system

Abstract

The system according to this embodiment aims to provide disaster prediction and advice on specific evacuation plans and preparedness measures. [Solution] The system according to the embodiment comprises an analysis unit, a prediction unit, a provision unit, and an alert unit. The analysis unit analyzes past disaster data and current observation data. The prediction unit makes disaster predictions based on the data analyzed by the analysis unit. The provision unit provides specific evacuation plans and preparedness advice based on the prediction information obtained by the prediction unit. The alert unit provides real-time alerts based on the advice provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, disaster prediction and the provision of evacuation plans have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide disaster prediction and specific evacuation plans and advice on preparations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a prediction unit, a provision unit, and an alert unit. The analysis unit analyzes past disaster data and current observation data. The prediction unit makes disaster predictions based on the data analyzed by the analysis unit. The provision unit provides specific evacuation plans and preparedness advice based on the prediction information obtained by the prediction unit. The alert unit provides real-time alerts based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide disaster prediction and advice on specific evacuation plans and preparedness measures. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The disaster prediction system according to an embodiment of the present invention is a system that predicts disasters by analyzing past disaster data and current observation data. This disaster prediction system analyzes past disaster data and current observation data to predict disasters. Next, it provides the user with specific evacuation plans and preparation advice based on the prediction information. Furthermore, it provides the user with real-time alerts in response to increasing risks and prompts them to take necessary measures. This system combines a large-scale language model (LLM) and machine learning algorithms to analyze past earthquake and weather data and learn patterns. Using an automated learning system, it monitors new earthquake signs and climate change data in real time and detects anomalies. Furthermore, it uses computer vision technology to analyze satellite images and drone footage to detect topographic changes and new risks. The disaster prediction system notifies the user of disaster predictions and countermeasures in an easy-to-understand manner using natural language. For example, the disaster prediction system analyzes past disaster data and current observation data. For example, the disaster prediction system analyzes past earthquake data and weather data to predict disasters. Next, the disaster prediction system provides the user with specific evacuation plans and preparation advice based on the prediction information. For example, a disaster prediction system provides suggestions for evacuation routes and lists of emergency supplies. Furthermore, it provides users with real-time alerts in response to increasing risks, prompting them to take necessary measures. For instance, if an earthquake is predicted, the system provides users with an alert urging them to evacuate. This allows the disaster prediction system to handle everything from disaster prediction to evacuation planning and alert provision in a consistent manner.

[0029] The disaster prediction system according to this embodiment comprises an analysis unit, a prediction unit, a provision unit, and an alert unit. The analysis unit analyzes past disaster data and current observation data. For example, the analysis unit analyzes past earthquake data and meteorological data. The analysis unit can analyze data using data preprocessing methods and analysis algorithms. For example, the analysis unit cleans and normalizes the data and converts it into a format suitable for analysis. The analysis unit can learn data patterns using machine learning algorithms and use this to predict disasters. The prediction unit makes disaster predictions based on the data analyzed by the analysis unit. For example, the prediction unit predicts the occurrence of disasters using prediction models and prediction algorithms. The prediction unit can take past disaster data and current observation data as input and predict the probability and timing of disaster occurrence. The prediction unit transmits the prediction results to the provision unit, which provides specific evacuation plans and preparedness advice based on the prediction results. For example, the provision unit provides suggestions for evacuation routes and lists of stockpiled supplies. The provision unit can notify users of disaster predictions and countermeasures in an easy-to-understand natural language. The provisioning unit can use natural language processing technology to notify users of disaster prediction results and countermeasures. The alerting unit provides real-time alerts based on the advice provided by the provisioning unit. For example, the alerting unit provides real-time alerts in response to increased risk. The alerting unit can provide users with alerts urging them to evacuate when a disaster is predicted to occur. The alerting unit can set the type of alert and notification method to provide users with appropriate alerts. As a result, the disaster prediction system according to this embodiment can consistently handle everything from disaster prediction to evacuation planning and alert provision.

[0030] The analysis unit analyzes past disaster data and current observational data. Specifically, it collects past earthquake and meteorological data and preprocesses this data. Preprocessing includes data cleaning and normalization, such as imputing missing values ​​and removing outliers. This allows the data to be converted into a format suitable for analysis. The analysis unit uses machine learning algorithms to learn patterns in the data. For example, it analyzes earthquake frequency, epicenter distribution, and temperature and precipitation fluctuation patterns in meteorological data. This allows it to extract features related to disaster occurrence and use them to build prediction models. Furthermore, the analysis unit performs time-series analysis of the data to identify precursor phenomena and trends from past disasters. This provides important indicators for predicting disaster occurrence. Based on these analysis results, the analysis unit evaluates the risk of disaster occurrence and provides this information to the prediction unit.

[0031] The prediction unit predicts disasters based on data analyzed by the analysis unit. Specifically, it uses prediction models and algorithms to predict the probability and timing of disaster occurrence. The prediction unit takes past disaster data and current observation data as input and simulates the occurrence of disasters based on this data. For example, in earthquake prediction, it takes data such as the location of the epicenter, magnitude, and frequency as input and calculates the probability of an earthquake occurring. In weather disaster prediction, it takes weather data such as temperature, precipitation, and wind speed as input and evaluates the risk of heavy rain and typhoons occurring. The prediction unit transmits these prediction results to the provision unit, which then provides specific evacuation plans and preparedness advice based on the prediction results. The prediction unit continuously improves its models and updates its data to improve the accuracy of its prediction results. This allows the prediction unit to always provide highly accurate disaster predictions based on the latest information.

[0032] The service provider provides users with specific evacuation plans and preparedness advice based on prediction results transmitted from the prediction unit. Specifically, it provides suggestions for evacuation routes and lists of emergency supplies. The service provider can notify users of disaster predictions and countermeasures in an easy-to-understand manner using natural language. For example, it suggests the optimal evacuation route based on the user's current location and evacuation destination. It also provides a list of necessary emergency supplies according to the type and scale of the disaster, helping users to make appropriate preparations. The service provider uses natural language processing technology to convey information to users in an easy-to-understand manner. This allows users to easily understand disaster prediction results and countermeasures and take action quickly. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and usefulness of the information it provides. This enables the service provider to provide users with reliable information and support appropriate actions during disasters.

[0033] The alert unit provides real-time alerts based on advice provided by the service provider. Specifically, it provides real-time alerts in response to increasing risks. The alert unit can provide users with alerts urging them to evacuate when a disaster is predicted. For example, if an earthquake is predicted, it will immediately send an alert to the user urging them to evacuate. In the case of weather-related disasters, it will provide alerts notifying users of the need to evacuate as heavy rain or typhoons approach. The alert unit can configure the type of alert and notification method to provide users with appropriate alerts. For example, it can reliably transmit information using multiple communication methods, such as smartphone push notifications, voice alerts, and email notifications. This allows the alert unit to provide users with alerts urging them to evacuate quickly and reliably, minimizing the risk of disaster. Furthermore, the alert unit can provide individually customized alerts based on the user's location and situation. This enables the alert unit to provide effective alerts to maximize user safety.

[0034] The analysis unit can analyze satellite imagery and drone footage using computer vision technology to detect topographic changes and new risks. For example, the analysis unit can analyze satellite imagery to detect topographic changes. The analysis unit can use image recognition algorithms to detect topographic changes from satellite imagery. The analysis unit can also analyze drone footage to detect new risks. The analysis unit can use object detection technology to detect new risks from drone footage. The analysis unit transmits the analysis results of the satellite imagery and drone footage to the prediction unit, which then makes disaster predictions based on the analysis results. This improves the accuracy of disaster prediction by detecting topographic changes and new risks. Computer vision technology is implemented, for example, by image recognition algorithms and object detection technology. For satellite imagery and drone footage, for example, high-resolution images and videos can be acquired and used for analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input satellite imagery and drone footage into the generative AI and have the generative AI perform the detection of topographic changes and new risks.

[0035] The service provider can notify users of disaster predictions and countermeasures in an easy-to-understand manner using natural language. For example, the service provider can notify users of disaster prediction results in natural language. The service provider can use natural language processing technology to notify users of disaster prediction results in an easy-to-understand manner. The service provider can also notify users of disaster countermeasures in natural language. The service provider can use natural language processing technology to notify users of disaster prediction results and countermeasures. This makes it easier for users to understand disaster predictions and countermeasures. Natural language is implemented, for example, by the language used or by natural language processing technology. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can input disaster prediction results and countermeasures into generative AI and have the generative AI execute notifications in natural language.

[0036] The alert unit can provide real-time alerts in response to increasing risks. For example, the alert unit can provide an alert urging users to evacuate when a disaster is predicted. The alert unit can provide real-time alerts in response to increasing risks. The alert unit can set the type of alert and notification method to provide appropriate alerts to users. This allows users to respond quickly in response to increasing risks. Real-time alerts are implemented, for example, by setting the type of alert and notification method. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alert unit can have a generative AI perform the task of providing alerts in response to increasing risks.

[0037] The analysis unit can prioritize the analysis of data that meets specific conditions from past disaster data. For example, the analysis unit can prioritize the analysis of disaster data that occurred in the same region from past disaster data. The analysis unit can prioritize the analysis of disaster data that occurred in the same season from past disaster data. The analysis unit can prioritize the analysis of disaster data of the same scale from past disaster data. This improves the accuracy of the analysis by prioritizing the analysis of data that meets specific conditions. The specific conditions can be set by, for example, the type of disaster, the time of occurrence, the scale of damage, etc. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input past disaster data into a generation AI and have the generation AI perform a priority analysis of data that meets specific conditions.

[0038] The analysis unit can adjust the current frequency of data acquisition to improve the accuracy of the analysis. For example, the analysis unit can increase the frequency of data acquisition to perform real-time analysis. The analysis unit can increase the frequency of data acquisition during specific time periods to improve the accuracy of the analysis. The analysis unit can automatically increase the frequency of data acquisition when an anomaly is detected. This improves the accuracy of the analysis by adjusting the frequency of data acquisition. The frequency of data acquisition is set by, for example, the data acquisition interval and real-time performance. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can have a generation AI perform the adjustment of the data acquisition frequency.

[0039] The analysis unit can analyze satellite imagery and drone footage, as well as integrate and analyze data from ground sensors. For example, the analysis unit can integrate satellite imagery and ground sensor data to perform a more detailed analysis. The analysis unit can integrate drone footage and ground sensor data to perform real-time analysis. The analysis unit can integrate satellite imagery, drone footage, and ground sensor data to perform a comprehensive analysis. This improves the accuracy of the analysis by integrating and analyzing data from ground sensors. Ground sensors can be implemented, for example, by specifying the sensor installation location or data collection method. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input satellite imagery, drone footage, and ground sensor data into a generative AI and have the generative AI perform a comprehensive analysis.

[0040] The analysis unit can compare and analyze disaster data from different regions and extract common patterns. For example, the analysis unit can compare disaster data from different regions and extract common patterns. The analysis unit can compare disaster data from different regions and extract common patterns under specific conditions. The analysis unit can compare disaster data from different regions and identify common risk factors. This allows common patterns to be extracted by comparing and analyzing disaster data from different regions. Common patterns are extracted, for example, by pattern recognition algorithms or similarity evaluation. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input disaster data from different regions into a generative AI and have the generative AI perform the extraction of common patterns.

[0041] The prediction unit can improve the accuracy of its predictions by referring to past disaster patterns during the prediction process. For example, the prediction unit can improve the accuracy of its predictions by referring to past disaster patterns. Based on past disaster patterns, the prediction unit can improve the accuracy of its predictions under specific conditions. The prediction unit can analyze past disaster patterns and optimize its prediction algorithm. This improves the accuracy of predictions by referring to past disaster patterns. Past disaster patterns are referenced, for example, in database construction and pattern matching. Some or all of the above processes in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input past disaster patterns into a generative AI and have the generative AI perform the task of improving the accuracy of its predictions.

[0042] The prediction unit can apply different prediction algorithms to different types of disasters during the prediction process. For example, the prediction unit can apply a prediction algorithm specifically for earthquakes to predict earthquakes, a prediction algorithm specifically for typhoons to predict typhoons, and a prediction algorithm specifically for floods to predict floods. By applying different prediction algorithms to different types of disasters, the accuracy of the prediction is improved. These different prediction algorithms may include, for example, weather prediction algorithms and earthquake prediction algorithms. Some or all of the above-described processes in the prediction unit may be performed using, for example, generative AI, or without using generative AI. For example, the prediction unit can have the generative AI perform the application of different prediction algorithms to different types of disasters.

[0043] The prediction unit can assess regional risks by considering geographical characteristics during the prediction process. For example, the prediction unit assesses regional risks by considering geographical characteristics. Based on geographical characteristics, the prediction unit can assess the risks of a specific region in detail. The prediction unit can analyze geographical characteristics and optimize regional risk assessments. This improves the accuracy of regional risk assessments by considering geographical characteristics. Geographical characteristics include, for example, topographic information and population density. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input geographical characteristics into a generative AI and have the generative AI perform regional risk assessments.

[0044] The prediction unit can perform complex disaster predictions by integrating meteorological and seismic data during the prediction process. For example, the prediction unit can integrate meteorological and seismic data to perform complex disaster predictions. Based on meteorological and seismic data, the prediction unit can evaluate complex disaster risks. The prediction unit can analyze meteorological and seismic data and optimize the complex disaster prediction algorithm. This improves the accuracy of complex disaster predictions by integrating meteorological and seismic data. Meteorological and seismic data are integrated, for example, through data format and integration algorithms. Some or all of the above processing in the prediction unit may be performed using, for example, generative AI, or without generative AI. For example, the prediction unit can have generative AI perform the integration of meteorological and seismic data.

[0045] The service provider can propose an optimal evacuation plan by referring to the user's past evacuation history at the time of provision. For example, the service provider can propose an optimal evacuation plan based on the user's past evacuation history. The service provider can propose an optimal evacuation plan under specific conditions based on the user's past evacuation history. The service provider can analyze the user's past evacuation history and propose the most efficient evacuation plan. In this way, an optimal evacuation plan can be proposed by referring to the user's past evacuation history. Past evacuation history is referenced, for example, in the construction of a database or in the method of acquiring historical data. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's past evacuation history into a generative AI and have the generative AI execute the proposal of an optimal evacuation plan.

[0046] The service provider can customize preparedness advice based on the user's current living situation at the time of delivery. For example, the service provider can customize preparedness advice considering the user's current living situation. The service provider can customize preparedness advice under specific conditions based on the user's current living situation. The service provider can analyze the user's current living situation and customize the most appropriate preparedness advice. This allows for the provision of more appropriate preparedness advice by customizing the advice based on the user's current living situation. The current living situation may be considered, for example, family structure or health status. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's current living situation into a generative AI and have the generative AI perform the customization of preparedness advice.

[0047] The service provider can propose the optimal evacuation route at the time of provision, taking into account the user's geographical location information. For example, the service provider can propose the optimal evacuation route based on the user's geographical location information. The service provider can propose the optimal evacuation route under specific conditions, taking into account the user's geographical location information. The service provider can analyze the user's geographical location information and propose the most efficient evacuation route. In this way, the optimal evacuation route can be proposed by taking into account the user's geographical location information. Geographical location information is obtained, for example, from GPS data or map information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's geographical location information into a generative AI and have the generative AI propose the optimal evacuation route.

[0048] The service provider can analyze the user's social media activity and provide relevant advice at the time of service provision. For example, the service provider can analyze the user's social media activity and provide relevant advice. Based on the user's social media activity, the service provider can provide relevant advice under specific conditions. The service provider can analyze the user's social media activity and provide the most appropriate advice. In this way, relevant advice can be provided by analyzing the user's social media activity. Social media activity is analyzed, for example, by analyzing the content of posts or the user's behavior patterns. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity into a generative AI and have the generative AI perform the task of providing relevant advice.

[0049] The alert unit can select the optimal alert method when an alert occurs by referring to the user's past response history. For example, the alert unit selects the optimal alert method based on the user's past response history. The alert unit can select the optimal alert method under specific conditions from the user's past response history. The alert unit can analyze the user's past response history and select the most effective alert method. This allows the optimal alert method to be selected by referring to the user's past response history. Past response history is referenced, for example, in database construction and methods for acquiring historical data. Some or all of the above processing in the alert unit may be performed using, for example, a generation AI, or without a generation AI. For example, the alert unit can input the user's past response history into a generation AI and have the generation AI select the optimal alert method.

[0050] The alert unit can provide the optimal alert method when an alert occurs, taking into account the user's device information. For example, if the user is using a smartphone, the alert unit can provide an alert using a push notification. If the user is using a tablet, the alert unit can provide an alert optimized for a larger screen. If the user is using a smartwatch, the alert unit can provide an alert using vibration or sound. This allows the system to provide the optimal alert method by taking into account the user's device information. Device information is obtained, for example, from the type of device and usage status. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alert unit can input the user's device information into a generative AI and have the generative AI perform the task of providing the optimal alert method.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The analysis unit can prioritize the analysis of data that meets specific criteria from past disaster data. For example, it can prioritize the analysis of disaster data that occurred in the same region. It can prioritize the analysis of disaster data that occurred in the same season from past disaster data. It can prioritize the analysis of disaster data of the same magnitude from past disaster data. By prioritizing the analysis of data that meets specific criteria, the accuracy of the analysis is improved.

[0053] The service provider can propose the optimal evacuation plan by referring to the user's past evacuation history at the time of provision. For example, it can propose the optimal evacuation plan based on the user's past evacuation history. It can propose the optimal evacuation plan under specific conditions based on the user's past evacuation history. It can analyze the user's past evacuation history and propose the most efficient evacuation plan. In this way, the optimal evacuation plan can be proposed by referring to the user's past evacuation history.

[0054] The alerting unit can select the optimal alerting method by referring to the user's past response history when an alert occurs. For example, it can select the optimal alerting method based on the user's past response history. It can select the optimal alerting method under specific conditions from the user's past response history. It can analyze the user's past response history and select the most effective alerting method. In this way, the optimal alerting method can be selected by referring to the user's past response history.

[0055] The analysis unit can adjust the current data acquisition frequency to improve the accuracy of the analysis. For example, it can increase the data acquisition frequency to perform real-time analysis. It can also increase the data acquisition frequency during specific time periods to improve the accuracy of the analysis. It can automatically increase the data acquisition frequency when an anomaly is detected. In this way, adjusting the data acquisition frequency improves the accuracy of the analysis.

[0056] The prediction unit can improve prediction accuracy by referring to past disaster patterns during the prediction process. For example, it can improve prediction accuracy by referring to past disaster patterns. Based on past disaster patterns, it can improve prediction accuracy under specific conditions. It can analyze past disaster patterns and optimize the prediction algorithm. As a result, prediction accuracy is improved by referring to past disaster patterns.

[0057] The prediction unit can apply different prediction algorithms to different types of disasters during the prediction process. For example, a prediction algorithm specifically for earthquakes can be applied to earthquake predictions. A prediction algorithm specifically for typhoons can be applied to typhoon predictions. A prediction algorithm specifically for floods can be applied to flood predictions. By applying different prediction algorithms to different types of disasters, the accuracy of the predictions can be improved.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The analysis unit analyzes past disaster data and current observation data. For example, the analysis unit analyzes past earthquake data and weather data, and analyzes the data using data preprocessing methods and analysis algorithms. Specifically, it cleans and normalizes the data and converts it into a format suitable for analysis. Furthermore, it can learn data patterns using machine learning algorithms and use this to help predict disasters. Step 2: The prediction unit predicts disasters based on the data analyzed by the analysis unit. The prediction unit predicts the occurrence of disasters using, for example, prediction models or prediction algorithms. The prediction unit can take past disaster data and current observation data as input and predict the probability and timing of disaster occurrences. Step 3: The service provider provides specific evacuation plans and preparedness advice based on the prediction information obtained by the prediction unit. For example, the service provider may provide suggestions for evacuation routes and lists of emergency supplies. The service provider can use natural language processing technology to inform users of disaster predictions and countermeasures in an easy-to-understand manner using natural language. Step 4: The alert unit provides real-time alerts based on the advice provided by the service unit. For example, the alert unit provides real-time alerts in response to increased risk. The alert unit can provide users with alerts urging them to evacuate when a disaster is predicted. The alert unit can configure the type of alert and notification method to provide users with appropriate alerts.

[0060] (Example of form 2) The disaster prediction system according to an embodiment of the present invention is a system that predicts disasters by analyzing past disaster data and current observation data. This disaster prediction system analyzes past disaster data and current observation data to predict disasters. Next, it provides the user with specific evacuation plans and preparation advice based on the prediction information. Furthermore, it provides the user with real-time alerts in response to increasing risks and prompts them to take necessary measures. This system combines a large-scale language model (LLM) and machine learning algorithms to analyze past earthquake and weather data and learn patterns. Using an automated learning system, it monitors new earthquake signs and climate change data in real time and detects anomalies. Furthermore, it uses computer vision technology to analyze satellite images and drone footage to detect topographic changes and new risks. The disaster prediction system notifies the user of disaster predictions and countermeasures in an easy-to-understand manner using natural language. For example, the disaster prediction system analyzes past disaster data and current observation data. For example, the disaster prediction system analyzes past earthquake data and weather data to predict disasters. Next, the disaster prediction system provides the user with specific evacuation plans and preparation advice based on the prediction information. For example, a disaster prediction system provides suggestions for evacuation routes and lists of emergency supplies. Furthermore, it provides users with real-time alerts in response to increasing risks, prompting them to take necessary measures. For instance, if an earthquake is predicted, the system provides users with an alert urging them to evacuate. This allows the disaster prediction system to handle everything from disaster prediction to evacuation planning and alert provision in a consistent manner.

[0061] The disaster prediction system according to this embodiment comprises an analysis unit, a prediction unit, a provision unit, and an alert unit. The analysis unit analyzes past disaster data and current observation data. For example, the analysis unit analyzes past earthquake data and meteorological data. The analysis unit can analyze data using data preprocessing methods and analysis algorithms. For example, the analysis unit cleans and normalizes the data and converts it into a format suitable for analysis. The analysis unit can learn data patterns using machine learning algorithms and use this to predict disasters. The prediction unit makes disaster predictions based on the data analyzed by the analysis unit. For example, the prediction unit predicts the occurrence of disasters using prediction models and prediction algorithms. The prediction unit can take past disaster data and current observation data as input and predict the probability and timing of disaster occurrence. The prediction unit transmits the prediction results to the provision unit, which provides specific evacuation plans and preparedness advice based on the prediction results. For example, the provision unit provides suggestions for evacuation routes and lists of stockpiled supplies. The provision unit can notify users of disaster predictions and countermeasures in an easy-to-understand natural language. The provisioning unit can use natural language processing technology to notify users of disaster prediction results and countermeasures. The alerting unit provides real-time alerts based on the advice provided by the provisioning unit. For example, the alerting unit provides real-time alerts in response to increased risk. The alerting unit can provide users with alerts urging them to evacuate when a disaster is predicted to occur. The alerting unit can set the type of alert and notification method to provide users with appropriate alerts. As a result, the disaster prediction system according to this embodiment can consistently handle everything from disaster prediction to evacuation planning and alert provision.

[0062] The analysis unit analyzes past disaster data and current observational data. Specifically, it collects past earthquake and meteorological data and preprocesses this data. Preprocessing includes data cleaning and normalization, such as imputing missing values ​​and removing outliers. This allows the data to be converted into a format suitable for analysis. The analysis unit uses machine learning algorithms to learn patterns in the data. For example, it analyzes earthquake frequency, epicenter distribution, and temperature and precipitation fluctuation patterns in meteorological data. This allows it to extract features related to disaster occurrence and use them to build prediction models. Furthermore, the analysis unit performs time-series analysis of the data to identify precursor phenomena and trends from past disasters. This provides important indicators for predicting disaster occurrence. Based on these analysis results, the analysis unit evaluates the risk of disaster occurrence and provides this information to the prediction unit.

[0063] The prediction unit predicts disasters based on data analyzed by the analysis unit. Specifically, it uses prediction models and algorithms to predict the probability and timing of disaster occurrence. The prediction unit takes past disaster data and current observation data as input and simulates the occurrence of disasters based on this data. For example, in earthquake prediction, it takes data such as the location of the epicenter, magnitude, and frequency as input and calculates the probability of an earthquake occurring. In weather disaster prediction, it takes weather data such as temperature, precipitation, and wind speed as input and evaluates the risk of heavy rain and typhoons occurring. The prediction unit transmits these prediction results to the provision unit, which then provides specific evacuation plans and preparedness advice based on the prediction results. The prediction unit continuously improves its models and updates its data to improve the accuracy of its prediction results. This allows the prediction unit to always provide highly accurate disaster predictions based on the latest information.

[0064] The service provider provides users with specific evacuation plans and preparedness advice based on prediction results transmitted from the prediction unit. Specifically, it provides suggestions for evacuation routes and lists of emergency supplies. The service provider can notify users of disaster predictions and countermeasures in an easy-to-understand manner using natural language. For example, it suggests the optimal evacuation route based on the user's current location and evacuation destination. It also provides a list of necessary emergency supplies according to the type and scale of the disaster, helping users to make appropriate preparations. The service provider uses natural language processing technology to convey information to users in an easy-to-understand manner. This allows users to easily understand disaster prediction results and countermeasures and take action quickly. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and usefulness of the information it provides. This enables the service provider to provide users with reliable information and support appropriate actions during disasters.

[0065] The alert unit provides real-time alerts based on advice provided by the service provider. Specifically, it provides real-time alerts in response to increasing risks. The alert unit can provide users with alerts urging them to evacuate when a disaster is predicted. For example, if an earthquake is predicted, it will immediately send an alert to the user urging them to evacuate. In the case of weather-related disasters, it will provide alerts notifying users of the need to evacuate as heavy rain or typhoons approach. The alert unit can configure the type of alert and notification method to provide users with appropriate alerts. For example, it can reliably transmit information using multiple communication methods, such as smartphone push notifications, voice alerts, and email notifications. This allows the alert unit to provide users with alerts urging them to evacuate quickly and reliably, minimizing the risk of disaster. Furthermore, the alert unit can provide individually customized alerts based on the user's location and situation. This enables the alert unit to provide effective alerts to maximize user safety.

[0066] The analysis unit can analyze satellite imagery and drone footage using computer vision technology to detect topographic changes and new risks. For example, the analysis unit can analyze satellite imagery to detect topographic changes. The analysis unit can use image recognition algorithms to detect topographic changes from satellite imagery. The analysis unit can also analyze drone footage to detect new risks. The analysis unit can use object detection technology to detect new risks from drone footage. The analysis unit transmits the analysis results of the satellite imagery and drone footage to the prediction unit, which then makes disaster predictions based on the analysis results. This improves the accuracy of disaster prediction by detecting topographic changes and new risks. Computer vision technology is implemented, for example, by image recognition algorithms and object detection technology. For satellite imagery and drone footage, for example, high-resolution images and videos can be acquired and used for analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input satellite imagery and drone footage into the generative AI and have the generative AI perform the detection of topographic changes and new risks.

[0067] The service provider can notify users of disaster predictions and countermeasures in an easy-to-understand manner using natural language. For example, the service provider can notify users of disaster prediction results in natural language. The service provider can use natural language processing technology to notify users of disaster prediction results in an easy-to-understand manner. The service provider can also notify users of disaster countermeasures in natural language. The service provider can use natural language processing technology to notify users of disaster prediction results and countermeasures. This makes it easier for users to understand disaster predictions and countermeasures. Natural language is implemented, for example, by the language used or by natural language processing technology. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can input disaster prediction results and countermeasures into generative AI and have the generative AI execute notifications in natural language.

[0068] The alert unit can provide real-time alerts in response to increasing risks. For example, the alert unit can provide an alert urging users to evacuate when a disaster is predicted. The alert unit can provide real-time alerts in response to increasing risks. The alert unit can set the type of alert and notification method to provide appropriate alerts to users. This allows users to respond quickly in response to increasing risks. Real-time alerts are implemented, for example, by setting the type of alert and notification method. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alert unit can have a generative AI perform the task of providing alerts in response to increasing risks.

[0069] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can display the results in a simple and easy-to-understand format. If the user is calm, the analysis unit can display the results including detailed data and graphs. If the user is excited, the analysis unit can display the results using visually appealing infographics. By adjusting how the analysis results are displayed according to the user's emotions, the user can more easily understand the results. The user's emotions are estimated, for example, using an emotion recognition algorithm or an emotion rating scale. Some or all of the above processing in the analysis unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0070] The analysis unit can prioritize the analysis of data that meets specific conditions from past disaster data. For example, the analysis unit can prioritize the analysis of disaster data that occurred in the same region from past disaster data. The analysis unit can prioritize the analysis of disaster data that occurred in the same season from past disaster data. The analysis unit can prioritize the analysis of disaster data of the same scale from past disaster data. This improves the accuracy of the analysis by prioritizing the analysis of data that meets specific conditions. The specific conditions can be set by, for example, the type of disaster, the time of occurrence, the scale of damage, etc. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input past disaster data into a generation AI and have the generation AI perform a priority analysis of data that meets specific conditions.

[0071] The analysis unit can adjust the current frequency of data acquisition to improve the accuracy of the analysis. For example, the analysis unit can increase the frequency of data acquisition to perform real-time analysis. The analysis unit can increase the frequency of data acquisition during specific time periods to improve the accuracy of the analysis. The analysis unit can automatically increase the frequency of data acquisition when an anomaly is detected. This improves the accuracy of the analysis by adjusting the frequency of data acquisition. The frequency of data acquisition is set by, for example, the data acquisition interval and real-time performance. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can have a generation AI perform the adjustment of the data acquisition frequency.

[0072] The analysis unit can estimate the user's emotions and adjust the importance of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can highlight important information. If the user is calm, the analysis unit can display all information equally. If the user is excited, the analysis unit can display important information in a visually appealing format. This ensures that the user does not miss important information by adjusting the importance of the analysis results according to their emotions. The importance of the analysis results is evaluated, for example, by an importance score or priority setting. The user's emotions are estimated, for example, by an emotion recognition algorithm or an emotion rating scale. Some or all of the above processing in the analysis unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0073] The analysis unit can analyze satellite imagery and drone footage, as well as integrate and analyze data from ground sensors. For example, the analysis unit can integrate satellite imagery and ground sensor data to perform a more detailed analysis. The analysis unit can integrate drone footage and ground sensor data to perform real-time analysis. The analysis unit can integrate satellite imagery, drone footage, and ground sensor data to perform a comprehensive analysis. This improves the accuracy of the analysis by integrating and analyzing data from ground sensors. Ground sensors can be implemented, for example, by specifying the sensor installation location or data collection method. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input satellite imagery, drone footage, and ground sensor data into a generative AI and have the generative AI perform a comprehensive analysis.

[0074] The analysis unit can compare and analyze disaster data from different regions and extract common patterns. For example, the analysis unit can compare disaster data from different regions and extract common patterns. The analysis unit can compare disaster data from different regions and extract common patterns under specific conditions. The analysis unit can compare disaster data from different regions and identify common risk factors. This allows common patterns to be extracted by comparing and analyzing disaster data from different regions. Common patterns are extracted, for example, by pattern recognition algorithms or similarity evaluation. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input disaster data from different regions into a generative AI and have the generative AI perform the extraction of common patterns.

[0075] The prediction unit can estimate the user's emotions and adjust the presentation of the prediction results based on the estimated emotions. For example, if the user is feeling anxious, the prediction unit can display the prediction results in a simple and easy-to-understand format. If the user is calm, the prediction unit can display the prediction results including detailed data and graphs. If the user is excited, the prediction unit can display the prediction results using visually appealing infographics. This makes it easier for the user to understand the prediction results by adjusting the presentation of the prediction results according to the user's emotions. The presentation of the prediction results can be adjusted, for example, by visualization methods or linguistic expressions. The user's emotions can be estimated, for example, by emotion recognition algorithms or emotion rating scales. Some or all of the above processing in the prediction unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0076] The prediction unit can improve the accuracy of its predictions by referring to past disaster patterns during the prediction process. For example, the prediction unit can improve the accuracy of its predictions by referring to past disaster patterns. Based on past disaster patterns, the prediction unit can improve the accuracy of its predictions under specific conditions. The prediction unit can analyze past disaster patterns and optimize its prediction algorithm. This improves the accuracy of predictions by referring to past disaster patterns. Past disaster patterns are referenced, for example, in database construction and pattern matching. Some or all of the above processes in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input past disaster patterns into a generative AI and have the generative AI perform the task of improving the accuracy of its predictions.

[0077] The prediction unit can apply different prediction algorithms to different types of disasters during the prediction process. For example, the prediction unit can apply a prediction algorithm specifically for earthquakes to predict earthquakes, a prediction algorithm specifically for typhoons to predict typhoons, and a prediction algorithm specifically for floods to predict floods. By applying different prediction algorithms to different types of disasters, the accuracy of the prediction is improved. These different prediction algorithms may include, for example, weather prediction algorithms and earthquake prediction algorithms. Some or all of the above-described processes in the prediction unit may be performed using, for example, generative AI, or without using generative AI. For example, the prediction unit can have the generative AI perform the application of different prediction algorithms to different types of disasters.

[0078] The prediction unit can estimate the user's emotions and prioritize prediction results based on the estimated emotions. For example, if the user is feeling anxious, the prediction unit will prioritize displaying important prediction results. If the user is calm, the prediction unit can display all prediction results equally. If the user is excited, the prediction unit can display important prediction results in a visually appealing format. This ensures that the user does not miss important prediction results by prioritizing them according to their emotions. The priority of prediction results is determined, for example, by an importance score or risk assessment. The user's emotions are estimated, for example, by an emotion recognition algorithm or an emotion rating scale. Some or all of the above processing in the prediction unit is implemented using emotion estimation functions, for example, by an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0079] The prediction unit can assess regional risks by considering geographical characteristics during the prediction process. For example, the prediction unit assesses regional risks by considering geographical characteristics. Based on geographical characteristics, the prediction unit can assess the risks of a specific region in detail. The prediction unit can analyze geographical characteristics and optimize regional risk assessments. This improves the accuracy of regional risk assessments by considering geographical characteristics. Geographical characteristics include, for example, topographic information and population density. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input geographical characteristics into a generative AI and have the generative AI perform regional risk assessments.

[0080] The prediction unit can perform complex disaster predictions by integrating meteorological and seismic data during the prediction process. For example, the prediction unit can integrate meteorological and seismic data to perform complex disaster predictions. Based on meteorological and seismic data, the prediction unit can evaluate complex disaster risks. The prediction unit can analyze meteorological and seismic data and optimize the complex disaster prediction algorithm. This improves the accuracy of complex disaster predictions by integrating meteorological and seismic data. Meteorological and seismic data are integrated, for example, through data format and integration algorithms. Some or all of the above processing in the prediction unit may be performed using, for example, generative AI, or without generative AI. For example, the prediction unit can have generative AI perform the integration of meteorological and seismic data.

[0081] The service provider can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide advice in a simple and easy-to-understand format. If the user is calm, the service provider can provide advice that includes detailed data and graphs. If the user is agitated, the service provider can provide advice using visually appealing infographics. By adjusting the way advice is presented according to the user's emotions, it becomes easier for the user to understand the advice. The way advice is presented is adjusted, for example, through linguistic expression or visualization methods. The user's emotions are estimated, for example, using emotion recognition algorithms or emotion rating scales. Some or all of the above processing in the service provider is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The service provider can propose an optimal evacuation plan by referring to the user's past evacuation history at the time of provision. For example, the service provider can propose an optimal evacuation plan based on the user's past evacuation history. The service provider can propose an optimal evacuation plan under specific conditions based on the user's past evacuation history. The service provider can analyze the user's past evacuation history and propose the most efficient evacuation plan. In this way, an optimal evacuation plan can be proposed by referring to the user's past evacuation history. Past evacuation history is referenced, for example, in the construction of a database or in the method of acquiring historical data. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's past evacuation history into a generative AI and have the generative AI execute the proposal of an optimal evacuation plan.

[0083] The service provider can customize preparedness advice based on the user's current living situation at the time of delivery. For example, the service provider can customize preparedness advice considering the user's current living situation. The service provider can customize preparedness advice under specific conditions based on the user's current living situation. The service provider can analyze the user's current living situation and customize the most appropriate preparedness advice. This allows for the provision of more appropriate preparedness advice by customizing the advice based on the user's current living situation. The current living situation may be considered, for example, family structure or health status. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's current living situation into a generative AI and have the generative AI perform the customization of preparedness advice.

[0084] The service provider can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is feeling anxious, the service provider will prioritize important advice. If the user is calm, the service provider can provide all advice equally. If the user is agitated, the service provider can provide important advice in a visually appealing format. This ensures that the user does not miss important advice by prioritizing it according to their emotions. The priority of advice is determined, for example, by an importance score or risk assessment. The user's emotions are estimated, for example, by an emotion recognition algorithm or an emotion rating scale. Some or all of the above processing in the service provider is implemented using emotion estimation functions, for example, by an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The service provider can propose the optimal evacuation route at the time of provision, taking into account the user's geographical location information. For example, the service provider can propose the optimal evacuation route based on the user's geographical location information. The service provider can propose the optimal evacuation route under specific conditions, taking into account the user's geographical location information. The service provider can analyze the user's geographical location information and propose the most efficient evacuation route. In this way, the optimal evacuation route can be proposed by taking into account the user's geographical location information. Geographical location information is obtained, for example, from GPS data or map information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's geographical location information into a generative AI and have the generative AI propose the optimal evacuation route.

[0086] The service provider can analyze the user's social media activity and provide relevant advice at the time of service provision. For example, the service provider can analyze the user's social media activity and provide relevant advice. Based on the user's social media activity, the service provider can provide relevant advice under specific conditions. The service provider can analyze the user's social media activity and provide the most appropriate advice. In this way, relevant advice can be provided by analyzing the user's social media activity. Social media activity is analyzed, for example, by analyzing the content of posts or the user's behavior patterns. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity into a generative AI and have the generative AI perform the task of providing relevant advice.

[0087] The alert unit can estimate the user's emotions and adjust the way the alert is presented based on the estimated emotions. For example, if the user is feeling anxious, the alert unit can provide an alert in a simple and easy-to-understand format. If the user is calm, the alert unit can provide an alert that includes detailed data and graphs. If the user is agitated, the alert unit can provide an alert using a visually appealing infographic. This makes it easier for the user to understand the alert by adjusting the way it is presented according to their emotions. The way the alert is presented is adjusted, for example, by the notification method and language expression. The user's emotions are estimated, for example, by an emotion recognition algorithm or an emotion rating scale. Some or all of the above processing in the alert unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the alert unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0088] The alert unit can select the optimal alert method when an alert occurs by referring to the user's past response history. For example, the alert unit selects the optimal alert method based on the user's past response history. The alert unit can select the optimal alert method under specific conditions from the user's past response history. The alert unit can analyze the user's past response history and select the most effective alert method. This allows the optimal alert method to be selected by referring to the user's past response history. Past response history is referenced, for example, in database construction and methods for acquiring historical data. Some or all of the above processing in the alert unit may be performed using, for example, a generation AI, or without a generation AI. For example, the alert unit can input the user's past response history into a generation AI and have the generation AI select the optimal alert method.

[0089] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is feeling anxious, the alert unit will prioritize important alerts. If the user is calm, the alert unit can provide all alerts equally. If the user is agitated, the alert unit can provide important alerts in a visually appealing format. This ensures that the user does not miss important alerts by prioritizing them according to their emotions. Alert priority is determined, for example, by an importance score or risk assessment. The user's emotions are estimated, for example, by an emotion recognition algorithm or an emotion rating scale. Some or all of the above processing in the alert unit is implemented using emotion estimation functions, for example, by an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the alert unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0090] The alert unit can provide the optimal alert method when an alert occurs, taking into account the user's device information. For example, if the user is using a smartphone, the alert unit can provide an alert using a push notification. If the user is using a tablet, the alert unit can provide an alert optimized for a larger screen. If the user is using a smartwatch, the alert unit can provide an alert using vibration or sound. This allows the system to provide the optimal alert method by taking into account the user's device information. Device information is obtained, for example, from the type of device and usage status. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alert unit can input the user's device information into a generative AI and have the generative AI perform the task of providing the optimal alert method.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The analysis unit not only analyzes past disaster data and current observation data, but can also estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis results will be displayed in a simple and easy-to-understand format. If the user is calm, the analysis results can be displayed with detailed data and graphs. If the user is excited, the analysis results can be displayed using visually appealing infographics. In this way, by adjusting the display method of the analysis results according to the user's emotions, it becomes easier for the user to understand the analysis results.

[0093] The analysis unit can prioritize the analysis of data that meets specific criteria from past disaster data. For example, it can prioritize the analysis of disaster data that occurred in the same region. It can prioritize the analysis of disaster data that occurred in the same season from past disaster data. It can prioritize the analysis of disaster data of the same magnitude from past disaster data. By prioritizing the analysis of data that meets specific criteria, the accuracy of the analysis is improved.

[0094] The service provider can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is feeling anxious, the advice can be provided in a simple and easy-to-understand format. If the user is calm, the advice can include detailed data and graphs. If the user is agitated, the advice can be provided using visually appealing infographics. By adjusting the way advice is presented according to the user's emotions, it becomes easier for the user to understand the advice.

[0095] The service provider can propose the optimal evacuation plan by referring to the user's past evacuation history at the time of provision. For example, it can propose the optimal evacuation plan based on the user's past evacuation history. It can propose the optimal evacuation plan under specific conditions based on the user's past evacuation history. It can analyze the user's past evacuation history and propose the most efficient evacuation plan. In this way, the optimal evacuation plan can be proposed by referring to the user's past evacuation history.

[0096] The alert function can estimate the user's emotions and adjust the way the alert is presented based on those emotions. For example, if the user is feeling anxious, the alert can be presented in a simple and easy-to-understand format. If the user is calm, the alert can include detailed data and graphs. If the user is excited, the alert can be presented using visually appealing infographics. By adjusting the way the alert is presented according to the user's emotions, it becomes easier for the user to understand the alert.

[0097] The alerting unit can select the optimal alerting method by referring to the user's past response history when an alert occurs. For example, it can select the optimal alerting method based on the user's past response history. It can select the optimal alerting method under specific conditions from the user's past response history. It can analyze the user's past response history and select the most effective alerting method. In this way, the optimal alerting method can be selected by referring to the user's past response history.

[0098] The analysis unit can adjust the current data acquisition frequency to improve the accuracy of the analysis. For example, it can increase the data acquisition frequency to perform real-time analysis. It can also increase the data acquisition frequency during specific time periods to improve the accuracy of the analysis. It can automatically increase the data acquisition frequency when an anomaly is detected. In this way, adjusting the data acquisition frequency improves the accuracy of the analysis.

[0099] The prediction unit can estimate the user's emotions and adjust the presentation of the prediction results based on those emotions. For example, if the user is feeling anxious, the prediction results can be displayed in a simple and easy-to-understand format. If the user is calm, the prediction results can include detailed data and graphs. If the user is excited, the prediction results can be displayed using visually appealing infographics. By adjusting the presentation of the prediction results according to the user's emotions, the system makes it easier for the user to understand the prediction results.

[0100] The prediction unit can improve prediction accuracy by referring to past disaster patterns during the prediction process. For example, it can improve prediction accuracy by referring to past disaster patterns. Based on past disaster patterns, it can improve prediction accuracy under specific conditions. It can analyze past disaster patterns and optimize the prediction algorithm. As a result, prediction accuracy is improved by referring to past disaster patterns.

[0101] The prediction unit can apply different prediction algorithms to different types of disasters during the prediction process. For example, a prediction algorithm specifically for earthquakes can be applied to earthquake predictions. A prediction algorithm specifically for typhoons can be applied to typhoon predictions. A prediction algorithm specifically for floods can be applied to flood predictions. By applying different prediction algorithms to different types of disasters, the accuracy of the predictions can be improved.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The analysis unit analyzes past disaster data and current observation data. For example, the analysis unit analyzes past earthquake data and weather data, and analyzes the data using data preprocessing methods and analysis algorithms. Specifically, it cleans and normalizes the data and converts it into a format suitable for analysis. Furthermore, it can learn data patterns using machine learning algorithms and use this to help predict disasters. Step 2: The prediction unit predicts disasters based on the data analyzed by the analysis unit. The prediction unit predicts the occurrence of disasters using, for example, prediction models or prediction algorithms. The prediction unit can take past disaster data and current observation data as input and predict the probability and timing of disaster occurrences. Step 3: The service provider provides specific evacuation plans and preparedness advice based on the prediction information obtained by the prediction unit. For example, the service provider may provide suggestions for evacuation routes and lists of emergency supplies. The service provider can use natural language processing technology to inform users of disaster predictions and countermeasures in an easy-to-understand manner using natural language. Step 4: The alert unit provides real-time alerts based on the advice provided by the service unit. For example, the alert unit provides real-time alerts in response to increased risk. The alert unit can provide users with alerts urging them to evacuate when a disaster is predicted. The alert unit can configure the type of alert and notification method to provide users with appropriate alerts.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0106] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] Each of the multiple elements described above, including the analysis unit, prediction unit, provision unit, and alert unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the smart device 14 and analyzes past disaster data and current observation data. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and makes disaster predictions based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart device 14 and provides specific evacuation plans and preparedness advice based on the prediction results. The alert unit is implemented by the control unit 46A of the smart device 14 and provides real-time alerts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0115] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the analysis unit, prediction unit, provision unit, and alert unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the smart glasses 214 and analyzes past disaster data and current observation data. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and makes disaster predictions based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides specific evacuation plans and preparedness advice based on the prediction results. The alert unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time alerts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the analysis unit, prediction unit, provision unit, and alert unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the headset terminal 314 and analyzes past disaster data and current observation data. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and makes disaster predictions based on the analyzed data. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides specific evacuation plans and preparedness advice based on the prediction results. The alert unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time alerts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0148] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the analysis unit, prediction unit, provision unit, and alert unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the robot 414 and analyzes past disaster data and current observation data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes disaster predictions based on the analyzed data. The provision unit is implemented by the control unit 46A of the robot 414 and provides specific evacuation plans and preparedness advice based on the prediction results. The alert unit is implemented by the control unit 46A of the robot 414 and provides real-time alerts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0159] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0160] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0161] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0165] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0166] 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.

[0167] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0168] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0169] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0170] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0172] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0173] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0174] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0175] (Note 1) The analysis unit analyzes past disaster data and current observation data, A prediction unit that predicts disasters based on the data analyzed by the aforementioned analysis unit, A provision unit provides specific evacuation plans and preparation advice based on the prediction information obtained by the prediction unit, The system includes an alert unit that provides real-time alerts based on advice provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Computer vision technology is used to analyze satellite imagery and drone footage to detect topographic changes and new risks. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Notify users of disaster predictions and countermeasures in an easy-to-understand natural language. The system described in Appendix 1, characterized by the features described herein. (Note 4) The alert unit is, Provides real-time alerts in response to increasing risks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Prioritize the analysis of data from past disaster records that meet specific criteria. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We will adjust the frequency of acquiring current observational data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the importance of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, In addition to analyzing satellite imagery and drone footage, data from ground sensors is also integrated and analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Compare and analyze disaster data from different regions to extract common patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The prediction unit, When making predictions, we improve the accuracy of predictions by referring to past disaster patterns. The system described in Appendix 1, characterized by the features described herein. (Note 13) The prediction unit, When making predictions, different prediction algorithms are applied for each different type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, It estimates the user's emotions and prioritizes the prediction results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The prediction unit, When making predictions, assess regional risks by taking geographical characteristics into account. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, During the forecasting process, weather and seismic data are integrated to perform comprehensive disaster predictions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the service, it will refer to the user's past evacuation history to propose the optimal evacuation plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the preparedness advice is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the system will suggest the optimal evacuation route, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, It estimates the user's emotions and adjusts how alerts are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, When an alert is triggered, the system will refer to the user's past response history to select the most appropriate alert method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The alert unit is, When an alert is triggered, the system provides the optimal alert method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes past disaster data and current observation data, A prediction unit that predicts disasters based on the data analyzed by the aforementioned analysis unit, A provision unit provides specific evacuation plans and preparation advice based on the prediction information obtained by the prediction unit, The system includes an alert unit that provides real-time alerts based on advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned analysis unit, Computer vision technology is used to analyze satellite imagery and drone footage to detect topographic changes and new risks. The system according to feature 1.

3. The aforementioned supply unit is, Notify users of disaster predictions and countermeasures in an easy-to-understand natural language. The system according to feature 1.

4. The alert unit is, Provides real-time alerts in response to increasing risks. The system according to feature 1.

5. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

6. The aforementioned analysis unit, Prioritize the analysis of data from past disaster records that meet specific criteria. The system according to feature 1.

7. The aforementioned analysis unit, We will adjust the frequency of acquiring current observational data to improve the accuracy of the analysis. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the importance of the analysis results based on the estimated user emotions. The system according to feature 1.

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