system

Abstract

The system according to this embodiment aims to predict the occurrence of disasters and provide warning information quickly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a prediction unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts the occurrence of disasters based on the analysis results obtained by the analysis unit. The generation unit generates warning information based on the disaster information predicted by the prediction unit. The provision unit provides the warning information generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, predicting the occurrence of disasters and promptly providing warning information have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to predict the occurrence of disasters and promptly provide warning information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a generation unit, and a provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The prediction unit predicts the occurrence of disasters based on the analysis results obtained by the analysis unit. The generation unit generates warning information based on the disaster information predicted by the prediction unit. The provision unit provides the warning information generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict the occurrence of a disaster and provide warning information quickly. [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 manages communication between multiple 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 uses generative AI to predict the occurrence of disasters and provide warning information. This disaster prediction system uses generative AI to analyze data collected from various sensors and data sources to predict the occurrence of disasters. Furthermore, it generates warning information based on the predicted disaster information and provides it to the user. This system enables early detection of disasters and rapid response, and is expected to reduce damage. For example, the disaster prediction system collects data from seismometers, weather data, river water level data, etc. This data is input into the generative AI and analyzed. The generative AI has learned from past disaster data and weather data, and analyzes current data based on this. For example, it analyzes earthquake precursor phenomena and weather patterns to predict the occurrence of disasters. If the generative AI predicts the occurrence of a disaster, it generates warning information based on that information. For example, if an earthquake is predicted, it generates warning information including information such as the epicenter, seismic intensity, and time of occurrence. Finally, it provides the generated warning information to the user. The user can receive the warning information via smartphone or personal computer. This allows the user to respond quickly, and is expected to reduce damage. For example, if an earthquake is predicted, users can evacuate in advance and ensure their safety. Similarly, if a flood is predicted, users can evacuate early and minimize damage. In this way, disaster prediction systems enable early detection and rapid response to disasters, thereby mitigating damage.

[0029] The disaster prediction system according to this embodiment comprises a collection unit, an analysis unit, a prediction unit, a generation unit, and a provision unit. The collection unit collects data from various sensors and data sources. For example, the collection unit can collect data from seismometers, meteorological data, river water level data, etc. The collection unit can collect seismic vibration data using a seismometer, for example. The collection unit can also use meteorological sensors to collect meteorological data. Furthermore, the collection unit can use water level gauges to collect river water level data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using, for example, a generation AI. The generation AI has learned past disaster data and meteorological data, and analyzes the current data based on this. For example, the analysis unit can analyze earthquake precursor phenomena using the generation AI. The analysis unit can also analyze meteorological patterns using the generation AI. The prediction unit predicts the occurrence of disasters based on the analysis results obtained by the analysis unit. The prediction unit can predict the occurrence of earthquakes using, for example, a generation AI. Furthermore, the prediction unit can also predict the occurrence of floods using generation AI. The generation unit generates warning information based on the disaster information predicted by the prediction unit. For example, if an earthquake is predicted, the generation unit can generate warning information including information such as the epicenter, seismic intensity, and time of occurrence. Also, if a flood is predicted, the generation unit can generate warning information including information such as the location of the flood and the predicted rise in water level. The provision unit provides the warning information generated by the generation unit. The provision unit can provide warning information, for example, via smartphones or personal computers. For example, the provision unit can notify users of warning information via a smartphone application. The provision unit can also provide warning information to users via a website on a personal computer. As a result, the disaster prediction system according to this embodiment can predict the occurrence of disasters and provide warning information, thereby enabling a rapid response and mitigating damage.

[0030] The data collection unit can collect at least one of the following data: seismometer data, meteorological data, or river water level data. For example, the data collection unit can collect seismic vibration data using a seismometer. For example, the data collection unit can install a seismometer and monitor seismic vibrations in real time. The data collection unit can also use meteorological sensors to collect meteorological data. For example, the data collection unit can collect meteorological data such as temperature, humidity, and wind speed. Furthermore, the data collection unit can use a water level gauge to collect river water level data. For example, the data collection unit can periodically measure river water levels and collect data. This enables the data collection unit to collect data from a variety of data sources. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input vibration data acquired from a seismometer into a generative AI, which can then analyze the data.

[0031] The analysis unit can learn from past disaster data and weather data and analyze current data. For example, the analysis unit uses a generative AI to learn from past disaster data and weather data. For example, the analysis unit can collect past earthquake data and use it to train the generative AI. The analysis unit can also collect past weather data and use it to train the generative AI. Furthermore, the analysis unit can use the generative AI to analyze current data. For example, the analysis unit can input current earthquake data into the generative AI, and the generative AI can analyze the data. The analysis unit can also input current weather data into the generative AI, and the generative AI can analyze the data. As a result, the analysis unit improves the accuracy of its analysis by utilizing past data to analyze current data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, for example, or without using a generative AI. For example, the analysis unit can train a generative AI with past disaster data and weather data and analyze current data.

[0032] The prediction unit can analyze earthquake precursor phenomena and weather patterns to predict the occurrence of disasters. For example, the prediction unit can use generative AI to analyze earthquake precursor phenomena. For example, the prediction unit can analyze earthquake precursor phenomena such as crustal movements and abnormal animal behavior. The prediction unit can also use generative AI to analyze weather patterns. For example, the prediction unit can analyze weather patterns such as typhoon paths and rainfall patterns. As a result, the prediction unit can predict the occurrence of disasters with high accuracy by analyzing earthquakes and weather patterns. Some or all of the above processing in the prediction unit may be performed using generative AI, or it may be performed without using generative AI. For example, the prediction unit can input earthquake precursor phenomena and weather patterns into the generative AI, and the generative AI can predict the occurrence of disasters.

[0033] The generation unit can generate warning information that includes at least one piece of information, such as the epicenter, seismic intensity, or time of occurrence. For example, if an earthquake is predicted, the generation unit generates warning information that includes information such as the epicenter, seismic intensity, and time of occurrence. For example, the generation unit can identify the epicenter of the earthquake and include its latitude and longitude information in the warning information. The generation unit can also measure the seismic intensity of the earthquake and include that intensity information in the warning information. Furthermore, the generation unit can identify the time of occurrence of the earthquake and include that time information in the warning information. This allows the generation unit to generate detailed warning information, enabling users to respond quickly. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, if an earthquake is predicted, the generation unit can input that information into a generation AI, and the generation AI can generate warning information.

[0034] The service provider can provide warning information via smartphones or personal computers. For example, the service provider can provide warning information through a smartphone application. For example, the service provider can notify users of warning information using the smartphone's notification function. The service provider can also provide warning information through a personal computer website. For example, the service provider can display warning information on a personal computer website and make it accessible to users. This allows the service provider to receive warning information from users through various devices. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can generate warning information using a generative AI and provide that information to smartphones or personal computers.

[0035] The data collection unit can analyze the data collection history from past disasters and select the optimal collection method. For example, the data collection unit can analyze the data collection history from past earthquakes to determine the most effective sensor placement. For example, the data collection unit can collect past earthquake data and optimize the placement of sensors. The data collection unit can also set the optimal data collection timing based on the data collection history from past floods. For example, the data collection unit can analyze past flood data and optimize the frequency of data collection. Furthermore, the data collection unit can select the optimal data collection method by referring to the data collection history from past typhoons. For example, the data collection unit can analyze past typhoon data and optimize the data collection method. In this way, the data collection unit can select the optimal data collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the data collection history from past disasters into a generative AI, and the generative AI can select the optimal collection method.

[0036] The data collection unit can filter data based on specific regions or environmental conditions during data collection. For example, when an earthquake occurs, the data collection unit can prioritize collecting data around the epicenter. For instance, the data collection unit can prioritize collecting seismometer data around the epicenter and use it for analysis. Similarly, when a flood occurs, the data collection unit can prioritize collecting data from areas with high river levels. For instance, the data collection unit can prioritize collecting water level data from areas with high river levels and use it for analysis. Furthermore, when a typhoon occurs, the data collection unit can prioritize collecting data from areas with strong winds. For instance, the data collection unit can prioritize collecting meteorological data from areas with strong winds and use it for analysis. This allows the data collection unit to prioritize the collection of important data by filtering it based on specific regions or environmental conditions. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without one. For example, the data collection unit can input criteria for filtering data based on specific regions or environmental conditions into the generative AI, which can then filter the data.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is close to the epicenter, the data collection unit can prioritize the collection of earthquake data. For example, the data collection unit can acquire the user's geographical location information and prioritize the collection of earthquake data if the user is close to the epicenter. The data collection unit can also prioritize the collection of river water level data if the user is near a river. For example, the data collection unit can acquire the user's geographical location information and prioritize the collection of river water level data if the user is near a river. Furthermore, if the user is in the path of a typhoon, the data collection unit can prioritize the collection of weather data. For example, the data collection unit can acquire the user's geographical location information and prioritize the collection of weather data if the user is in the path of a typhoon. This allows the data collection unit to prioritize the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then prioritize the collection of highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user has made posts about earthquakes, the data collection unit can prioritize collecting earthquake data. For example, if a user has made posts about earthquakes, the data collection unit can analyze the user's social media activity and prioritize collecting earthquake data if there are many posts about earthquakes. The data collection unit can also prioritize collecting river water level data if a user has made posts about floods. For example, if a user has made posts about typhoons, the data collection unit can prioritize collecting weather data. For example, if a user has made posts about typhoons, the data collection unit can analyze the user's social media activity and prioritize collecting weather data if there are many posts about typhoons. In this way, the data collection unit can efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, and the generative AI can collect relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, if earthquake data is important, the analysis unit can perform a detailed analysis. For example, the analysis unit can analyze the affected area and seismic intensity of earthquake data in detail. The analysis unit can also perform a detailed analysis if meteorological data is important. For example, the analysis unit can analyze the variation patterns and prediction models of meteorological data in detail. Furthermore, if river water level data is important, the analysis unit can perform a detailed analysis. For example, the analysis unit can analyze the variation patterns and flood predictions of river water level data in detail. In this way, the analysis unit can perform a detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a seismic wave analysis algorithm to earthquake data. For example, it can apply an algorithm that analyzes the frequency and amplitude of seismic waves. The analysis unit can also apply a weather forecasting algorithm to meteorological data. For example, it can apply an algorithm that analyzes the variation patterns and prediction models of meteorological data. Furthermore, the analysis unit can apply a water level forecasting algorithm to river water level data. For example, it can apply an algorithm that analyzes the variation patterns and flood forecasts of river water level data. In this way, the analysis unit improves the accuracy of the analysis by applying an appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data category into a generative AI, and the generative AI can apply an appropriate analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the most recent earthquake data. For example, the analysis unit can prioritize the analysis of the most recent data, taking into account the data collection timing of the earthquake data. The analysis unit can also prioritize the analysis of the most recent meteorological data. For example, the analysis unit can prioritize the analysis of the most recent data, taking into account the data collection timing of the meteorological data. Furthermore, the analysis unit can prioritize the analysis of the most recent river water level data. For example, the analysis unit can prioritize the analysis of the most recent data, taking into account the data collection timing of the river water level data. In this way, the analysis unit can prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the data collection timing into the generating AI, and the generating AI can determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit can determine the order of analysis by considering the relationships between earthquake data and meteorological data. For example, the analysis unit can evaluate the correlation between earthquake data and meteorological data and prioritize the analysis of data with high relevance. The analysis unit can also determine the order of analysis by considering the relationships between river water level data and meteorological data. For example, the analysis unit can evaluate the correlation between river water level data and meteorological data and prioritize the analysis of data with high relevance. Furthermore, the analysis unit can determine the order of analysis by considering the relationships between earthquake data and river water level data. For example, the analysis unit can evaluate the correlation between earthquake data and river water level data and prioritize the analysis of data with high relevance. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relationships between the data. Some or all of the above processing 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 the relationships between the data into a generative AI, and the generative AI can adjust the order of analysis.

[0043] The prediction unit can improve the accuracy of its predictions by considering the interrelationships between data. For example, the prediction unit can make predictions by considering the interrelationships between earthquake data and meteorological data. For example, the prediction unit can evaluate the correlation between earthquake data and meteorological data and make predictions while considering these interrelationships. The prediction unit can also make predictions by considering the interrelationships between river water level data and meteorological data. For example, the prediction unit can evaluate the correlation between river water level data and meteorological data and make predictions while considering these interrelationships. Furthermore, the prediction unit can make predictions by considering the interrelationships between earthquake data and river water level data. For example, the prediction unit can evaluate the correlation between earthquake data and river water level data and make predictions while considering these interrelationships. As a result, the prediction unit improves the accuracy of its predictions by considering the interrelationships between data. 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 the interrelationships between data into a generative AI, which can then improve the accuracy of its predictions.

[0044] The prediction unit can make predictions while considering the attribute information of the data provider. For example, if the data provider is an expert, the prediction unit can prioritize using that data for predictions. For example, the prediction unit can obtain the attribute information of the data provider and prioritize using the expert's data for predictions. The prediction unit can also make predictions by supplementing the data if the data provider is a general user. For example, the prediction unit can obtain the attribute information of the data provider and supplementarily use the general user's data for predictions. Furthermore, if the data provider is an institution, the prediction unit can make predictions by using that data as a reliable source. For example, the prediction unit can obtain the attribute information of the data provider and use the institution's data as a reliable source for predictions. As a result, the prediction unit can make reliable predictions by considering the attribute information of the data provider. 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 the attribute information of the data provider into a generative AI, and the generative AI can make predictions.

[0045] The prediction unit can perform predictions while considering the geographical distribution of data. For example, the prediction unit can perform predictions while considering the geographical distribution of earthquake data. For example, the prediction unit can evaluate the geographical distribution of earthquake data and predict the earthquake risk for each region. The prediction unit can also perform predictions while considering the geographical distribution of meteorological data. For example, the prediction unit can evaluate the geographical distribution of meteorological data and predict meteorological risk for each region. Furthermore, the prediction unit can perform predictions while considering the geographical distribution of river water level data. For example, the prediction unit can evaluate the geographical distribution of river water level data and predict the flood risk for each region. In this way, the prediction unit can perform region-specific predictions by considering the geographical distribution of data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input the geographical distribution of data into a generative AI, and the generative AI can perform predictions.

[0046] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on the data during the prediction process. For example, the prediction unit can make predictions by referring to literature related to earthquake data. For example, the prediction unit can improve the accuracy of its prediction model by referring to academic papers and technical reports related to earthquake data. The prediction unit can also make predictions by referring to literature related to meteorological data. For example, the prediction unit can improve the accuracy of its prediction model by referring to academic papers and technical reports related to meteorological data. Furthermore, the prediction unit can make predictions by referring to literature related to river water level data. For example, the prediction unit can improve the accuracy of its prediction model by referring to academic papers and technical reports related to river water level data. In this way, the prediction unit improves the accuracy of its predictions by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input relevant literature into a generative AI, which can then improve the accuracy of its predictions.

[0047] The generation unit can adjust the level of detail of information based on the importance of the data when generating warning information. For example, if earthquake data is important, the generation unit can generate detailed warning information. For example, the generation unit can generate warning information that details the affected area and seismic intensity of the earthquake data. The generation unit can also generate detailed warning information if meteorological data is important. For example, the generation unit can generate warning information that details the fluctuation patterns and prediction models of the meteorological data. Furthermore, if river water level data is important, the generation unit can generate detailed warning information. For example, the generation unit can generate warning information that details the fluctuation patterns and flood predictions of the river water level data. In this way, the generation unit can provide important information in detail by adjusting the level of detail of the information based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the data into the generation AI, and the generation AI can adjust the level of detail of the information.

[0048] The generation unit can apply different generation algorithms depending on the data category when generating warning information. For example, the generation unit can apply an earthquake warning information generation algorithm to earthquake data. For example, the generation unit can analyze the affected area and seismic intensity of earthquake data and apply an algorithm to generate earthquake warning information. The generation unit can also apply a weather warning information generation algorithm to weather data. For example, the generation unit can analyze the fluctuation patterns and prediction models of weather data and apply an algorithm to generate weather warning information. Furthermore, the generation unit can apply a flood warning information generation algorithm to river water level data. For example, the generation unit can analyze the fluctuation patterns and flood predictions of river water level data and apply an algorithm to generate flood warning information. In this way, the generation unit improves the accuracy of warning information by applying an appropriate generation algorithm according to the data category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the data category into the generation AI, and the generation AI can apply an appropriate generation algorithm.

[0049] The generation unit can determine the priority of information based on the data collection timing when generating warning information. For example, the generation unit can generate warning information based on the latest earthquake data. For example, the generation unit can consider the data collection timing of earthquake data and generate warning information based on the latest data. The generation unit can also generate warning information based on the latest weather data. For example, the generation unit can consider the data collection timing of weather data and generate warning information based on the latest data. Furthermore, the generation unit can generate warning information based on the latest river water level data. For example, the generation unit can consider the data collection timing of river water level data and generate warning information based on the latest data. In this way, the generation unit can prioritize the provision of the latest information by determining the priority of information based on the data collection timing. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the data collection timing into the generation AI, and the generation AI can determine the priority of information.

[0050] The generation unit can adjust the order of information based on the relationships between the data when generating warning information. For example, the generation unit can determine the order of warning information by considering the relationships between earthquake data and weather data. For example, the generation unit can evaluate the correlation between earthquake data and weather data and display information with higher relevance preferentially. The generation unit can also determine the order of warning information by considering the relationships between river water level data and weather data. For example, the generation unit can evaluate the correlation between river water level data and weather data and display information with higher relevance preferentially. Furthermore, the generation unit can determine the order of warning information by considering the relationships between earthquake data and river water level data. For example, the generation unit can evaluate the correlation between earthquake data and river water level data and display information with higher relevance preferentially. In this way, the generation unit can efficiently provide information by adjusting the order of information based on the relationships between the data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the relationships between the data into the generation AI, and the generation AI can adjust the order of the information.

[0051] The information provider can select the optimal method of providing warning information by referring to the user's past response history. For example, if the user has previously preferred detailed information, the information provider can provide detailed warning information. For example, the information provider can refer to the user's past response history and provide detailed warning information. The information provider can also provide concise warning information if the user has previously preferred concise information. For example, the information provider can refer to the user's past response history and provide concise warning information. Furthermore, if the user has previously preferred rapid information provision, the information provider can provide warning information that can be quickly understood. For example, the information provider can refer to the user's past response history and provide warning information that can be quickly understood. In this way, the information provider can provide optimal information by referring to the user's past response history. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provider can input the user's past response history into a generating AI, and the generating AI can select the optimal method of providing information.

[0052] The information provider can select the optimal method of providing warning information by considering the user's device information. For example, if the user is using a smartphone, the information provider can provide warning information tailored to the screen size. For example, the information provider can generate warning information optimized for the smartphone screen size and provide it to the user. Also, if the user is using a PC, the information provider can provide warning information optimized for a larger screen. For example, the information provider can generate warning information optimized for the PC screen size and provide it to the user. Furthermore, if the user is using a smartwatch, the information provider can provide concise and highly visible warning information. For example, the information provider can generate warning information optimized for the smartwatch screen size and provide it to the user. In this way, the information provider can provide optimal information by considering the user's device information. Some or all of the above processing in the information provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the information provider can input the user's device information into the generation AI, and the generation AI can select the optimal method of providing the information.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The data collection unit can collect users' health data and use it for risk assessment during disasters. For example, the data collection unit can collect health data such as users' heart rate, blood pressure, and body temperature to assess stress levels during disasters. Furthermore, the data collection unit can optimize evacuation behavior during disasters based on users' health data. In addition, the data collection unit can analyze users' health data to identify users who require medical assistance during disasters. This allows the data collection unit to more effectively assess and respond to disaster risks by utilizing users' health data.

[0055] The analysis unit can learn from users' past evacuation behavior data and predict current evacuation behavior. For example, the analysis unit can collect past evacuation behavior data and train a generating AI with it. It can also input current evacuation behavior data into the generating AI to predict evacuation behavior. Furthermore, the analysis unit can analyze evacuation behavior patterns based on past evacuation behavior data and optimize evacuation behavior. In this way, the analysis unit can use past data to predict current evacuation behavior and optimize evacuation behavior.

[0056] The data collection unit can collect traffic data during disasters and use it to optimize evacuation routes. For example, the data collection unit can use traffic sensors to collect road congestion information and use it to optimize evacuation routes. It can also collect information on the operation of public transportation and use it to optimize evacuation routes. Furthermore, it can collect information on traffic accidents and use it to optimize evacuation routes. In this way, the data collection unit can optimize evacuation routes by utilizing traffic data.

[0057] The analysis unit can analyze infrastructure data during a disaster and evaluate the safety of evacuation routes. For example, the analysis unit can analyze the damage to roads and evaluate the safety of evacuation routes. It can also analyze the damage to bridges and evaluate the safety of evacuation routes. Furthermore, it can analyze the collapse status of buildings and evaluate the safety of evacuation routes. In this way, the analysis unit can evaluate the safety of evacuation routes by analyzing infrastructure data and provide safer evacuation routes.

[0058] The forecasting unit can analyze weather data during a disaster and predict the safety of evacuation routes. For example, it can analyze the path of a typhoon and predict the safety of evacuation routes. It can also analyze the rainfall patterns of heavy rain and predict the safety of evacuation routes. Furthermore, it can analyze the wind speed patterns of strong winds and predict the safety of evacuation routes. In this way, by analyzing weather data, the forecasting unit can predict the safety of evacuation routes and provide safer evacuation routes.

[0059] The generation unit can generate evacuation routes based on evacuation shelter data during a disaster. For example, the generation unit can generate evacuation routes considering the capacity of each evacuation shelter. It can also generate evacuation routes considering the facilities of each evacuation shelter. Furthermore, it can generate evacuation routes considering the accessibility of each evacuation shelter. As a result, by generating evacuation routes based on evacuation shelter data, the generation unit can provide more appropriate evacuation support.

[0060] The service provider can provide evacuation information in real time during a disaster. For example, the service provider can provide real-time evacuation information through a smartphone application. It can also provide real-time evacuation information through a website on a personal computer. Furthermore, the service provider can provide real-time evacuation information through a smartwatch. This allows users to take swift evacuation action by providing real-time evacuation information.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects data from various sensors and data sources. For example, it collects seismic vibration data using seismometers, meteorological data using weather sensors, and river water level data using water level gauges. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses a generation AI to learn from past disaster data and weather data, and then analyzes current data. This makes it possible to analyze earthquake precursor phenomena and weather patterns. Step 3: The prediction unit predicts the occurrence of disasters based on the analysis results obtained by the analysis unit. For example, it can use generative AI to predict the occurrence of earthquakes and floods. Step 4: The generation unit generates warning information based on the disaster information predicted by the prediction unit. For example, if an earthquake is predicted, it generates warning information including information such as the epicenter, seismic intensity, and time of occurrence. If a flood is predicted, it generates warning information including information such as the location of the flood and the predicted rise in water level. Step 5: The providing unit provides the warning information generated by the generating unit. For example, it provides the warning information via smartphones or personal computers and notifies users through smartphone applications or personal computer websites.

[0063] (Example of form 2) The disaster prediction system according to an embodiment of the present invention is a system that uses generative AI to predict the occurrence of disasters and provide warning information. This disaster prediction system uses generative AI to analyze data collected from various sensors and data sources to predict the occurrence of disasters. Furthermore, it generates warning information based on the predicted disaster information and provides it to the user. This system enables early detection of disasters and rapid response, and is expected to reduce damage. For example, the disaster prediction system collects data from seismometers, weather data, river water level data, etc. This data is input into the generative AI and analyzed. The generative AI has learned from past disaster data and weather data, and analyzes current data based on this. For example, it analyzes earthquake precursor phenomena and weather patterns to predict the occurrence of disasters. If the generative AI predicts the occurrence of a disaster, it generates warning information based on that information. For example, if an earthquake is predicted, it generates warning information including information such as the epicenter, seismic intensity, and time of occurrence. Finally, it provides the generated warning information to the user. The user can receive the warning information via smartphone or personal computer. This allows the user to respond quickly, and is expected to reduce damage. For example, if an earthquake is predicted, users can evacuate in advance and ensure their safety. Similarly, if a flood is predicted, users can evacuate early and minimize damage. In this way, disaster prediction systems enable early detection and rapid response to disasters, thereby mitigating damage.

[0064] The disaster prediction system according to this embodiment comprises a collection unit, an analysis unit, a prediction unit, a generation unit, and a provision unit. The collection unit collects data from various sensors and data sources. For example, the collection unit can collect data from seismometers, meteorological data, river water level data, etc. The collection unit can collect seismic vibration data using a seismometer, for example. The collection unit can also use meteorological sensors to collect meteorological data. Furthermore, the collection unit can use water level gauges to collect river water level data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using, for example, a generation AI. The generation AI has learned past disaster data and meteorological data, and analyzes the current data based on this. For example, the analysis unit can analyze earthquake precursor phenomena using the generation AI. The analysis unit can also analyze meteorological patterns using the generation AI. The prediction unit predicts the occurrence of disasters based on the analysis results obtained by the analysis unit. The prediction unit can predict the occurrence of earthquakes using, for example, a generation AI. Furthermore, the prediction unit can also predict the occurrence of floods using generation AI. The generation unit generates warning information based on the disaster information predicted by the prediction unit. For example, if an earthquake is predicted, the generation unit can generate warning information including information such as the epicenter, seismic intensity, and time of occurrence. Also, if a flood is predicted, the generation unit can generate warning information including information such as the location of the flood and the predicted rise in water level. The provision unit provides the warning information generated by the generation unit. The provision unit can provide warning information, for example, via smartphones or personal computers. For example, the provision unit can notify users of warning information via a smartphone application. The provision unit can also provide warning information to users via a website on a personal computer. As a result, the disaster prediction system according to this embodiment can predict the occurrence of disasters and provide warning information, thereby enabling a rapid response and mitigating damage.

[0065] The data collection unit can collect at least one of the following data: seismometer data, meteorological data, or river water level data. For example, the data collection unit can collect seismic vibration data using a seismometer. For example, the data collection unit can install a seismometer and monitor seismic vibrations in real time. The data collection unit can also use meteorological sensors to collect meteorological data. For example, the data collection unit can collect meteorological data such as temperature, humidity, and wind speed. Furthermore, the data collection unit can use a water level gauge to collect river water level data. For example, the data collection unit can periodically measure river water levels and collect data. This enables the data collection unit to collect data from a variety of data sources. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input vibration data acquired from a seismometer into a generative AI, which can then analyze the data.

[0066] The analysis unit can learn from past disaster data and weather data and analyze current data. For example, the analysis unit uses a generative AI to learn from past disaster data and weather data. For example, the analysis unit can collect past earthquake data and use it to train the generative AI. The analysis unit can also collect past weather data and use it to train the generative AI. Furthermore, the analysis unit can use the generative AI to analyze current data. For example, the analysis unit can input current earthquake data into the generative AI, and the generative AI can analyze the data. The analysis unit can also input current weather data into the generative AI, and the generative AI can analyze the data. As a result, the analysis unit improves the accuracy of its analysis by utilizing past data to analyze current data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, for example, or without using a generative AI. For example, the analysis unit can train a generative AI with past disaster data and weather data and analyze current data.

[0067] The prediction unit can analyze earthquake precursor phenomena and weather patterns to predict the occurrence of disasters. For example, the prediction unit can use generative AI to analyze earthquake precursor phenomena. For example, the prediction unit can analyze earthquake precursor phenomena such as crustal movements and abnormal animal behavior. The prediction unit can also use generative AI to analyze weather patterns. For example, the prediction unit can analyze weather patterns such as typhoon paths and rainfall patterns. As a result, the prediction unit can predict the occurrence of disasters with high accuracy by analyzing earthquakes and weather patterns. Some or all of the above processing in the prediction unit may be performed using generative AI, or it may be performed without using generative AI. For example, the prediction unit can input earthquake precursor phenomena and weather patterns into the generative AI, and the generative AI can predict the occurrence of disasters.

[0068] The generation unit can generate warning information that includes at least one piece of information, such as the epicenter, seismic intensity, or time of occurrence. For example, if an earthquake is predicted, the generation unit generates warning information that includes information such as the epicenter, seismic intensity, and time of occurrence. For example, the generation unit can identify the epicenter of the earthquake and include its latitude and longitude information in the warning information. The generation unit can also measure the seismic intensity of the earthquake and include that intensity information in the warning information. Furthermore, the generation unit can identify the time of occurrence of the earthquake and include that time information in the warning information. This allows the generation unit to generate detailed warning information, enabling users to respond quickly. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, if an earthquake is predicted, the generation unit can input that information into a generation AI, and the generation AI can generate warning information.

[0069] The service provider can provide warning information via smartphones or personal computers. For example, the service provider can provide warning information through a smartphone application. For example, the service provider can notify users of warning information using the smartphone's notification function. The service provider can also provide warning information through a personal computer website. For example, the service provider can display warning information on a personal computer website and make it accessible to users. This allows the service provider to receive warning information from users through various devices. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can generate warning information using a generative AI and provide that information to smartphones or personal computers.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can increase the frequency of data collection and provide information in real time. For example, the data collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Also, if the user is relaxed, the data collection unit can decrease the frequency of data collection and collect only the necessary information. For example, the data collection unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is facing an emergency, the data collection unit can immediately start data collection and provide information quickly. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows the data collection unit to provide more appropriate information by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using the generation AI, or not using the generation AI. For example, the collection unit may input user sentiment data into the generation AI, which can then adjust the timing of data collection.

[0071] The data collection unit can analyze the data collection history from past disasters and select the optimal collection method. For example, the data collection unit can analyze the data collection history from past earthquakes to determine the most effective sensor placement. For example, the data collection unit can collect past earthquake data and optimize the placement of sensors. The data collection unit can also set the optimal data collection timing based on the data collection history from past floods. For example, the data collection unit can analyze past flood data and optimize the frequency of data collection. Furthermore, the data collection unit can select the optimal data collection method by referring to the data collection history from past typhoons. For example, the data collection unit can analyze past typhoon data and optimize the data collection method. In this way, the data collection unit can select the optimal data collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the data collection history from past disasters into a generative AI, and the generative AI can select the optimal collection method.

[0072] The data collection unit can filter data based on specific regions or environmental conditions during data collection. For example, when an earthquake occurs, the data collection unit can prioritize collecting data around the epicenter. For instance, the data collection unit can prioritize collecting seismometer data around the epicenter and use it for analysis. Similarly, when a flood occurs, the data collection unit can prioritize collecting data from areas with high river levels. For instance, the data collection unit can prioritize collecting water level data from areas with high river levels and use it for analysis. Furthermore, when a typhoon occurs, the data collection unit can prioritize collecting data from areas with strong winds. For instance, the data collection unit can prioritize collecting meteorological data from areas with strong winds and use it for analysis. This allows the data collection unit to prioritize the collection of important data by filtering it based on specific regions or environmental conditions. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without one. For example, the data collection unit can input criteria for filtering data based on specific regions or environmental conditions into the generative AI, which can then filter the data.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting earthquake data. For instance, the unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and prioritize collecting earthquake data. Similarly, if the user is relaxed, the data collection unit can prioritize collecting weather data. For example, the unit can record the user's voice, estimate the user's emotions using voice analysis technology, and prioritize collecting weather data. Furthermore, if the user is facing an emergency, the data collection unit can prioritize collecting river water level data. For example, the unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and prioritize collecting river water level data. This allows the data collection unit to prioritize data collection according to the user's emotions, enabling more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using a generation AI, or not using a generation AI. For example, the collection unit can input user sentiment data into a generation AI and determine the priority of the data that the generation AI will collect.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is close to the epicenter, the data collection unit can prioritize the collection of earthquake data. For example, the data collection unit can acquire the user's geographical location information and prioritize the collection of earthquake data if the user is close to the epicenter. The data collection unit can also prioritize the collection of river water level data if the user is near a river. For example, the data collection unit can acquire the user's geographical location information and prioritize the collection of river water level data if the user is near a river. Furthermore, if the user is in the path of a typhoon, the data collection unit can prioritize the collection of weather data. For example, the data collection unit can acquire the user's geographical location information and prioritize the collection of weather data if the user is in the path of a typhoon. This allows the data collection unit to prioritize the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then prioritize the collection of highly relevant data.

[0075] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user has made posts about earthquakes, the data collection unit can prioritize collecting earthquake data. For example, if a user has made posts about earthquakes, the data collection unit can analyze the user's social media activity and prioritize collecting earthquake data if there are many posts about earthquakes. The data collection unit can also prioritize collecting river water level data if a user has made posts about floods. For example, if a user has made posts about typhoons, the data collection unit can prioritize collecting weather data. For example, if a user has made posts about typhoons, the data collection unit can analyze the user's social media activity and prioritize collecting weather data if there are many posts about typhoons. In this way, the data collection unit can efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, and the generative AI can collect relevant data.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide simple and easily understandable analysis results. For instance, it can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide simple analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, it can record the user's voice, estimate the user's emotions using voice analysis technology, and provide detailed analysis results. Additionally, if the user is facing an emergency, the analysis unit can provide quickly understandable analysis results. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and provide quickly understandable analysis results. This allows the analysis unit to provide more easily understandable analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using the generative AI, or not using the generative AI. For example, the analysis unit may input user emotion data into the generative AI, which can then adjust the way the analysis is presented.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, if earthquake data is important, the analysis unit can perform a detailed analysis. For example, the analysis unit can analyze the affected area and seismic intensity of earthquake data in detail. The analysis unit can also perform a detailed analysis if meteorological data is important. For example, the analysis unit can analyze the variation patterns and prediction models of meteorological data in detail. Furthermore, if river water level data is important, the analysis unit can perform a detailed analysis. For example, the analysis unit can analyze the variation patterns and flood predictions of river water level data in detail. In this way, the analysis unit can perform a detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a seismic wave analysis algorithm to earthquake data. For example, it can apply an algorithm that analyzes the frequency and amplitude of seismic waves. The analysis unit can also apply a weather forecasting algorithm to meteorological data. For example, it can apply an algorithm that analyzes the variation patterns and prediction models of meteorological data. Furthermore, the analysis unit can apply a water level forecasting algorithm to river water level data. For example, it can apply an algorithm that analyzes the variation patterns and flood forecasts of river water level data. In this way, the analysis unit improves the accuracy of the analysis by applying an appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data category into a generative AI, and the generative AI can apply an appropriate analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a short, concise analysis result. For instance, the analysis unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed analysis result. Furthermore, if the user is facing an emergency, the analysis unit can provide an analysis result that can be quickly understood. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and provide an analysis result that can be quickly understood. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using the generation AI, or not using the generation AI. For example, the analysis unit may input user sentiment data into the generation AI, which can then adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the most recent earthquake data. For example, the analysis unit can prioritize the analysis of the most recent data, taking into account the data collection timing of the earthquake data. The analysis unit can also prioritize the analysis of the most recent meteorological data. For example, the analysis unit can prioritize the analysis of the most recent data, taking into account the data collection timing of the meteorological data. Furthermore, the analysis unit can prioritize the analysis of the most recent river water level data. For example, the analysis unit can prioritize the analysis of the most recent data, taking into account the data collection timing of the river water level data. In this way, the analysis unit can prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the data collection timing into the generating AI, and the generating AI can determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit can determine the order of analysis by considering the relationships between earthquake data and meteorological data. For example, the analysis unit can evaluate the correlation between earthquake data and meteorological data and prioritize the analysis of data with high relevance. The analysis unit can also determine the order of analysis by considering the relationships between river water level data and meteorological data. For example, the analysis unit can evaluate the correlation between river water level data and meteorological data and prioritize the analysis of data with high relevance. Furthermore, the analysis unit can determine the order of analysis by considering the relationships between earthquake data and river water level data. For example, the analysis unit can evaluate the correlation between earthquake data and river water level data and prioritize the analysis of data with high relevance. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relationships between the data. Some or all of the above processing 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 the relationships between the data into a generative AI, and the generative AI can adjust the order of analysis.

[0082] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated emotions. For example, if the user is feeling anxious, the prediction unit can use detailed data to improve the accuracy of the prediction. For example, the prediction unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and make a prediction using detailed data. The prediction unit can also make predictions using general data if the user is relaxed. For example, the prediction unit can record the user's voice, estimate the user's emotions using voice analysis technology, and make a prediction using general data. Furthermore, if the user is facing an emergency, the prediction unit can use simplified data to make a quick prediction. For example, the prediction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and make a prediction using simplified data. This allows the prediction unit to make more appropriate predictions by adjusting the prediction criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the prediction unit may be performed using a generative AI, or not using a generative AI. For example, the prediction unit may input user sentiment data into the generative AI, which can then adjust the prediction criteria.

[0083] The prediction unit can improve the accuracy of its predictions by considering the interrelationships between data. For example, the prediction unit can make predictions by considering the interrelationships between earthquake data and meteorological data. For example, the prediction unit can evaluate the correlation between earthquake data and meteorological data and make predictions while considering these interrelationships. The prediction unit can also make predictions by considering the interrelationships between river water level data and meteorological data. For example, the prediction unit can evaluate the correlation between river water level data and meteorological data and make predictions while considering these interrelationships. Furthermore, the prediction unit can make predictions by considering the interrelationships between earthquake data and river water level data. For example, the prediction unit can evaluate the correlation between earthquake data and river water level data and make predictions while considering these interrelationships. As a result, the prediction unit improves the accuracy of its predictions by considering the interrelationships between data. 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 the interrelationships between data into a generative AI, which can then improve the accuracy of its predictions.

[0084] The prediction unit can make predictions while considering the attribute information of the data provider. For example, if the data provider is an expert, the prediction unit can prioritize using that data for predictions. For example, the prediction unit can obtain the attribute information of the data provider and prioritize using the expert's data for predictions. The prediction unit can also make predictions by supplementing the data if the data provider is a general user. For example, the prediction unit can obtain the attribute information of the data provider and supplementarily use the general user's data for predictions. Furthermore, if the data provider is an institution, the prediction unit can make predictions by using that data as a reliable source. For example, the prediction unit can obtain the attribute information of the data provider and use the institution's data as a reliable source for predictions. As a result, the prediction unit can make reliable predictions by considering the attribute information of the data provider. 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 the attribute information of the data provider into a generative AI, and the generative AI can make predictions.

[0085] The prediction unit can estimate the user's emotions and adjust the order in which prediction results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the prediction unit will display the most important prediction result first. For instance, the prediction unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and display the most important prediction result first. Furthermore, if the user is relaxed, the prediction unit can sequentially display detailed prediction results. For example, the prediction unit can record the user's voice, estimate the user's emotions using voice analysis technology, and display detailed prediction results sequentially. Additionally, if the user is facing an emergency, the prediction unit can display prediction results that can be quickly understood first. For example, the prediction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and display prediction results that can be quickly understood first. This allows the prediction unit to provide more appropriate information by adjusting the display order of prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text-generating AI (e.g., LLM) or a multimodal-generating AI. Some or all of the processing described above in the prediction unit may be performed using the generative AI, or not using the generative AI. For example, the prediction unit may input user sentiment data into the generative AI, which can then adjust the display order of the prediction results.

[0086] The prediction unit can perform predictions while considering the geographical distribution of data. For example, the prediction unit can perform predictions while considering the geographical distribution of earthquake data. For example, the prediction unit can evaluate the geographical distribution of earthquake data and predict the earthquake risk for each region. The prediction unit can also perform predictions while considering the geographical distribution of meteorological data. For example, the prediction unit can evaluate the geographical distribution of meteorological data and predict meteorological risk for each region. Furthermore, the prediction unit can perform predictions while considering the geographical distribution of river water level data. For example, the prediction unit can evaluate the geographical distribution of river water level data and predict the flood risk for each region. In this way, the prediction unit can perform region-specific predictions by considering the geographical distribution of data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input the geographical distribution of data into a generative AI, and the generative AI can perform predictions.

[0087] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on the data during the prediction process. For example, the prediction unit can make predictions by referring to literature related to earthquake data. For example, the prediction unit can improve the accuracy of its prediction model by referring to academic papers and technical reports related to earthquake data. The prediction unit can also make predictions by referring to literature related to meteorological data. For example, the prediction unit can improve the accuracy of its prediction model by referring to academic papers and technical reports related to meteorological data. Furthermore, the prediction unit can make predictions by referring to literature related to river water level data. For example, the prediction unit can improve the accuracy of its prediction model by referring to academic papers and technical reports related to river water level data. In this way, the prediction unit improves the accuracy of its predictions by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input relevant literature into a generative AI, which can then improve the accuracy of its predictions.

[0088] The generation unit can estimate the user's emotions and adjust the method of generating warning information based on the estimated emotions. For example, if the user is feeling anxious, the generation unit can generate detailed warning information. For example, the generation unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and generate detailed warning information. The generation unit can also generate concise warning information if the user is relaxed. For example, the generation unit can record the user's voice, estimate the user's emotions using voice analysis technology, and generate concise warning information. Furthermore, if the user is facing an emergency, the generation unit can generate warning information that can be quickly understood. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and generate warning information that can be quickly understood. In this way, the generation unit can provide more appropriate warning information by adjusting the method of generating warning information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust how the warning information is generated.

[0089] The generation unit can adjust the level of detail of information based on the importance of the data when generating warning information. For example, if earthquake data is important, the generation unit can generate detailed warning information. For example, the generation unit can generate warning information that details the affected area and seismic intensity of the earthquake data. The generation unit can also generate detailed warning information if meteorological data is important. For example, the generation unit can generate warning information that details the fluctuation patterns and prediction models of the meteorological data. Furthermore, if river water level data is important, the generation unit can generate detailed warning information. For example, the generation unit can generate warning information that details the fluctuation patterns and flood predictions of the river water level data. In this way, the generation unit can provide important information in detail by adjusting the level of detail of the information based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the data into the generation AI, and the generation AI can adjust the level of detail of the information.

[0090] The generation unit can apply different generation algorithms depending on the data category when generating warning information. For example, the generation unit can apply an earthquake warning information generation algorithm to earthquake data. For example, the generation unit can analyze the affected area and seismic intensity of earthquake data and apply an algorithm to generate earthquake warning information. The generation unit can also apply a weather warning information generation algorithm to weather data. For example, the generation unit can analyze the fluctuation patterns and prediction models of weather data and apply an algorithm to generate weather warning information. Furthermore, the generation unit can apply a flood warning information generation algorithm to river water level data. For example, the generation unit can analyze the fluctuation patterns and flood predictions of river water level data and apply an algorithm to generate flood warning information. In this way, the generation unit improves the accuracy of warning information by applying an appropriate generation algorithm according to the data category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the data category into the generation AI, and the generation AI can apply an appropriate generation algorithm.

[0091] The generation unit can estimate the user's emotions and adjust the display method of warning information based on the estimated user emotions. For example, if the user is feeling anxious, the generation unit can provide a highly visible display method. For example, the generation unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a highly visible display method. The generation unit can also provide a display method that includes detailed information if the user is relaxed. For example, the generation unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a display method that includes detailed information. Furthermore, if the user is facing an emergency, the generation unit can provide a display method that can be quickly understood. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and provide a display method that can be quickly understood. As a result, the generation unit can provide more appropriate information by adjusting the display method of warning information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user sentiment data into the generation AI, which can then adjust how the warning information is displayed.

[0092] The generation unit can determine the priority of information based on the data collection timing when generating warning information. For example, the generation unit can generate warning information based on the latest earthquake data. For example, the generation unit can consider the data collection timing of earthquake data and generate warning information based on the latest data. The generation unit can also generate warning information based on the latest weather data. For example, the generation unit can consider the data collection timing of weather data and generate warning information based on the latest data. Furthermore, the generation unit can generate warning information based on the latest river water level data. For example, the generation unit can consider the data collection timing of river water level data and generate warning information based on the latest data. In this way, the generation unit can prioritize the provision of the latest information by determining the priority of information based on the data collection timing. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the data collection timing into the generation AI, and the generation AI can determine the priority of information.

[0093] The generation unit can adjust the order of information based on the relationships between the data when generating warning information. For example, the generation unit can determine the order of warning information by considering the relationships between earthquake data and weather data. For example, the generation unit can evaluate the correlation between earthquake data and weather data and display information with higher relevance preferentially. The generation unit can also determine the order of warning information by considering the relationships between river water level data and weather data. For example, the generation unit can evaluate the correlation between river water level data and weather data and display information with higher relevance preferentially. Furthermore, the generation unit can determine the order of warning information by considering the relationships between earthquake data and river water level data. For example, the generation unit can evaluate the correlation between earthquake data and river water level data and display information with higher relevance preferentially. In this way, the generation unit can efficiently provide information by adjusting the order of information based on the relationships between the data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the relationships between the data into the generation AI, and the generation AI can adjust the order of the information.

[0094] The information provider can estimate the user's emotions and adjust the method of providing warning information based on the estimated emotions. For example, if the user is feeling anxious, the information provider can provide detailed warning information. For example, the information provider can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide detailed warning information. The information provider can also provide concise warning information if the user is relaxed. For example, the information provider can record the user's voice, estimate the user's emotions using voice analysis technology, and provide concise warning information. Furthermore, if the user is facing an emergency, the information provider can provide warning information that can be quickly understood. For example, the information provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and provide warning information that can be quickly understood. This allows the information provider to provide more appropriate information by adjusting the method of providing warning information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the delivery unit may be performed using a generating AI, or not using a generating AI. For example, the delivery unit may input user emotion data into a generating AI, which can then adjust how the warning information is provided.

[0095] The information provider can select the optimal method of providing warning information by referring to the user's past response history. For example, if the user has previously preferred detailed information, the information provider can provide detailed warning information. For example, the information provider can refer to the user's past response history and provide detailed warning information. The information provider can also provide concise warning information if the user has previously preferred concise information. For example, the information provider can refer to the user's past response history and provide concise warning information. Furthermore, if the user has previously preferred rapid information provision, the information provider can provide warning information that can be quickly understood. For example, the information provider can refer to the user's past response history and provide warning information that can be quickly understood. In this way, the information provider can provide optimal information by referring to the user's past response history. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provider can input the user's past response history into a generating AI, and the generating AI can select the optimal method of providing information.

[0096] The information provider can estimate the user's emotions and prioritize warning information based on those emotions. For example, if the user is feeling anxious, the information provider will provide the most important warning information first. For instance, the information provider can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide the most important warning information first. Furthermore, if the user is relaxed, the information provider can sequentially provide detailed warning information. For example, the information provider can record the user's voice, estimate the user's emotions using voice analysis technology, and provide detailed warning information sequentially. Additionally, if the user is facing an emergency, the information provider can provide warning information that can be quickly understood first. For example, the information provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and provide warning information that can be quickly understood first. This allows the information provider to provide more appropriate information by prioritizing warning information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, a text generating AI (e.g., LLM) or a multimodal generating AI. Some or all of the processing described above in the service provider may be performed using a generating AI, or not using a generating AI. For example, the service provider can input user sentiment data into a generating AI, which can then determine the priority of warning information.

[0097] The information provider can select the optimal method of providing warning information by considering the user's device information. For example, if the user is using a smartphone, the information provider can provide warning information tailored to the screen size. For example, the information provider can generate warning information optimized for the smartphone screen size and provide it to the user. Also, if the user is using a PC, the information provider can provide warning information optimized for a larger screen. For example, the information provider can generate warning information optimized for the PC screen size and provide it to the user. Furthermore, if the user is using a smartwatch, the information provider can provide concise and highly visible warning information. For example, the information provider can generate warning information optimized for the smartwatch screen size and provide it to the user. In this way, the information provider can provide optimal information by considering the user's device information. Some or all of the above processing in the information provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the information provider can input the user's device information into the generation AI, and the generation AI can select the optimal method of providing the information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the smart device 14 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts the occurrence of a disaster based on the analysis results. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates warning information based on the predicted disaster information. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the generated warning information to the user. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts the occurrence of a disaster based on the analysis results. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates warning information based on the predicted disaster information. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated warning information to the user. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the headset terminal 314 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts the occurrence of a disaster based on the analysis results. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates warning information based on the predicted disaster information. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the generated warning information to the user. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the robot 414 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts the occurrence of a disaster based on the analysis results. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates warning information based on the predicted disaster information. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated warning information to the user.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can collect users' health data and use it for risk assessment during disasters. For example, the data collection unit can collect health data such as users' heart rate, blood pressure, and body temperature to assess stress levels during disasters. Furthermore, the data collection unit can optimize evacuation behavior during disasters based on users' health data. In addition, the data collection unit can analyze users' health data to identify users who require medical assistance during disasters. This allows the data collection unit to more effectively assess and respond to disaster risks by utilizing users' health data.

[0100] The analysis unit can learn from users' past evacuation behavior data and predict current evacuation behavior. For example, the analysis unit can collect past evacuation behavior data and train a generating AI with it. It can also input current evacuation behavior data into the generating AI to predict evacuation behavior. Furthermore, the analysis unit can analyze evacuation behavior patterns based on past evacuation behavior data and optimize evacuation behavior. In this way, the analysis unit can use past data to predict current evacuation behavior and optimize evacuation behavior.

[0101] The prediction unit can estimate the user's emotions and predict evacuation behavior based on those emotions. For example, the prediction unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and predict evacuation behavior. It can also record the user's voice, estimate the user's emotions using voice analysis technology, and predict evacuation behavior. Furthermore, the prediction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and predict evacuation behavior. This allows the prediction unit to support more appropriate evacuation behavior by predicting evacuation behavior based on the user's emotions.

[0102] The generation unit can estimate the user's emotions and generate an evacuation route based on those emotions. For example, the generation unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and generate an evacuation route. It can also record the user's voice, estimate the user's emotions using voice analysis technology, and generate an evacuation route. Furthermore, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and generate an evacuation route. This allows the generation unit to provide more appropriate evacuation assistance by generating evacuation routes based on the user's emotions.

[0103] The service provider can estimate the user's emotions and provide evacuation information based on those estimated emotions. For example, the service provider can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide evacuation information. It can also record the user's voice, estimate the user's emotions using voice analysis technology, and provide evacuation information. Furthermore, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the user's emotions using an emotion estimation algorithm, and provide evacuation information. This allows the service provider to provide more appropriate evacuation support by offering evacuation information based on the user's emotions.

[0104] The data collection unit can collect traffic data during disasters and use it to optimize evacuation routes. For example, the data collection unit can use traffic sensors to collect road congestion information and use it to optimize evacuation routes. It can also collect information on the operation of public transportation and use it to optimize evacuation routes. Furthermore, it can collect information on traffic accidents and use it to optimize evacuation routes. In this way, the data collection unit can optimize evacuation routes by utilizing traffic data.

[0105] The analysis unit can analyze infrastructure data during a disaster and evaluate the safety of evacuation routes. For example, the analysis unit can analyze the damage to roads and evaluate the safety of evacuation routes. It can also analyze the damage to bridges and evaluate the safety of evacuation routes. Furthermore, it can analyze the collapse status of buildings and evaluate the safety of evacuation routes. In this way, the analysis unit can evaluate the safety of evacuation routes by analyzing infrastructure data and provide safer evacuation routes.

[0106] The forecasting unit can analyze weather data during a disaster and predict the safety of evacuation routes. For example, it can analyze the path of a typhoon and predict the safety of evacuation routes. It can also analyze the rainfall patterns of heavy rain and predict the safety of evacuation routes. Furthermore, it can analyze the wind speed patterns of strong winds and predict the safety of evacuation routes. In this way, by analyzing weather data, the forecasting unit can predict the safety of evacuation routes and provide safer evacuation routes.

[0107] The generation unit can generate evacuation routes based on evacuation shelter data during a disaster. For example, the generation unit can generate evacuation routes considering the capacity of each evacuation shelter. It can also generate evacuation routes considering the facilities of each evacuation shelter. Furthermore, it can generate evacuation routes considering the accessibility of each evacuation shelter. As a result, by generating evacuation routes based on evacuation shelter data, the generation unit can provide more appropriate evacuation support.

[0108] The service provider can provide evacuation information in real time during a disaster. For example, the service provider can provide real-time evacuation information through a smartphone application. It can also provide real-time evacuation information through a website on a personal computer. Furthermore, the service provider can provide real-time evacuation information through a smartwatch. This allows users to take swift evacuation action by providing real-time evacuation information.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects data from various sensors and data sources. For example, it collects seismic vibration data using seismometers, meteorological data using weather sensors, and river water level data using water level gauges. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses a generation AI to learn from past disaster data and weather data, and then analyzes current data. This makes it possible to analyze earthquake precursor phenomena and weather patterns. Step 3: The prediction unit predicts the occurrence of disasters based on the analysis results obtained by the analysis unit. For example, it can use generative AI to predict the occurrence of earthquakes and floods. Step 4: The generation unit generates warning information based on the disaster information predicted by the prediction unit. For example, if an earthquake is predicted, it generates warning information including information such as the epicenter, seismic intensity, and time of occurrence. If a flood is predicted, it generates warning information including information such as the location of the flood and the predicted rise in water level. Step 5: The providing unit provides the warning information generated by the generating unit. For example, it provides the warning information via smartphones or personal computers and notifies users through smartphone applications or personal computer websites.

[0111] 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.

[0112] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0113] 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.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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. 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.

[0129] 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.

[0130] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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. 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.

[0145] 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.

[0146] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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. 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.

[0162] 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.

[0163] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A prediction unit predicts the occurrence of a disaster based on the analysis results obtained by the analysis unit, A generation unit that generates warning information based on the disaster information predicted by the prediction unit, The system includes a providing unit that provides warning information generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect at least one data point from the following: seismometer data, weather data, or river water level data. The system according to feature 1.

3. The aforementioned analysis unit, It learns from past disaster and weather data and analyzes current data. The system according to feature 1.

4. The prediction unit, Analyzing earthquake precursor phenomena and weather patterns to predict the occurrence of disasters. The system according to feature 1.

5. The generating unit is Generate warning information that includes at least one of the following pieces of information: epicenter or seismic intensity, and time of occurrence. The system according to feature 1.

6. The aforementioned supply unit is, Warning information will be provided via smartphone or computer. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is We will analyze the data collection history from past disasters and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on specific regions or environmental conditions. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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