system

Abstract

The system according to this embodiment aims to provide timely and appropriate disaster prevention information during natural disasters. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit in real time. The generation unit generates disaster prevention information based on the analysis results obtained by the analysis unit. The provision unit provides the disaster prevention 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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] <00ffff025>In the conventional technology, providing prompt and appropriate disaster prevention information during natural disasters has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide prompt and appropriate disaster prevention information during natural disasters.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit in real time. The generation unit generates disaster prevention information based on the analysis result obtained by the analysis unit. The provision unit provides the disaster prevention information generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide rapid and appropriate disaster prevention information during natural disasters. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 prevention information provision system according to an embodiment of the present invention is a system that utilizes an AI agent to reduce natural disaster risks and realize the rapid provision of disaster prevention information. This disaster prevention information provision system collects data such as weather data, earthquake data, and reports from the field, analyzes it in real time to perform future prediction simulations, and generates disaster prevention information tailored to the needs of each region. The generated disaster prevention information is provided to users through smartphone apps, smart home devices, and data sharing with city halls and public institutions. This enables swift action and minimizes damage. For example, the disaster prevention information provision system collects weather data from the Japan Meteorological Agency, earthquake data from the Earthquake Research Institute, and reports from residents and local governments. Next, the disaster prevention information provision system analyzes the collected data in real time and performs future prediction simulations. For example, it analyzes weather data to predict the path of a typhoon or analyzes earthquake data to predict the probability of aftershocks. Based on the analysis results, the disaster prevention information provision system generates disaster prevention information tailored to the needs of each region. For example, it issues evacuation advisories to areas in the path of a typhoon and provides information on evacuation centers to areas heavily affected by earthquakes. Furthermore, the generated disaster prevention information will be provided to users through smartphone apps, smart home devices, and data sharing with city halls and public institutions. This will enable users to take swift action and minimize damage. In addition, the disaster prevention information provision system will optimize the user interface and provide access support for the elderly and vulnerable. For example, the smartphone app will offer an intuitive interface and add features such as voice guidance and large font display. Smart home devices will also provide voice information and automatic emergency notification functions. In this way, the disaster prevention information provision system can reduce the risk of natural disasters and provide disaster prevention information quickly by utilizing AI agents. This will enable residents to take swift action and minimize damage. Furthermore, by collaborating with local community groups and providing curated local news feeds and immediate notification functions in emergencies, it will be possible to provide information that is closely tailored to the local community.Furthermore, it is expected to be deployed as part of smart city development, expanded to other regions and overseas, and applied to crisis management environments beyond disaster prevention. This will enable the disaster information provision system to reduce natural disaster risks and provide disaster information quickly.

[0029] The disaster prevention information provision system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. For example, the collection unit collects meteorological data from the Japan Meteorological Agency. The collection unit can also collect earthquake data from the Earthquake Research Institute. The collection unit can also collect reports from the field from residents and local governments. The collection unit collects data in real time and can always obtain the latest information. The analysis unit analyzes the data collected by the collection unit in real time. For example, the analysis unit analyzes meteorological data to predict the path of a typhoon. The analysis unit can also analyze earthquake data to predict the probability of aftershocks. The analysis unit performs future prediction simulations to predict the probability of disaster occurrence and the extent of impact. Some or all of the above-described processes in the analysis unit may be performed using AI or not. The generation unit generates disaster prevention information based on the analysis results obtained by the analysis unit. For example, the generation unit generates disaster prevention information that issues evacuation advisories to areas in the path of a typhoon. The generation unit can also generate disaster prevention information that provides information on evacuation shelters to areas greatly affected by earthquakes. The generation unit generates disaster prevention information tailored to the needs of each region. Some or all of the processing described above in the generation unit may be performed using AI or not. The provision unit provides the disaster prevention information generated by the generation unit. The provision unit provides disaster prevention information, for example, through a smartphone application. The provision unit can also provide disaster prevention information through smart home devices. The provision unit can also provide disaster prevention information through data sharing with city halls and other public institutions. Some or all of the processing described above in the provision unit may be performed using AI or not. As a result, the disaster prevention information provision system according to this embodiment efficiently collects, analyzes, generates, and provides data.

[0030] The data collection unit collects data. For example, the data collection unit collects meteorological data from the Japan Meteorological Agency. Specifically, it uses APIs provided by the Japan Meteorological Agency to obtain detailed meteorological data such as rainfall, wind speed, temperature, and humidity in real time. This allows the data collection unit to always have access to the latest information on meteorological phenomena such as typhoons and heavy rains. The data collection unit can also collect earthquake data from the Earthquake Research Institute. It obtains earthquake observation data provided by the Earthquake Research Institute and collects information such as the time of earthquake occurrence, epicenter, magnitude, and seismic intensity distribution. This enables the data collection unit to respond quickly immediately after an earthquake. The data collection unit can also collect reports from residents and local governments. It collects information reported by residents and local governments through dedicated applications and web portals to understand the extent of damage and evacuation in the area. This allows the data collection unit to obtain real-time information from the area and respond quickly. The data collection unit collects data in real time and can always obtain the latest information. This allows the data collection unit to collect a wide range of information from diverse data sources and improve the overall accuracy of the system. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by increasing the frequency of meteorological data collection when a typhoon approaches and increasing the frequency of earthquake data collection when an earthquake occurs, it becomes possible to provide information quickly and accurately. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit in real time. For example, the analysis unit analyzes meteorological data to predict the path of a typhoon. Specifically, based on meteorological data, it analyzes the typhoon's center position, direction of movement, speed, intensity, etc., to predict its future path. Numerical weather prediction models and statistical models can be used for this. The analysis unit can also analyze earthquake data to predict the probability of aftershocks. Based on earthquake data, it analyzes the characteristics of the epicenter and past aftershock data to predict the probability and magnitude of aftershocks. Probabilistic methods and machine learning models can be used for this. The analysis unit performs future prediction simulations to predict the probability of disasters occurring and the extent of their impact. For example, it performs flood simulations and predicts the probability of floods occurring and the extent of flooding based on rainfall and topographic data. Fluid dynamics models and geographic information systems (GIS) can be used for this. Some or all of the above-described processes in the analysis unit may be performed using AI or not. When using AI, techniques such as deep learning and reinforcement learning can be used to improve the accuracy of data analysis and prediction. For example, deep learning can be used to analyze weather data and build a typhoon path prediction model. This allows the analysis unit to quickly and accurately analyze collected data and grasp disaster risks in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The generation unit generates disaster prevention information based on the analysis results obtained by the analysis unit. For example, the generation unit generates disaster prevention information that issues evacuation advisories to areas in the path of a typhoon. Specifically, it identifies areas that may be affected based on typhoon path prediction data and issues evacuation advisories to residents in those areas. The generation unit can also generate disaster prevention information that provides information on evacuation shelters to areas heavily affected by earthquakes. Based on earthquake seismic intensity distribution data, it identifies areas predicted to suffer significant damage and provides residents in those areas with information on the location of evacuation shelters and evacuation routes. The generation unit generates disaster prevention information tailored to the needs of each region. For example, it provides information on special assistance needed in areas with a large elderly or disabled population. Some or all of the above processing in the generation unit may be performed using AI or not. When using AI, natural language generation (NLG) technology can be used to automatically generate disaster prevention information based on the analysis results. For example, NLG technology can be used to automatically generate evacuation advisory text based on typhoon path prediction data. This allows the generation unit to quickly and accurately generate and provide disaster prevention information to residents. Furthermore, the generation unit can continuously improve the content of the generated disaster prevention information. For example, by evaluating the effectiveness of past disaster prevention information and reviewing the content and expression of the information based on the results, it can provide more effective disaster prevention information. In this way, the generation unit can generate appropriate disaster prevention information that meets the needs of each region and ensure the safety of residents.

[0033] The provisioning unit provides disaster prevention information generated by the generation unit. The provisioning unit provides disaster prevention information, for example, through a smartphone app. Specifically, it notifies residents of real-time updated disaster prevention information through a smartphone app. The app has a push notification function, allowing information to be immediately transmitted to residents in emergencies. The provisioning unit can also provide disaster prevention information through smart home devices. Smart home devices are equipped with voice assistants and displays, providing disaster prevention information through voice and visual means. This allows information to be transmitted to residents with visual or hearing impairments. The provisioning unit can also provide disaster prevention information through data sharing with city halls and public institutions. Based on the disaster prevention information received from the provisioning unit, city halls and public institutions can formulate local disaster prevention plans and issue appropriate instructions to residents. Some or all of the above-described processes in the provisioning unit may be performed using AI or not. When using AI, it is possible to provide individually customized disaster prevention information based on the user's behavior history and location information. For example, it is possible to prioritize notifications of evacuation routes and shelters that the user frequently uses. This allows the service provider to quickly provide appropriate disaster prevention information to each user, minimizing the risk of disaster. Furthermore, the service provider can collect feedback from users and continuously improve the accuracy and effectiveness of the information provided. As a result, the service provider can quickly and reliably provide disaster prevention information to users and ensure the safety of residents.

[0034] The analysis unit includes a simulation unit that performs future prediction simulations. For example, the analysis unit performs simulations to predict the path of a typhoon by analyzing meteorological data. The analysis unit can also perform simulations to predict the probability of aftershocks by analyzing earthquake data. The analysis unit performs future prediction simulations to predict the probability of disaster occurrence and the extent of its impact. Future prediction simulations are performed using, for example, meteorological models and earthquake prediction models. Meteorological models predict the path of a typhoon and rainfall based on meteorological data. Earthquake prediction models determine the probability of aftershocks and identify the epicenters based on earthquake data. As a result, the probability of disaster occurrence and the extent of its impact can be predicted by future prediction simulations. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input meteorological data and earthquake data into a generating AI, and the generating AI can perform future prediction simulations.

[0035] The information provision unit includes a collaboration unit that handles data sharing with smartphone apps, smart home devices, and city halls and public institutions. The information provision unit can, for example, provide disaster prevention information through a smartphone app. The information provision unit can also provide disaster prevention information through smart home devices. The information provision unit can also provide disaster prevention information through data sharing with city halls and public institutions. Through data sharing, the information provision unit can quickly provide disaster prevention information to users. Data sharing provides disaster prevention information in real time, for example, through a smartphone app. Smart home devices can provide disaster prevention information by voice. Data sharing with city halls and public institutions allows for the sharing of local disaster prevention information and enables a rapid response. This allows for the quick provision of disaster prevention information to users through data sharing. Some or all of the above-described processes in the information provision unit may be performed using AI or not. For example, the information provision unit can input disaster prevention information into a generating AI for a smartphone app or smart home device, and the generating AI can provide the disaster prevention information.

[0036] The service provider includes a support unit that optimizes the user interface and provides access support for the elderly and vulnerable. For example, the service provider provides an intuitive interface for smartphone applications. The service provider can also add features such as voice guidance and large font displays. The service provider can also provide voice information and automatic emergency notification functions for smart home devices. By optimizing the user interface, the service provider makes disaster prevention information easily accessible to everyone. User interface optimization improves usability, visibility, and operability, for example. Access support for the elderly and vulnerable includes providing voice guidance, large font displays, and simple operation methods. This makes disaster prevention information easily accessible to everyone. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input data for user interface optimization and access support into a generating AI, which can then provide an optimal interface.

[0037] The generation unit can generate disaster prevention information tailored to the needs of each region. For example, the generation unit can generate disaster prevention information that issues evacuation advisories to areas in the path of a typhoon. The generation unit can also generate disaster prevention information that provides information on evacuation shelters to areas heavily affected by earthquakes. The generation unit generates disaster prevention information tailored to the needs of each region. These regional needs are determined, for example, based on the disaster risks of the region and the requests of the residents. The generation unit generates appropriate disaster prevention information based on these regional needs. This allows for the provision of disaster prevention information tailored to the needs of each region. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input data on regional needs into a generation AI, and the generation AI can generate disaster prevention information tailored to the regional needs.

[0038] The service provider can collaborate with local community groups to provide curated local news feeds and instant notification functions in emergencies. For example, the service provider can collaborate with local community groups to curate local news feeds. The service provider can also provide instant notification functions in emergencies. The service provider provides locally focused information. Curation of local news feeds is done, for example, based on news selection criteria and information sources. Instant notification functions in emergencies are done, for example, based on the timing and means of notification. This enables locally focused information provision. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input information from local community groups into a generating AI, and the generating AI can perform local news feed curation and instant notification functions.

[0039] The data collection unit can analyze past disaster data and select the optimal data collection method. For example, the data collection unit can analyze past typhoon data and determine the timing of data collection based on the typhoon's path. The data collection unit can also analyze past earthquake data and adjust the frequency of data collection based on the probability of aftershocks. The data collection unit can analyze past flood data and select a data collection method based on the rise in river water levels. In this way, the optimal data collection method can be selected by analyzing past disaster data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past disaster data into a generating AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter data based on regional characteristics and seasons during data collection. For example, in winter, the data collection unit can prioritize collecting data from areas with a high risk of avalanches. In summer, the data collection unit can also prioritize collecting data from areas with a high risk of heatstroke. In areas where earthquakes frequently occur, the data collection unit can prioritize collecting earthquake data. By filtering data based on regional characteristics and seasons, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on regional characteristics and seasons into a generating AI, which can then perform data filtering.

[0041] 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 in the path of a typhoon, the data collection unit will prioritize the collection of typhoon-related data. If the user is near the epicenter of an earthquake, the data collection unit can also prioritize the collection of earthquake-related data. If the user is in a flood-prone area, the data collection unit will prioritize the collection of flood-related data. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about typhoons on social media, the data collection unit can collect typhoon-related data. If a user posts about earthquakes, the data collection unit can also collect earthquake-related data. If a user posts about floods, the data collection unit can collect flood-related data. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's social media activity into a generating AI, which can then collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a standard analysis on normal data. The analysis unit performs a rapid analysis on urgent data. This allows for more detailed analysis of more important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the importance of the data into a generating AI, which can then adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a meteorological analysis algorithm to meteorological data. The analysis unit can also apply an earthquake analysis algorithm to earthquake data. The analysis unit can apply a flood analysis algorithm to flood data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the data category into an AI that generates data, and the generating AI can apply different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also perform normal analysis on historical data. The analysis unit prioritizes the analysis of urgent data. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the data collection timing into a generating AI, which can then determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can also perform normal analysis on less relevant data. The analysis unit prioritizes the analysis of urgent data. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the relevance of the data into a generating AI, which can then adjust the order of analysis.

[0047] The generation unit can adjust the level of detail of disaster prevention information based on regional characteristics during generation. For example, the generation unit can provide detailed earthquake information to areas prone to frequent earthquakes. It can also provide detailed typhoon information to areas prone to typhoons. It can provide detailed flood information to areas prone to floods. By adjusting the level of detail of disaster prevention information based on regional characteristics, more appropriate information can be provided. Some or all of the above-described processing in the generation unit may be performed using AI or not. For example, the generation unit can input information about regional characteristics into a generation AI, which can then adjust the level of detail of the disaster prevention information.

[0048] The generation unit can apply different generation algorithms depending on the type of disaster during generation. For example, the generation unit can apply an earthquake generation algorithm to earthquake information. The generation unit can also apply a typhoon generation algorithm to typhoon information. The generation unit can apply a flood generation algorithm to flood information. By applying different generation algorithms depending on the type of disaster, more appropriate disaster prevention information can be generated. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input information about the type of disaster into a generation AI, and the generation AI can apply different generation algorithms.

[0049] The generation unit can determine the priority of disaster prevention information based on the data collection timing during generation. For example, the generation unit may prioritize providing disaster prevention information based on the latest data. The generation unit may also provide regular disaster prevention information based on historical data. The generation unit may provide disaster prevention information with the highest priority based on emergency data. This ensures that the latest information is provided preferentially by determining the priority of disaster prevention information based on the data collection timing. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may input information about the data collection timing into a generation AI, which can then determine the priority of disaster prevention information.

[0050] The generation unit can adjust the order of disaster prevention information based on the relevance of the data during generation. For example, the generation unit may prioritize providing disaster prevention information based on highly relevant data. The generation unit may also provide regular disaster prevention information based on less relevant data. The generation unit may prioritize providing disaster prevention information based on emergency data. This allows for the priority provision of more relevant information by adjusting the order of disaster prevention information based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may input information about the relevance of the data into a generation AI, which can then adjust the order of the disaster prevention information.

[0051] The service provider can select the optimal service delivery method by referring to the user's past behavior history at the time of delivery. For example, the service provider can select the optimal service delivery method based on methods the user has used in the past. The service provider can also select the most effective service delivery method from the user's past behavior history. The service provider analyzes the user's past behavior history and provides disaster prevention information at the optimal time. This allows the service provider to select the optimal service delivery method by referring to the user's past behavior history. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past behavior history into a generating AI, which can then select the optimal service delivery method.

[0052] The service provider can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide disaster prevention information via push notification. If the user is using a smart home device, the service provider can also provide disaster prevention information via voice. If the user is using a PC, the service provider can provide disaster prevention information via browser notification. This allows the service provider to select the optimal delivery method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into a generating AI, which can then select the optimal delivery method.

[0053] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in the path of a typhoon, the service provider will provide typhoon-related disaster prevention information. If the user is near the epicenter of an earthquake, the service provider can also provide earthquake-related disaster prevention information. If the user is in a flood-prone area, the service provider will provide flood-related disaster prevention information. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into a generating AI, and the generating AI can select the optimal delivery method.

[0054] The service provider can analyze the user's social media activity and provide relevant disaster prevention information at the time of delivery. For example, if the user has posted about typhoons on social media, the service provider can provide typhoon-related disaster prevention information. If the user has posted about earthquakes, the service provider can also provide earthquake-related disaster prevention information. If the user has posted about floods, the service provider can provide flood-related disaster prevention information. In this way, relevant disaster prevention information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity into a generating AI, and the generating AI can provide relevant disaster prevention information.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze past disaster data and select the optimal data collection method. For example, the data collection unit can analyze past typhoon data and determine the timing of data collection based on the typhoon's path. The data collection unit can also analyze past earthquake data and adjust the frequency of data collection based on the probability of aftershocks. The data collection unit can analyze past flood data and select a data collection method based on the rise in river water levels. In this way, the optimal data collection method can be selected by analyzing past disaster data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past disaster data into a generating AI, which can then select the optimal data collection method.

[0057] The data collection unit can filter data based on regional characteristics and seasons during data collection. For example, in winter, the data collection unit can prioritize collecting data from areas with a high risk of avalanches. In summer, the data collection unit can also prioritize collecting data from areas with a high risk of heatstroke. In areas where earthquakes frequently occur, the data collection unit can prioritize collecting earthquake data. By filtering data based on regional characteristics and seasons, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on regional characteristics and seasons into a generating AI, which can then perform data filtering.

[0058] 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 in the path of a typhoon, the data collection unit will prioritize the collection of typhoon-related data. If the user is near the epicenter of an earthquake, the data collection unit can also prioritize the collection of earthquake-related data. If the user is in a flood-prone area, the data collection unit will prioritize the collection of flood-related data. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0059] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about typhoons on social media, the data collection unit can collect typhoon-related data. If a user posts about earthquakes, the data collection unit can also collect earthquake-related data. If a user posts about floods, the data collection unit can collect flood-related data. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's social media activity into a generating AI, which can then collect relevant data.

[0060] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a standard analysis on normal data. The analysis unit performs a rapid analysis on urgent data. This allows for more detailed analysis of more important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the importance of the data into a generating AI, which can then adjust the level of detail of the analysis.

[0061] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a meteorological analysis algorithm to meteorological data. The analysis unit can also apply an earthquake analysis algorithm to earthquake data. The analysis unit can apply a flood analysis algorithm to flood data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the data category into an AI that generates data, and the generating AI can apply different analysis algorithms.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects data. For example, it can collect meteorological data from the Japan Meteorological Agency, earthquake data from the Earthquake Research Institute, and reports from residents and local governments. The data collection unit collects data in real time, ensuring that the latest information is always available. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. For example, it can analyze weather data to predict the path of a typhoon, or analyze earthquake data to predict the probability of aftershocks. The analysis unit also performs future prediction simulations to predict the probability of disasters occurring and the extent of their impact. These processes may or may not be performed using AI. Step 3: The generation unit generates disaster prevention information based on the analysis results obtained by the analysis unit. For example, it can generate disaster prevention information that issues evacuation advisories to areas in the path of a typhoon, and generate disaster prevention information that provides information on evacuation shelters to areas heavily affected by earthquakes. The generation unit generates disaster prevention information tailored to the needs of each region. These processes may or may not be performed using AI. Step 4: The providing unit provides the disaster prevention information generated by the generating unit. For example, disaster prevention information can be provided through a smartphone app or through smart home devices. The providing unit can also provide disaster prevention information through data sharing with city halls and other public institutions. These processes may or may not be performed using AI.

[0064] (Example of form 2) The disaster prevention information provision system according to an embodiment of the present invention is a system that utilizes an AI agent to reduce natural disaster risks and realize the rapid provision of disaster prevention information. This disaster prevention information provision system collects data such as weather data, earthquake data, and reports from the field, analyzes it in real time to perform future prediction simulations, and generates disaster prevention information tailored to the needs of each region. The generated disaster prevention information is provided to users through smartphone apps, smart home devices, and data sharing with city halls and public institutions. This enables swift action and minimizes damage. For example, the disaster prevention information provision system collects weather data from the Japan Meteorological Agency, earthquake data from the Earthquake Research Institute, and reports from residents and local governments. Next, the disaster prevention information provision system analyzes the collected data in real time and performs future prediction simulations. For example, it analyzes weather data to predict the path of a typhoon or analyzes earthquake data to predict the probability of aftershocks. Based on the analysis results, the disaster prevention information provision system generates disaster prevention information tailored to the needs of each region. For example, it issues evacuation advisories to areas in the path of a typhoon and provides information on evacuation centers to areas heavily affected by earthquakes. Furthermore, the generated disaster prevention information will be provided to users through smartphone apps, smart home devices, and data sharing with city halls and public institutions. This will enable users to take swift action and minimize damage. In addition, the disaster prevention information provision system will optimize the user interface and provide access support for the elderly and vulnerable. For example, the smartphone app will offer an intuitive interface and add features such as voice guidance and large font display. Smart home devices will also provide voice information and automatic emergency notification functions. In this way, the disaster prevention information provision system can reduce the risk of natural disasters and provide disaster prevention information quickly by utilizing AI agents. This will enable residents to take swift action and minimize damage. Furthermore, by collaborating with local community groups and providing curated local news feeds and immediate notification functions in emergencies, it will be possible to provide information that is closely tailored to the local community.Furthermore, it is expected to be deployed as part of smart city development, expanded to other regions and overseas, and applied to crisis management environments beyond disaster prevention. This will enable the disaster information provision system to reduce natural disaster risks and provide disaster information quickly.

[0065] The disaster prevention information provision system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. For example, the collection unit collects meteorological data from the Japan Meteorological Agency. The collection unit can also collect earthquake data from the Earthquake Research Institute. The collection unit can also collect reports from the field from residents and local governments. The collection unit collects data in real time and can always obtain the latest information. The analysis unit analyzes the data collected by the collection unit in real time. For example, the analysis unit analyzes meteorological data to predict the path of a typhoon. The analysis unit can also analyze earthquake data to predict the probability of aftershocks. The analysis unit performs future prediction simulations to predict the probability of disaster occurrence and the extent of impact. Some or all of the above-described processes in the analysis unit may be performed using AI or not. The generation unit generates disaster prevention information based on the analysis results obtained by the analysis unit. For example, the generation unit generates disaster prevention information that issues evacuation advisories to areas in the path of a typhoon. The generation unit can also generate disaster prevention information that provides information on evacuation shelters to areas greatly affected by earthquakes. The generation unit generates disaster prevention information tailored to the needs of each region. Some or all of the processing described above in the generation unit may be performed using AI or not. The provision unit provides the disaster prevention information generated by the generation unit. The provision unit provides disaster prevention information, for example, through a smartphone application. The provision unit can also provide disaster prevention information through smart home devices. The provision unit can also provide disaster prevention information through data sharing with city halls and other public institutions. Some or all of the processing described above in the provision unit may be performed using AI or not. As a result, the disaster prevention information provision system according to this embodiment efficiently collects, analyzes, generates, and provides data.

[0066] The data collection unit collects data. For example, the data collection unit collects meteorological data from the Japan Meteorological Agency. Specifically, it uses APIs provided by the Japan Meteorological Agency to obtain detailed meteorological data such as rainfall, wind speed, temperature, and humidity in real time. This allows the data collection unit to always have access to the latest information on meteorological phenomena such as typhoons and heavy rains. The data collection unit can also collect earthquake data from the Earthquake Research Institute. It obtains earthquake observation data provided by the Earthquake Research Institute and collects information such as the time of earthquake occurrence, epicenter, magnitude, and seismic intensity distribution. This enables the data collection unit to respond quickly immediately after an earthquake. The data collection unit can also collect reports from residents and local governments. It collects information reported by residents and local governments through dedicated applications and web portals to understand the extent of damage and evacuation in the area. This allows the data collection unit to obtain real-time information from the area and respond quickly. The data collection unit collects data in real time and can always obtain the latest information. This allows the data collection unit to collect a wide range of information from diverse data sources and improve the overall accuracy of the system. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by increasing the frequency of meteorological data collection when a typhoon approaches and increasing the frequency of earthquake data collection when an earthquake occurs, it becomes possible to provide information quickly and accurately. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0067] The analysis unit analyzes the data collected by the collection unit in real time. For example, the analysis unit analyzes meteorological data to predict the path of a typhoon. Specifically, based on meteorological data, it analyzes the typhoon's center position, direction of movement, speed, intensity, etc., to predict its future path. Numerical weather prediction models and statistical models can be used for this. The analysis unit can also analyze earthquake data to predict the probability of aftershocks. Based on earthquake data, it analyzes the characteristics of the epicenter and past aftershock data to predict the probability and magnitude of aftershocks. Probabilistic methods and machine learning models can be used for this. The analysis unit performs future prediction simulations to predict the probability of disasters occurring and the extent of their impact. For example, it performs flood simulations and predicts the probability of floods occurring and the extent of flooding based on rainfall and topographic data. Fluid dynamics models and geographic information systems (GIS) can be used for this. Some or all of the above-described processes in the analysis unit may be performed using AI or not. When using AI, techniques such as deep learning and reinforcement learning can be used to improve the accuracy of data analysis and prediction. For example, deep learning can be used to analyze weather data and build a typhoon path prediction model. This allows the analysis unit to quickly and accurately analyze collected data and grasp disaster risks in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0068] The generation unit generates disaster prevention information based on the analysis results obtained by the analysis unit. For example, the generation unit generates disaster prevention information that issues evacuation advisories to areas in the path of a typhoon. Specifically, it identifies areas that may be affected based on typhoon path prediction data and issues evacuation advisories to residents in those areas. The generation unit can also generate disaster prevention information that provides information on evacuation shelters to areas heavily affected by earthquakes. Based on earthquake seismic intensity distribution data, it identifies areas predicted to suffer significant damage and provides residents in those areas with information on the location of evacuation shelters and evacuation routes. The generation unit generates disaster prevention information tailored to the needs of each region. For example, it provides information on special assistance needed in areas with a large elderly or disabled population. Some or all of the above processing in the generation unit may be performed using AI or not. When using AI, natural language generation (NLG) technology can be used to automatically generate disaster prevention information based on the analysis results. For example, NLG technology can be used to automatically generate evacuation advisory text based on typhoon path prediction data. This allows the generation unit to quickly and accurately generate and provide disaster prevention information to residents. Furthermore, the generation unit can continuously improve the content of the generated disaster prevention information. For example, by evaluating the effectiveness of past disaster prevention information and reviewing the content and expression of the information based on the results, it can provide more effective disaster prevention information. In this way, the generation unit can generate appropriate disaster prevention information that meets the needs of each region and ensure the safety of residents.

[0069] The provisioning unit provides disaster prevention information generated by the generation unit. The provisioning unit provides disaster prevention information, for example, through a smartphone app. Specifically, it notifies residents of real-time updated disaster prevention information through a smartphone app. The app has a push notification function, allowing information to be immediately transmitted to residents in emergencies. The provisioning unit can also provide disaster prevention information through smart home devices. Smart home devices are equipped with voice assistants and displays, providing disaster prevention information through voice and visual means. This allows information to be transmitted to residents with visual or hearing impairments. The provisioning unit can also provide disaster prevention information through data sharing with city halls and public institutions. Based on the disaster prevention information received from the provisioning unit, city halls and public institutions can formulate local disaster prevention plans and issue appropriate instructions to residents. Some or all of the above-described processes in the provisioning unit may be performed using AI or not. When using AI, it is possible to provide individually customized disaster prevention information based on the user's behavior history and location information. For example, it is possible to prioritize notifications of evacuation routes and shelters that the user frequently uses. This allows the service provider to quickly provide appropriate disaster prevention information to each user, minimizing the risk of disaster. Furthermore, the service provider can collect feedback from users and continuously improve the accuracy and effectiveness of the information provided. As a result, the service provider can quickly and reliably provide disaster prevention information to users and ensure the safety of residents.

[0070] The analysis unit includes a simulation unit that performs future prediction simulations. For example, the analysis unit performs simulations to predict the path of a typhoon by analyzing meteorological data. The analysis unit can also perform simulations to predict the probability of aftershocks by analyzing earthquake data. The analysis unit performs future prediction simulations to predict the probability of disaster occurrence and the extent of its impact. Future prediction simulations are performed using, for example, meteorological models and earthquake prediction models. Meteorological models predict the path of a typhoon and rainfall based on meteorological data. Earthquake prediction models determine the probability of aftershocks and identify the epicenters based on earthquake data. As a result, the probability of disaster occurrence and the extent of its impact can be predicted by future prediction simulations. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input meteorological data and earthquake data into a generating AI, and the generating AI can perform future prediction simulations.

[0071] The information provision unit includes a collaboration unit that handles data sharing with smartphone apps, smart home devices, and city halls and public institutions. The information provision unit can, for example, provide disaster prevention information through a smartphone app. The information provision unit can also provide disaster prevention information through smart home devices. The information provision unit can also provide disaster prevention information through data sharing with city halls and public institutions. Through data sharing, the information provision unit can quickly provide disaster prevention information to users. Data sharing provides disaster prevention information in real time, for example, through a smartphone app. Smart home devices can provide disaster prevention information by voice. Data sharing with city halls and public institutions allows for the sharing of local disaster prevention information and enables a rapid response. This allows for the quick provision of disaster prevention information to users through data sharing. Some or all of the above-described processes in the information provision unit may be performed using AI or not. For example, the information provision unit can input disaster prevention information into a generating AI for a smartphone app or smart home device, and the generating AI can provide the disaster prevention information.

[0072] The service provider includes a support unit that optimizes the user interface and provides access support for the elderly and vulnerable. For example, the service provider provides an intuitive interface for smartphone applications. The service provider can also add features such as voice guidance and large font displays. The service provider can also provide voice information and automatic emergency notification functions for smart home devices. By optimizing the user interface, the service provider makes disaster prevention information easily accessible to everyone. User interface optimization improves usability, visibility, and operability, for example. Access support for the elderly and vulnerable includes providing voice guidance, large font displays, and simple operation methods. This makes disaster prevention information easily accessible to everyone. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input data for user interface optimization and access support into a generating AI, which can then provide an optimal interface.

[0073] The generation unit can generate disaster prevention information tailored to the needs of each region. For example, the generation unit can generate disaster prevention information that issues evacuation advisories to areas in the path of a typhoon. The generation unit can also generate disaster prevention information that provides information on evacuation shelters to areas heavily affected by earthquakes. The generation unit generates disaster prevention information tailored to the needs of each region. These regional needs are determined, for example, based on the disaster risks of the region and the requests of the residents. The generation unit generates appropriate disaster prevention information based on these regional needs. This allows for the provision of disaster prevention information tailored to the needs of each region. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input data on regional needs into a generation AI, and the generation AI can generate disaster prevention information tailored to the regional needs.

[0074] The service provider can collaborate with local community groups to provide curated local news feeds and instant notification functions in emergencies. For example, the service provider can collaborate with local community groups to curate local news feeds. The service provider can also provide instant notification functions in emergencies. The service provider provides locally focused information. Curation of local news feeds is done, for example, based on news selection criteria and information sources. Instant notification functions in emergencies are done, for example, based on the timing and means of notification. This enables locally focused information provision. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input information from local community groups into a generating AI, and the generating AI can perform local news feed curation and instant notification functions.

[0075] 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 collect data frequently and provide up-to-date information. If the user is relaxed, the data collection unit can reduce the frequency of data collection and provide information only when necessary. If the user is facing an emergency, the data collection unit can collect data immediately and provide information quickly. This allows for more appropriate information to be provided by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of data collection.

[0076] The data collection unit can analyze past disaster data and select the optimal data collection method. For example, the data collection unit can analyze past typhoon data and determine the timing of data collection based on the typhoon's path. The data collection unit can also analyze past earthquake data and adjust the frequency of data collection based on the probability of aftershocks. The data collection unit can analyze past flood data and select a data collection method based on the rise in river water levels. In this way, the optimal data collection method can be selected by analyzing past disaster data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past disaster data into a generating AI, which can then select the optimal data collection method.

[0077] The data collection unit can filter data based on regional characteristics and seasons during data collection. For example, in winter, the data collection unit can prioritize collecting data from areas with a high risk of avalanches. In summer, the data collection unit can also prioritize collecting data from areas with a high risk of heatstroke. In areas where earthquakes frequently occur, the data collection unit can prioritize collecting earthquake data. By filtering data based on regional characteristics and seasons, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on regional characteristics and seasons into a generating AI, which can then perform data filtering.

[0078] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can perform normal data collection. If the user is facing an emergency, the data collection unit will prioritize collecting the most important data. This allows for the priority collection of more important information by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the data prioritization.

[0079] 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 in the path of a typhoon, the data collection unit will prioritize the collection of typhoon-related data. If the user is near the epicenter of an earthquake, the data collection unit can also prioritize the collection of earthquake-related data. If the user is in a flood-prone area, the data collection unit will prioritize the collection of flood-related data. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0080] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about typhoons on social media, the data collection unit can collect typhoon-related data. If a user posts about earthquakes, the data collection unit can also collect earthquake-related data. If a user posts about floods, the data collection unit can collect flood-related data. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's social media activity into a generating AI, which can then collect relevant data.

[0081] 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 provides a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is facing an emergency, the analysis unit provides a rapid analysis result. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the presentation of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a standard analysis on normal data. The analysis unit performs a rapid analysis on urgent data. This allows for more detailed analysis of more important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the importance of the data into a generating AI, which can then adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a meteorological analysis algorithm to meteorological data. The analysis unit can also apply an earthquake analysis algorithm to earthquake data. The analysis unit can apply a flood analysis algorithm to flood data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the data category into an AI that generates data, and the generating AI can apply different analysis algorithms.

[0084] 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. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is facing an emergency, the analysis unit can provide a rapid analysis result. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI, which can then adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also perform normal analysis on historical data. The analysis unit prioritizes the analysis of urgent data. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the data collection timing into a generating AI, which can then determine the priority of analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can also perform normal analysis on less relevant data. The analysis unit prioritizes the analysis of urgent data. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the relevance of the data into a generating AI, which can then adjust the order of analysis.

[0087] The generation unit can estimate the user's emotions and adjust the way disaster prevention information is presented based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will generate simple and easy-to-understand disaster prevention information. If the user is relaxed, the generation unit can also generate detailed disaster prevention information. If the user is facing an emergency, the generation unit will quickly generate disaster prevention information. This allows for more appropriate information to be provided by adjusting the way disaster prevention information is presented 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 is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the way disaster prevention information is presented.

[0088] The generation unit can adjust the level of detail of disaster prevention information based on regional characteristics during generation. For example, the generation unit can provide detailed earthquake information to areas prone to frequent earthquakes. It can also provide detailed typhoon information to areas prone to typhoons. It can provide detailed flood information to areas prone to floods. By adjusting the level of detail of disaster prevention information based on regional characteristics, more appropriate information can be provided. Some or all of the above-described processing in the generation unit may be performed using AI or not. For example, the generation unit can input information about regional characteristics into a generation AI, which can then adjust the level of detail of the disaster prevention information.

[0089] The generation unit can apply different generation algorithms depending on the type of disaster during generation. For example, the generation unit can apply an earthquake generation algorithm to earthquake information. The generation unit can also apply a typhoon generation algorithm to typhoon information. The generation unit can apply a flood generation algorithm to flood information. By applying different generation algorithms depending on the type of disaster, more appropriate disaster prevention information can be generated. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input information about the type of disaster into a generation AI, and the generation AI can apply different generation algorithms.

[0090] The generation unit can estimate the user's emotions and determine the priority of disaster prevention information to generate based on the estimated user emotions. For example, if the user is feeling anxious, the generation unit will prioritize providing important disaster prevention information. If the user is relaxed, the generation unit can also provide normal disaster prevention information. If the user is facing an emergency, the generation unit will immediately provide the most important disaster prevention information. In this way, by prioritizing disaster prevention information according to the user's emotions, more important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into a generation AI, and the generation AI can determine the priority of disaster prevention information.

[0091] The generation unit can determine the priority of disaster prevention information based on the data collection timing during generation. For example, the generation unit may prioritize providing disaster prevention information based on the latest data. The generation unit may also provide regular disaster prevention information based on historical data. The generation unit may provide disaster prevention information with the highest priority based on emergency data. This ensures that the latest information is provided preferentially by determining the priority of disaster prevention information based on the data collection timing. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may input information about the data collection timing into a generation AI, which can then determine the priority of disaster prevention information.

[0092] The generation unit can adjust the order of disaster prevention information based on the relevance of the data during generation. For example, the generation unit may prioritize providing disaster prevention information based on highly relevant data. The generation unit may also provide regular disaster prevention information based on less relevant data. The generation unit may prioritize providing disaster prevention information based on emergency data. This allows for the priority provision of more relevant information by adjusting the order of disaster prevention information based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may input information about the relevance of the data into a generation AI, which can then adjust the order of the disaster prevention information.

[0093] The information provider can estimate the user's emotions and adjust the method of providing disaster prevention information based on the estimated emotions. For example, if the user is feeling anxious, the information provider will provide disaster prevention information in a simple and easy-to-understand manner. If the user is relaxed, the information provider may also provide detailed disaster prevention information. If the user is facing an emergency, the information provider will provide disaster prevention information quickly. This allows for more appropriate information to be provided by adjusting the method of providing disaster prevention information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI, which can then adjust the method of providing disaster prevention information.

[0094] The service provider can select the optimal service delivery method by referring to the user's past behavior history at the time of delivery. For example, the service provider can select the optimal service delivery method based on methods the user has used in the past. The service provider can also select the most effective service delivery method from the user's past behavior history. The service provider analyzes the user's past behavior history and provides disaster prevention information at the optimal time. This allows the service provider to select the optimal service delivery method by referring to the user's past behavior history. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past behavior history into a generating AI, which can then select the optimal service delivery method.

[0095] The service provider can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide disaster prevention information via push notification. If the user is using a smart home device, the service provider can also provide disaster prevention information via voice. If the user is using a PC, the service provider can provide disaster prevention information via browser notification. This allows the service provider to select the optimal delivery method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into a generating AI, which can then select the optimal delivery method.

[0096] The information provider can estimate the user's emotions and adjust the timing of disaster prevention information delivery based on the estimated emotions. For example, if the user is feeling anxious, the information provider can quickly deliver disaster prevention information. If the user is relaxed, the information provider can also deliver disaster prevention information at the normal time. If the user is facing an emergency, the information provider can deliver disaster prevention information immediately. By adjusting the timing of disaster prevention information delivery according to the user's emotions, it becomes possible to deliver information at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI, and the generative AI can adjust the timing of disaster prevention information delivery.

[0097] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in the path of a typhoon, the service provider will provide typhoon-related disaster prevention information. If the user is near the epicenter of an earthquake, the service provider can also provide earthquake-related disaster prevention information. If the user is in a flood-prone area, the service provider will provide flood-related disaster prevention information. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into a generating AI, and the generating AI can select the optimal delivery method.

[0098] The service provider can analyze the user's social media activity and provide relevant disaster prevention information at the time of delivery. For example, if the user has posted about typhoons on social media, the service provider can provide typhoon-related disaster prevention information. If the user has posted about earthquakes, the service provider can also provide earthquake-related disaster prevention information. If the user has posted about floods, the service provider can provide flood-related disaster prevention information. In this way, relevant disaster prevention information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity into a generating AI, and the generating AI can provide relevant disaster prevention information.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] 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 collect data frequently and provide up-to-date information. If the user is relaxed, the data collection unit can reduce the frequency of data collection and provide information only when necessary. If the user is facing an emergency, the data collection unit can collect data immediately and provide information quickly. This allows for more appropriate information to be provided by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of data collection.

[0101] The data collection unit can analyze past disaster data and select the optimal data collection method. For example, the data collection unit can analyze past typhoon data and determine the timing of data collection based on the typhoon's path. The data collection unit can also analyze past earthquake data and adjust the frequency of data collection based on the probability of aftershocks. The data collection unit can analyze past flood data and select a data collection method based on the rise in river water levels. In this way, the optimal data collection method can be selected by analyzing past disaster data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past disaster data into a generating AI, which can then select the optimal data collection method.

[0102] The data collection unit can filter data based on regional characteristics and seasons during data collection. For example, in winter, the data collection unit can prioritize collecting data from areas with a high risk of avalanches. In summer, the data collection unit can also prioritize collecting data from areas with a high risk of heatstroke. In areas where earthquakes frequently occur, the data collection unit can prioritize collecting earthquake data. By filtering data based on regional characteristics and seasons, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on regional characteristics and seasons into a generating AI, which can then perform data filtering.

[0103] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can perform normal data collection. If the user is facing an emergency, the data collection unit will prioritize collecting the most important data. This allows for the priority collection of more important information by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the data prioritization.

[0104] 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 in the path of a typhoon, the data collection unit will prioritize the collection of typhoon-related data. If the user is near the epicenter of an earthquake, the data collection unit can also prioritize the collection of earthquake-related data. If the user is in a flood-prone area, the data collection unit will prioritize the collection of flood-related data. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0105] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about typhoons on social media, the data collection unit can collect typhoon-related data. If a user posts about earthquakes, the data collection unit can also collect earthquake-related data. If a user posts about floods, the data collection unit can collect flood-related data. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's social media activity into a generating AI, which can then collect relevant data.

[0106] 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 provides a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is facing an emergency, the analysis unit provides a rapid analysis result. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the presentation of the analysis.

[0107] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a standard analysis on normal data. The analysis unit performs a rapid analysis on urgent data. This allows for more detailed analysis of more important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the importance of the data into a generating AI, which can then adjust the level of detail of the analysis.

[0108] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a meteorological analysis algorithm to meteorological data. The analysis unit can also apply an earthquake analysis algorithm to earthquake data. The analysis unit can apply a flood analysis algorithm to flood data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the data category into an AI that generates data, and the generating AI can apply different analysis algorithms.

[0109] 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. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is facing an emergency, the analysis unit can provide a rapid analysis result. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI, which can then adjust the length of the analysis.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects data. For example, it can collect meteorological data from the Japan Meteorological Agency, earthquake data from the Earthquake Research Institute, and reports from residents and local governments. The data collection unit collects data in real time, ensuring that the latest information is always available. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. For example, it can analyze weather data to predict the path of a typhoon, or analyze earthquake data to predict the probability of aftershocks. The analysis unit also performs future prediction simulations to predict the probability of disasters occurring and the extent of their impact. These processes may or may not be performed using AI. Step 3: The generation unit generates disaster prevention information based on the analysis results obtained by the analysis unit. For example, it can generate disaster prevention information that issues evacuation advisories to areas in the path of a typhoon, and generate disaster prevention information that provides information on evacuation shelters to areas heavily affected by earthquakes. The generation unit generates disaster prevention information tailored to the needs of each region. These processes may or may not be performed using AI. Step 4: The providing unit provides the disaster prevention information generated by the generating unit. For example, disaster prevention information can be provided through a smartphone app or through smart home devices. The providing unit can also provide disaster prevention information through data sharing with city halls and other public institutions. These processes may or may not be performed using AI.

[0112] 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in 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 microphone 38B of the smart device 14 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The generation unit is implemented in real time by the specific processing unit 290 of the data processing unit 12 and generates disaster prevention information based on the analysis results. The provision unit is implemented in real time by the control unit 46A of the smart device 14 and provides the generated disaster prevention information to the user via a smartphone app or smart home device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented 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 microphone 238 of the smart glasses 214 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in real time by the specific processing unit 290 of the data processing unit 12 and generates disaster prevention information based on the analysis results. The provision unit is implemented in real time by the control unit 46A of the smart glasses 214 and provides the generated disaster prevention information to the user via a smartphone app or smart home device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in 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 microphone 238 of the headset terminal 314 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The generation unit is implemented in real time by the specific processing unit 290 of the data processing unit 12 and generates disaster prevention information based on the analysis results. The provision unit is implemented in real time by the control unit 46A of the headset terminal 314 and provides the generated disaster prevention information to the user via a smartphone app or smart home device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, 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 microphone 238 of the robot 414 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates disaster prevention information based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated disaster prevention information to the user via a smartphone app or smart home device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A generation unit that generates disaster prevention information based on the analysis results obtained by the aforementioned analysis unit, The system comprises a providing unit that provides disaster prevention information generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It is equipped with a simulation unit that performs future prediction simulations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It includes a department that handles data sharing with smartphone apps, smart home devices, and city halls and other public institutions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It includes a support department that optimizes the user interface and provides access assistance for the elderly and vulnerable. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate disaster prevention information tailored to the needs of each region. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We collaborate with local community groups to provide curated local news feeds and instant notification features during emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 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 described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past disaster data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on regional characteristics and season. The system described in Appendix 1, characterized by the features described herein. (Note 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 described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the user's emotions and adjust the way disaster prevention information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail in disaster prevention information is adjusted based on regional characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of disaster prevention information to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of disaster prevention information is determined based on the data collection period. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of disaster prevention information is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the method of providing disaster prevention information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of disaster prevention information delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the system analyzes the user's social media activity and provides relevant disaster prevention information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 in real time, A generation unit that generates disaster prevention information based on the analysis results obtained by the aforementioned analysis unit, The system comprises a providing unit that provides disaster prevention information generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, It is equipped with a simulation unit that performs future prediction simulations. The system according to feature 1.

3. The aforementioned supply unit is, It includes a department that handles data sharing with smartphone apps, smart home devices, and city halls and other public institutions. The system according to feature 1.

4. The aforementioned supply unit is, It includes a support department that optimizes the user interface and provides access assistance for the elderly and vulnerable. The system according to feature 1.

5. The generating unit is Generate disaster prevention information tailored to the needs of each region. The system according to feature 1.

6. The aforementioned supply unit is, We collaborate with local community groups to provide curated local news feeds and instant notification features during emergencies. 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 Analyze past disaster data and select the optimal data collection method. The system according to feature 1.

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