit may be performed using AI, for example, or may be performed without using AI. For example, the alert unit can input the type of natural disaster to the generation AI and cause the generation AI to apply different alert algorithms.

[0049] When issuing an alert, the alert unit can improve the accuracy of the alert by referring to past alert results. The alert unit, for example, can improve the accuracy of the current alert by referring to past earthquake alert results. The alert unit, for example, can improve the accuracy of the current alert by referring to past flood alert results. The alert unit, for example, can improve the accuracy of the current alert by referring to past typhoon alert results. This makes it possible to improve the accuracy of the current alert by referring to past alert results. Some or all of the above-mentioned processing in the alert unit may be performed, for example, using AI, or may be performed without using AI. For example, the alert unit can input past alert results into the generation AI and have the generation AI improve the accuracy of the alert.

[0050] When issuing an alert, the alert unit can determine the priority of the alert based on the time of occurrence of the natural disaster. For example, the alert unit prioritizes issuing an alert for the most recent earthquake. For example, the alert unit prioritizes issuing an alert for the most recent flood. For example, the alert unit prioritizes issuing an alert for the most recent typhoon. This makes it possible to optimize the priority of alerts based on the time of occurrence of the natural disaster. Some or all of the above-mentioned processing in the alert unit may be performed using AI, for example, or may be performed without using AI. For example, the alert unit can input the time of occurrence of the natural disaster into the generation AI and have the generation AI determine the priority of the alerts.

[0051] The warning unit can adjust the order of warnings based on the relevance of natural disasters when issuing a warning. The warning unit adjusts the order of warnings based on, for example, the relevance of earthquakes. The warning unit adjusts the order of warnings based on, for example, the relevance of floods. The warning unit adjusts the order of warnings based on, for example, the relevance of typhoons. This makes it possible to optimize the order of warnings based on the relevance of natural disasters. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the relevance of natural disasters to a generation AI and have the generation AI adjust the order of warnings.

[0052] When issuing an alert, the alert unit can adjust the use of technical terminology in the alert according to the user's level of expertise. For example, the alert unit issues an alert in simple terms to a user with little technical expertise. For example, the alert unit issues an alert in detailed technical terminology to a user with technical expertise. For example, the alert unit issues an alert in appropriate terminology according to the user's level of expertise. This makes it possible to issue an alert in appropriate terminology according to the user's level of expertise. Some or all of the above-described processing in the alert unit may be performed using AI, for example, or may be performed without using AI. For example, the alert unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology in the alert.

[0053] The notification unit can adjust the level of detail of the notification based on the importance of the natural disaster when issuing a notification. For example, the notification unit issues a detailed notification for an earthquake with a high importance. For example, the notification unit issues a detailed notification for a flood with a high importance. For example, the notification unit issues a detailed notification for a typhoon with a high importance. This makes it possible to optimize the level of detail of the notification based on the importance of the natural disaster. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the importance of the natural disaster to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0054] The notification unit can apply different notification algorithms depending on the type of natural disaster when making a notification. For example, the notification unit applies a notification algorithm dedicated to earthquakes to an earthquake. For example, the notification unit applies a notification algorithm dedicated to floods to a flood. For example, the notification unit applies a notification algorithm dedicated to typhoons to a typhoon. This makes it possible to apply the optimal notification algorithm depending on the type of natural disaster. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the type of natural disaster to the generation AI and cause the generation AI to apply different notification algorithms.

[0055] The notification unit can improve the accuracy of notifications by referring to past notification results when making notifications. The notification unit can improve the accuracy of current notifications by referring to, for example, past earthquake notification results. The notification unit can improve the accuracy of current notifications by referring to, for example, past flood notification results. The notification unit can improve the accuracy of current notifications by referring to, for example, past typhoon notification results. This makes it possible to improve the accuracy of current notifications by referring to past notification results. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input past notification results into the generation AI and have the generation AI improve the accuracy of notifications.

[0056] At the time of notification, the notification unit can determine the priority of notifications based on the time of occurrence of the natural disaster. For example, the notification unit prioritizes issuing notifications for the most recent earthquake. For example, the notification unit prioritizes issuing notifications for the most recent flood. For example, the notification unit prioritizes issuing notifications for the most recent typhoon. This makes it possible to optimize the priority of notifications based on the time of occurrence of the natural disaster. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the time of occurrence of the natural disaster into the generation AI and have the generation AI determine the priority of notifications.

[0057] The notification unit can adjust the order of notifications based on the relevance of natural disasters when making notifications. The notification unit adjusts the order of notifications based on, for example, the relevance of earthquakes. The notification unit adjusts the order of notifications based on, for example, the relevance of floods. The notification unit adjusts the order of notifications based on, for example, the relevance of typhoons. This makes it possible to optimize the order of notifications based on the relevance of natural disasters. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the relevance of natural disasters into a generation AI and cause the generation AI to adjust the order of notifications.

[0058] The notification unit can adjust the use of technical terminology in the notification depending on the user's level of expertise when issuing a notification. For example, the notification unit issues a notification in simple terms to a user with little expertise. For example, the notification unit issues a notification in detailed technical terminology to a user with expertise. For example, the notification unit issues a notification in appropriate terminology depending on the user's level of expertise. This makes it possible to issue a notification in appropriate terminology depending on the user's level of expertise. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology in the notification.

[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] The analysis unit can also use an anomaly detection algorithm when identifying patterns of natural disasters. For example, the analysis unit can apply an anomaly detection algorithm to earthquake data to detect abnormal vibrations that deviate from normal vibration patterns. The analysis unit can also apply an anomaly detection algorithm to flood data to detect abnormal rainfall that deviates from normal rainfall patterns. The analysis unit can also apply an anomaly detection algorithm to typhoon data to detect abnormal wind speeds that deviate from normal wind speed patterns. Thus, by using an anomaly detection algorithm, it is possible to detect precursors to natural disasters at an earlier stage.

[0061] During data collection, the collection unit may also use environmental sensors to collect air quality data. For example, the collection unit may use environmental sensors to detect changes in atmospheric gas concentrations during an earthquake. The collection unit may also use environmental sensors to collect water quality data during a flood. Furthermore, the collection unit may use environmental sensors to measure atmospheric particulate concentrations during a typhoon. Thus, the use of environmental sensors allows for the collection of additional data related to natural disasters and allows for more accurate analysis.

[0062] When identifying patterns of natural disasters, the analysis unit can also distribute the analysis process by using cloud computing. For example, the analysis unit can improve the analysis speed by distributing the analysis of earthquake data across multiple cloud servers. In addition, the analysis unit can efficiently process large amounts of data by analyzing flood data on the cloud. Furthermore, the analysis unit can achieve real-time analysis by using cloud computing to analyze typhoon data. As a result, the efficiency of the analysis process can be significantly improved by using cloud computing.

[0063] The warning unit may also customize the content of the warning by taking into account the user's location information when issuing a voice or text message warning based on the identified pattern. For example, if an earthquake precursor is detected, the warning unit may issue a voice message providing guidance to the nearest evacuation site based on the user's location information. Also, if a flood precursor is detected, the warning unit may issue a text message indicating an evacuation route based on the user's location information. Furthermore, if a typhoon path is predicted, the warning unit may issue a warning including predicted wind speed and rainfall based on the user's location information. By taking the user's location information into account, more specific and useful warnings can be provided.

[0064] The notification unit can also use social media to spread the warning when notifying residents. For example, if the notification unit detects signs of an earthquake, it can issue a warning through social media such as Twitter (registered trademark) or Facebook (registered trademark). Also, if the notification unit detects signs of a flood, it can widely disseminate evacuation instructions through social media. Furthermore, if the path of a typhoon is predicted, the notification unit can provide the latest information in real time through social media. This makes it possible to disseminate warnings quickly and widely by utilizing social media.

[0065] When analyzing damage caused by natural disasters, the analysis unit can also use drones to collect on-site video data and use it for analysis. For example, the analysis unit can fly a drone after an earthquake to collect video of the affected area and analyze the state of collapsed buildings. The analysis unit can also fly a drone after a flood to collect video of the inundated area and analyze the damage. Furthermore, the analysis unit can fly a drone after a typhoon to check the effects of wind speed and rainfall through video and analyze the damage. In this way, by using drones, detailed on-site video data can be collected and more accurate analysis of the damage can be performed.

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

[0067] Step 1: The collection unit collects data related to natural disasters from sensors or databases. Data related to natural disasters includes earthquake data, weather data, flood data, etc. The collection unit collects data from seismometers, weather observation devices, satellite data, and drones. Seismometers include short-period seismometers and long-period seismometers, and weather observation devices include anemometers and rain gauges. Satellite data includes weather satellite data and remote sensing data, and drones include fixed-wing drones and multi-rotor drones. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies patterns related to natural disasters. The analysis unit uses deep learning and recurrent neural networks (RNNs) to identify patterns related to natural disasters in the data. Deep learning can use algorithms such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), while recurrent neural networks (RNNs) can use algorithms such as long short-term memory (LSTMs) and gated recurrent units (GRUs). Step 3: The alarm unit issues an alert based on the pattern identified by the analysis unit. The alarm unit issues an early warning by voice or text message based on the identified pattern, including the language of the voice message and the format of the text message. Step 4: The notification unit notifies residents of the alert issued by the warning unit. The notification unit notifies residents of the alert using means such as SMS, email, or app notification.

[0068] (Example 2) A natural disaster warning system according to an embodiment of the present invention utilizes AI-based machine learning algorithms to identify patterns associated with natural disasters such as earthquakes, floods, and typhoons and issue early warnings. The system collects data related to natural disasters from sensors and databases, analyzes the data using AI, identifies patterns, and issues early warnings. For example, the system collects data from seismometers, weather observation devices, satellite data, and drones. The system then analyzes the collected data using AI and identifies natural disaster patterns using deep learning and recurrent neural networks (RNNs). Examples of patterns include minute vibrations that signal an earthquake or a sudden increase in rainfall that signals a flood. The system then issues early warnings using AI based on the identified patterns. For example, if an earthquake signal is detected, the AI ​​immediately issues an alert and urges residents to evacuate via voice or text message. This allows residents to evacuate early and minimizes damage. The system also analyzes damage after a natural disaster occurs and prioritizes rescue efforts. For example, when an earthquake occurs, AI analyzes the damage situation and determines the priority of rescue efforts, enabling rapid and efficient rescue efforts. This enables natural disaster warning systems to provide early warnings and rapid responses to natural disasters, ensuring the safety of residents. For example, when precursors to an earthquake are detected, residents can evacuate with a few minutes to a few tens of minutes to spare. Similarly, when precursors to a flood are detected, residents can evacuate with a few hours to spare. This makes it possible to minimize damage.

[0069] A natural disaster warning system according to an embodiment includes a collection unit, an analysis unit, a warning unit, and a notification unit. The collection unit collects data related to natural disasters from sensors or a database. Examples of data related to natural disasters include, but are not limited to, earthquake data, meteorological data, and flood data. The collection unit collects data from, for example, seismometers, meteorological observation devices, satellite data, and drones. Examples of seismometers include short-period seismometers and long-period seismometers. Examples of meteorological observation devices include anemometers and rain gauges. Examples of satellite data include meteorological satellite data and remote sensing data. Examples of drones include fixed-wing drones and multi-rotor drones. The analysis unit analyzes the data collected by the collection unit and identifies patterns related to natural disasters. The analysis unit uses deep learning or recurrent neural networks (RNNs) to identify patterns related to natural disasters from the data. Deep learning can use algorithms such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). The recurrent neural network (RNN) may use algorithms such as a long short-term memory (LSTM) or a gated recurrent unit (GRU). The warning unit issues an alert based on the pattern identified by the analysis unit. The warning unit issues an early alert by voice or text message based on the identified pattern. The voice or text message may include, for example, the language of the voice message and the format of the text message. The notification unit notifies residents of the alert issued by the warning unit. The notification unit notifies residents of the alert using means such as SMS, email, or app notification. As a result, the natural disaster warning system according to the embodiment can issue an early alert for natural disasters and increase the evacuation time for residents.

[0070] The collection unit can collect data from seismometers, meteorological observation equipment, satellite data, and drones. The collection unit, for example, collects earthquake data using a seismometer. Seismometers include short-period seismometers and long-period seismometers. The collection unit, for example, collects weather data using a weather observation equipment. Weather observation equipment includes anemometers and rain gauges. The collection unit, for example, collects weather data using satellite data. Satellite data includes meteorological satellite data and remote sensing data. The collection unit, for example, collects data related to natural disasters using a drone. Drones include fixed-wing drones and multi-rotor drones. This makes it possible to collect data related to natural disasters from a variety of data sources. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from a seismometer or meteorological observation equipment into a generation AI and have the generation AI perform preprocessing of the data.

[0071] The analysis unit can identify patterns of natural disasters using deep learning or a recurrent neural network (RNN). The analysis unit can identify patterns of natural disasters using, for example, deep learning. Deep learning includes algorithms such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). The analysis unit can identify patterns of natural disasters using, for example, a recurrent neural network (RNN). Recurrent neural networks include algorithms such as long short-term memory (LSTM) and gated recurrent units (GRUs). The analysis unit can identify minute vibrations that are precursors to earthquakes using, for example, deep learning. The analysis unit can also identify sudden increases in rainfall that are precursors to floods using, for example, a recurrent neural network. The analysis unit can also predict the path of typhoons using, for example, deep learning. This allows for highly accurate identification of patterns of natural disasters using advanced machine learning algorithms. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data obtained from the collection unit into the generation AI and have the generation AI perform pattern recognition of natural disasters.

[0072] The warning unit can issue a warning by voice or text message based on the identified pattern. For example, the warning unit can issue a warning by voice message based on the identified pattern. The voice message includes the content of the warning and evacuation instructions. For example, the warning unit can issue a warning by text message based on the identified pattern. The text message includes the content of the warning and evacuation instructions. For example, if an earthquake precursor is detected, the warning unit can immediately issue a warning by voice message to urge residents to evacuate. For example, if a flood precursor is detected, the warning unit can immediately issue a warning by text message to urge residents to evacuate. For example, if a typhoon path is predicted, the warning unit can issue a warning by both voice message and text message. This allows for prompt and effective warning to residents by using voice and text messages. Some or all of the above-described processing in the warning unit can be performed using, for example, AI. For example, the warning unit can input the pattern identification results obtained from the analysis unit to a generation AI and cause the generation AI to issue a warning.

[0073] The notification unit can notify residents of the alert. The notification unit can notify residents of the alert, for example, by SMS. The SMS includes the content of the alert, evacuation instructions, etc. The notification unit can notify residents of the alert, for example, by email. The email includes the content of the alert, evacuation instructions, etc. The notification unit can notify residents of the alert, for example, by app notification. The app notification includes the content of the alert, evacuation instructions, etc. The notification unit can immediately notify residents of the alert by SMS, for example, if an earthquake precursor is detected. The notification unit can also immediately notify residents of the alert by email, for example, if a flood precursor is detected. The notification unit can also notify residents of the alert by app notification, for example, if a typhoon path is predicted. This allows residents to be notified of the alert quickly. Some or all of the above-described processing in the notification unit can be performed, for example, using AI, or can be performed without AI. For example, the notification unit can input the alert obtained from the alert unit into a generation AI and cause the generation AI to issue a notification.

[0074] The analysis unit can analyze the damage situation of a natural disaster and determine the priority of rescue operations. The analysis unit, for example, analyzes the damage situation after an earthquake. The damage situation includes the collapse of buildings and human casualties. The analysis unit, for example, analyzes the damage situation after a flood. The damage situation includes the flooded area and the number of evacuees. The analysis unit, for example, analyzes the damage situation after a typhoon. The damage situation includes wind speed and rainfall. The analysis unit, for example, analyzes the damage situation after an earthquake and determine the priority of rescue operations. The analysis unit can also analyze the damage situation after a flood and determine the priority of rescue operations. The analysis unit can also analyze the damage situation after a typhoon and determine the priority of rescue operations. This allows the damage situation to be analyzed quickly and rescue operations to be carried out efficiently. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input damage situation data to a generation AI and cause the generation AI to determine the priority of rescue operations.

[0075] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit increases the frequency of data collection and provides information in real time. For example, if the user is relaxed, the collection unit maintains the normal frequency of data collection. For example, if the user is facing an emergency, the collection unit immediately starts data collection and quickly provides information. This allows the timing of data collection to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0076] The collection unit can analyze past natural disaster data and select the optimal data collection method. The collection unit, for example, analyzes past earthquake data and optimizes the placement of seismometers. The collection unit, for example, analyzes past flood data and optimizes the placement of meteorological observation devices. The collection unit, for example, analyzes past typhoon data and optimizes the method of collecting satellite data. This enables efficient data collection by utilizing past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past natural disaster data into the generation AI and have the generation AI select the optimal data collection method.

[0077] The collection unit can perform filtering based on specific regional or environmental conditions when collecting data. For example, the collection unit prioritizes collecting seismometer data in areas where earthquakes occur frequently. For example, the collection unit prioritizes collecting meteorological observation device data in areas prone to flooding. For example, the collection unit prioritizes collecting satellite data in areas in the path of typhoons. This allows for prioritized collection of necessary data based on specific regional or environmental conditions. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data based on specific regional or environmental conditions into the generation AI and have the generation AI perform filtering.

[0078] The collection unit can select the optimal collection means depending on the type of sensor when collecting data. The collection unit, for example, collects earthquake data using a seismometer. The collection unit, for example, collects flood data using a meteorological observation device. The collection unit, for example, collects typhoon data using satellite data. This makes it possible to select the optimal data collection means depending on the type of sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data depending on the type of sensor to the generation AI and have the generation AI select the optimal collection means.

[0079] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting earthquake data. For example, if the user is relaxed, the collection unit performs normal data collection. For example, if the user is facing an emergency, the collection unit immediately collects the most important data. This allows the priority of data to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0080] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data from areas where an earthquake has occurred. For example, the collection unit prioritizes collecting data from areas where a flood has occurred. For example, the collection unit prioritizes collecting data from areas where a typhoon is approaching. This makes it possible to prioritize collecting highly relevant data by taking geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0081] The collection unit can analyze social media activity and collect related data when collecting data. For example, the collection unit collects data on areas where there are many social media posts about earthquakes. For example, the collection unit collects data on areas where there are many social media posts about floods. For example, the collection unit collects data on areas where there are many social media posts about typhoons. This makes it possible to analyze social media activity and collect related data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activity data to a generation AI and cause the generation AI to collect related data.

[0082] The collection unit can customize the collection method by reflecting past feedback when collecting data. The collection unit improves the collection method, for example, based on feedback from past earthquake data collection. The collection unit improves the collection method, for example, based on feedback from past flood data collection. The collection unit improves the collection method, for example, based on feedback from past typhoon data collection. This allows the collection method to be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and have the generation AI customize the collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit provides simple, highly visible analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is facing an emergency, the analysis unit provides quick, concise analysis results. This allows the way the analysis results are presented to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on earthquake data with high importance. For example, the analysis unit performs a detailed analysis on flood data with high importance. For example, the analysis unit performs a detailed analysis on typhoon data with high importance. This makes it possible to optimize the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the type of data. For example, the analysis unit applies an analysis algorithm dedicated to earthquakes to earthquake data. For example, the analysis unit applies an analysis algorithm dedicated to floods to flood data. For example, the analysis unit applies an analysis algorithm dedicated to typhoons to typhoon data. This makes it possible to apply the optimal analysis algorithm depending on the type of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the type of data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, refers to past earthquake analysis results to improve the accuracy of the current analysis. The analysis unit, for example, refers to past flood analysis results to improve the accuracy of the current analysis. The analysis unit, for example, refers to past typhoon analysis results to improve the accuracy of the current analysis. In this way, the accuracy of the current analysis can be improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit provides a short and concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is facing an emergency, the analysis unit provides a quick and concise analysis result. This allows the length of the analysis result to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the time of data collection. For example, the analysis unit prioritizes analysis of the latest earthquake data. For example, the analysis unit prioritizes analysis of the latest flood data. For example, the analysis unit prioritizes analysis of the latest typhoon data. This makes it possible to optimize the priority of analysis based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the time of data collection to the generation AI and have the generation AI determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relationships between data during analysis. For example, the analysis unit adjusts the order of analysis based on the relationships between earthquake data. For example, the analysis unit adjusts the order of analysis based on the relationships between flood data. For example, the analysis unit adjusts the order of analysis based on the relationships between typhoon data. This makes it possible to optimize the order of analysis based on the relationships between data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relationships between data to the generation AI and have the generation AI adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit provides analysis results in simple terms to a user with little technical expertise. For example, the analysis unit provides analysis results in detailed technical terminology to a user with technical expertise. For example, the analysis unit provides analysis results in appropriate terminology according to the user's level of expertise. This makes it possible to provide analysis results in appropriate terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology in the analysis.

[0091] The warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the warning unit issues a simple, highly visible warning. For example, if the user is relaxed, the warning unit issues a detailed warning. For example, if the user is facing an emergency, the warning unit issues a quick, to-the-point warning. This allows the way the warning is expressed to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the warning is expressed.

[0092] The warning unit can adjust the level of detail of the warning based on the importance of the natural disaster when issuing the warning. For example, the warning unit issues a detailed warning for an earthquake with a high importance. For example, the warning unit issues a detailed warning for a flood with a high importance. For example, the warning unit issues a detailed warning for a typhoon with a high importance. This makes it possible to optimize the level of detail of the warning based on the importance of the natural disaster. Some or all of the above-mentioned processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input the importance of the natural disaster to the generation AI and cause the generation AI to adjust the level of detail of the warning.

[0093] When issuing an alert, the alert unit can apply different alert algorithms depending on the type of natural disaster. For example, the alert unit applies an alert algorithm dedicated to earthquakes to earthquakes. For example, the alert unit applies an alert algorithm dedicated to floods to floods. For example, the alert unit applies an alert algorithm dedicated to typhoons to typhoons. This makes it possible to apply the optimal alert algorithm depending on the type of natural disaster. Some or all of the above-mentioned processing in the alert unit may be performed using AI, for example, or may be performed without using AI. For example, the alert unit can input the type of natural disaster to the generation AI and cause the generation AI to apply different alert algorithms.

[0094] When issuing an alert, the alert unit can improve the accuracy of the alert by referring to past alert results. The alert unit, for example, can improve the accuracy of the current alert by referring to past earthquake alert results. The alert unit, for example, can improve the accuracy of the current alert by referring to past flood alert results. The alert unit, for example, can improve the accuracy of the current alert by referring to past typhoon alert results. This makes it possible to improve the accuracy of the current alert by referring to past alert results. Some or all of the above-mentioned processing in the alert unit may be performed, for example, using AI, or may be performed without using AI. For example, the alert unit can input past alert results into the generation AI and have the generation AI improve the accuracy of the alert.

[0095] The warning unit can estimate the user's emotion and adjust the length of the warning based on the estimated user's emotion. For example, if the user is feeling anxious, the warning unit issues a short and to-the-point warning. For example, if the user is relaxed, the warning unit issues a detailed warning. For example, if the user is facing an emergency, the warning unit issues a quick and to-the-point warning. This allows the length of the warning to be optimized according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input the user's emotion data to the generation AI and have the generation AI adjust the length of the warning.

[0096] When issuing an alert, the alert unit can determine the priority of the alert based on the time of occurrence of the natural disaster. For example, the alert unit prioritizes issuing an alert for the most recent earthquake. For example, the alert unit prioritizes issuing an alert for the most recent flood. For example, the alert unit prioritizes issuing an alert for the most recent typhoon. This makes it possible to optimize the priority of alerts based on the time of occurrence of the natural disaster. Some or all of the above-mentioned processing in the alert unit may be performed using AI, for example, or may be performed without using AI. For example, the alert unit can input the time of occurrence of the natural disaster into the generation AI and have the generation AI determine the priority of the alerts.

[0097] The warning unit can adjust the order of warnings based on the relevance of natural disasters when issuing a warning. The warning unit adjusts the order of warnings based on, for example, the relevance of earthquakes. The warning unit adjusts the order of warnings based on, for example, the relevance of floods. The warning unit adjusts the order of warnings based on, for example, the relevance of typhoons. This makes it possible to optimize the order of warnings based on the relevance of natural disasters. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the relevance of natural disasters to a generation AI and have the generation AI adjust the order of warnings.

[0098] When issuing an alert, the alert unit can adjust the use of technical terminology in the alert according to the user's level of expertise. For example, the alert unit issues an alert in simple terms to a user with little technical expertise. For example, the alert unit issues an alert in detailed technical terminology to a user with technical expertise. For example, the alert unit issues an alert in appropriate terminology according to the user's level of expertise. This makes it possible to issue an alert in appropriate terminology according to the user's level of expertise. Some or all of the above-described processing in the alert unit may be performed using AI, for example, or may be performed without using AI. For example, the alert unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology in the alert.

[0099] The notification unit can estimate the user's emotions and adjust the notification presentation method based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit issues a simple, highly visible notification. For example, if the user is relaxed, the notification unit issues a detailed notification. For example, if the user is facing an emergency, the notification unit issues a quick, to-the-point notification. This allows the notification presentation method to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification presentation method.

[0100] The notification unit can adjust the level of detail of the notification based on the importance of the natural disaster when issuing a notification. For example, the notification unit issues a detailed notification for an earthquake with a high importance. For example, the notification unit issues a detailed notification for a flood with a high importance. For example, the notification unit issues a detailed notification for a typhoon with a high importance. This makes it possible to optimize the level of detail of the notification based on the importance of the natural disaster. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the importance of the natural disaster to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0101] The notification unit can apply different notification algorithms depending on the type of natural disaster when making a notification. For example, the notification unit applies a notification algorithm dedicated to earthquakes to an earthquake. For example, the notification unit applies a notification algorithm dedicated to floods to a flood. For example, the notification unit applies a notification algorithm dedicated to typhoons to a typhoon. This makes it possible to apply the optimal notification algorithm depending on the type of natural disaster. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the type of natural disaster to the generation AI and cause the generation AI to apply different notification algorithms.

[0102] The notification unit can improve the accuracy of notifications by referring to past notification results when making notifications. The notification unit can improve the accuracy of current notifications by referring to, for example, past earthquake notification results. The notification unit can improve the accuracy of current notifications by referring to, for example, past flood notification results. The notification unit can improve the accuracy of current notifications by referring to, for example, past typhoon notification results. This makes it possible to improve the accuracy of current notifications by referring to past notification results. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input past notification results into the generation AI and have the generation AI improve the accuracy of notifications.

[0103] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated user's emotions. For example, if the user is feeling anxious, the notification unit issues a short and to-the-point notification. For example, if the user is feeling relaxed, the notification unit issues a detailed notification. For example, if the user is facing an emergency, the notification unit issues a quick and to-the-point notification. This allows the length of the notification to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the notification.

[0104] At the time of notification, the notification unit can determine the priority of notifications based on the time of occurrence of the natural disaster. For example, the notification unit prioritizes issuing notifications for the most recent earthquake. For example, the notification unit prioritizes issuing notifications for the most recent flood. For example, the notification unit prioritizes issuing notifications for the most recent typhoon. This makes it possible to optimize the priority of notifications based on the time of occurrence of the natural disaster. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the time of occurrence of the natural disaster into the generation AI and have the generation AI determine the priority of notifications.

[0105] The notification unit can adjust the order of notifications based on the relevance of natural disasters when making notifications. The notification unit adjusts the order of notifications based on, for example, the relevance of earthquakes. The notification unit adjusts the order of notifications based on, for example, the relevance of floods. The notification unit adjusts the order of notifications based on, for example, the relevance of typhoons. This makes it possible to optimize the order of notifications based on the relevance of natural disasters. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the relevance of natural disasters into a generation AI and cause the generation AI to adjust the order of notifications.

[0106] The notification unit can adjust the use of technical terminology in the notification depending on the user's level of expertise when issuing a notification. For example, the notification unit issues a notification in simple terms to a user with little expertise. For example, the notification unit issues a notification in detailed technical terminology to a user with expertise. For example, the notification unit issues a notification in appropriate terminology depending on the user's level of expertise. This makes it possible to issue a notification in appropriate terminology depending on the user's level of expertise. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology in the notification. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, warning unit, and notification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects earthquake data and weather data using the camera 42 and sensors of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, identifies patterns of natural disasters using deep learning or recurrent neural networks (RNN). The warning unit, realized, for example, by the control unit 46A of the smart device 14, issues an early warning by voice or text message based on the identified pattern. The notification unit, for example, notifies residents of the warning via the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, warning 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 collection unit collects earthquake data and meteorological data using the camera 42 and sensors of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, identifies patterns of natural disasters using deep learning or recurrent neural networks (RNN). The warning unit, realized, for example, by the control unit 46A of the smart glasses 214, issues an early warning by voice or text message based on the identified pattern. The notification unit, for example, notifies residents of the warning via the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, warning 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 collection unit collects earthquake data and meteorological data using the camera 42 and sensors of the headset terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, identifies patterns of natural disasters using deep learning or a recurrent neural network (RNN). The warning unit, realized, for example, by the control unit 46A of the headset terminal 314, issues an early warning by voice or text message based on the identified pattern. The notification unit, for example, notifies residents of the warning via the communication I / F 44 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, warning 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 collection unit collects earthquake data and meteorological data using the camera 42 and sensors of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies patterns of natural disasters using deep learning or a recurrent neural network (RNN). The warning unit is realized, for example, by the control unit 46A of the robot 414 and issues an early warning by voice or text message based on the identified pattern. The notification unit notifies residents of the warning via, for example, the communication I / F 44 of the robot 414.

[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] The analysis unit can also use an anomaly detection algorithm when identifying patterns of natural disasters. For example, the analysis unit can apply an anomaly detection algorithm to earthquake data to detect abnormal vibrations that deviate from normal vibration patterns. The analysis unit can also apply an anomaly detection algorithm to flood data to detect abnormal rainfall that deviates from normal rainfall patterns. The analysis unit can also apply an anomaly detection algorithm to typhoon data to detect abnormal wind speeds that deviate from normal wind speed patterns. Thus, by using an anomaly detection algorithm, it is possible to detect precursors to natural disasters at an earlier stage.

[0109] During data collection, the collection unit may also use environmental sensors to collect air quality data. For example, the collection unit may use environmental sensors to detect changes in atmospheric gas concentrations during an earthquake. The collection unit may also use environmental sensors to collect water quality data during a flood. Furthermore, the collection unit may use environmental sensors to measure atmospheric particulate concentrations during a typhoon. Thus, the use of environmental sensors allows for the collection of additional data related to natural disasters and allows for more accurate analysis.

[0110] When identifying patterns of natural disasters, the analysis unit can also distribute the analysis process by using cloud computing. For example, the analysis unit can improve the analysis speed by distributing the analysis of earthquake data across multiple cloud servers. In addition, the analysis unit can efficiently process large amounts of data by analyzing flood data on the cloud. Furthermore, the analysis unit can achieve real-time analysis by using cloud computing to analyze typhoon data. As a result, the efficiency of the analysis process can be significantly improved by using cloud computing.

[0111] The warning unit may also customize the content of the warning by taking into account the user's location information when issuing a voice or text message warning based on the identified pattern. For example, if an earthquake precursor is detected, the warning unit may issue a voice message providing guidance to the nearest evacuation site based on the user's location information. Also, if a flood precursor is detected, the warning unit may issue a text message indicating an evacuation route based on the user's location information. Furthermore, if a typhoon path is predicted, the warning unit may issue a warning including predicted wind speed and rainfall based on the user's location information. By taking the user's location information into account, more specific and useful warnings can be provided.

[0112] When notifying residents of an alert, the notification unit can also use social media to spread the alert. For example, if the notification unit detects signs of an earthquake, it can issue an alert through social media such as Twitter or Facebook. Also, if the notification unit detects signs of a flood, it can widely disseminate evacuation instructions through social media. Furthermore, if the path of a typhoon is predicted, the notification unit can provide the latest information in real time through social media. This makes it possible to disseminate alerts quickly and widely by utilizing social media.

[0113] When analyzing damage caused by natural disasters, the analysis unit can also use drones to collect on-site video data and use it for analysis. For example, the analysis unit can fly a drone after an earthquake to collect video of the affected area and analyze the state of collapsed buildings. The analysis unit can also fly a drone after a flood to collect video of the inundated area and analyze the damage. Furthermore, the analysis unit can fly a drone after a typhoon to check the effects of wind speed and rainfall through video and analyze the damage. In this way, by using drones, detailed on-site video data can be collected and more accurate analysis of the damage can be performed.

[0114] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit increases the frequency of data collection and provides information in real time. If the user is relaxed, the collection unit maintains the normal frequency of data collection. If the user is facing an emergency, the collection unit immediately starts data collection and quickly provides information. This allows the timing of data collection to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0115] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit provides a simple, highly visible analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is facing an emergency, the analysis unit provides a quick, concise analysis result. This allows the way the analysis result is presented to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0116] The warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the warning unit issues a simple, highly visible warning. If the user is relaxed, the warning unit issues a detailed warning. If the user is facing an emergency, the warning unit issues a quick, to-the-point warning. This allows the way the warning is expressed to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input user emotion data into the generation AI and have the generation AI adjust the way the warning is expressed.

[0117] The notification unit can estimate the user's emotions and adjust the notification presentation style based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit issues a simple, highly visible notification. If the user is feeling relaxed, the notification unit issues a detailed notification. If the user is facing an emergency, the notification unit issues a quick, to-the-point notification. This allows the notification presentation style to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification presentation style.

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

[0119] Step 1: The collection unit collects data related to natural disasters from sensors or databases. Data related to natural disasters includes earthquake data, weather data, flood data, etc. The collection unit collects data from seismometers, weather observation devices, satellite data, and drones. Seismometers include short-period seismometers and long-period seismometers, and weather observation devices include anemometers and rain gauges. Satellite data includes weather satellite data and remote sensing data, and drones include fixed-wing drones and multi-rotor drones. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies patterns related to natural disasters. The analysis unit uses deep learning and recurrent neural networks (RNNs) to identify patterns related to natural disasters in the data. Deep learning can use algorithms such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), while recurrent neural networks (RNNs) can use algorithms such as long short-term memory (LSTMs) and gated recurrent units (GRUs). Step 3: The alarm unit issues an alert based on the pattern identified by the analysis unit. The alarm unit issues an early warning by voice or text message based on the identified pattern, including the language of the voice message and the format of the text message. Step 4: The notification unit notifies residents of the alert issued by the warning unit. The notification unit notifies residents of the alert using means such as SMS, email, or app notification.

[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 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.

[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 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.

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

[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 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.

[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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," 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. a collection unit that collects data related to natural disasters from a sensor or a database; an analysis unit that analyzes the data collected by the collection unit and identifies patterns of natural disasters; an alarm unit that issues an alarm based on the pattern identified by the analysis unit; a notification unit that notifies residents of the warning issued by the warning unit. A system characterized by:

2. The collecting unit Collect data from seismometers, weather stations, satellites, and drones 2. The system of claim 1.

3. The analysis unit Identifying patterns in natural disasters using deep learning or recurrent neural networks 2. The system of claim 1.

4. The alarm unit Issue voice or text message alerts based on identified patterns 2. The system of claim 1.

5. The notification unit Notifying residents of the warning 2. The system of claim 1.

6. The analysis unit Analyzing damage caused by natural disasters and prioritizing rescue efforts 2. The system of claim 1.

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

8. The collecting unit Analyze past natural disaster data and select the most appropriate data collection method 2. The system of claim 1.

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