system

Abstract

The system according to the embodiment aims to update a user's action plan and evacuation route in real time during a disaster and provide an appropriate detour route. [Solution] A system according to an embodiment includes a registration unit, a collection unit, an analysis unit, and a recommendation unit. The registration unit registers a user's action plan or evacuation route. The collection unit collects disaster information. The analysis unit analyzes the disaster information collected by the collection unit and the user's action plan or evacuation route. The recommendation unit recommends a detour route based on the information analyzed by the analysis unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] With conventional technology, users' action plans and evacuation routes are not updated in real time during a disaster, making it difficult to provide appropriate detour routes.

[0005] The system according to the embodiment aims to update a user's action plan and evacuation route in real time during a disaster and provide an appropriate detour route. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a collection unit, an analysis unit, and a recommendation unit. The registration unit registers a user's action plan or evacuation route. The collection unit collects disaster information. The analysis unit analyzes the disaster information collected by the collection unit and the user's action plan or evacuation route. The recommendation unit recommends a detour route based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can update a user's action plan and evacuation route in real time during a disaster and provide an appropriate detour route. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0028] (Example 1) A disaster prevention system according to an embodiment of the present invention allows users to evacuate quickly and safely in the event of a disaster. This disaster prevention system registers the user's action plan and evacuation route, collects and analyzes disaster information, and recommends optimal detour routes. First, in preparation for a disaster, the user registers their action plan and evacuation route as their "My Timeline." For example, they record details such as the route from their home to an evacuation shelter and how to contact their family. This information is stored in the system. Next, when a disaster occurs, the system collects disaster information in real time. For example, disaster information on earthquakes, typhoons, etc. is obtained from the Japan Meteorological Agency and local governments. This information is input into a generation AI. The generation AI analyzes the collected disaster information and the user's "My Timeline" and recommends optimal detour routes. For example, it proposes a safe evacuation route based on information such as road collapses caused by earthquakes or floods caused by typhoons. This proposal is notified to the user's smartphone. This allows the user to evacuate quickly and safely in the event of a disaster. For example, if a road collapses on the user's way to an evacuation shelter, the generation AI proposes a detour route, allowing the user to safely reach the shelter. In addition, contact methods with family members are also registered in My Timeline, allowing for quick contact in the event of a disaster. This allows the disaster prevention system to register the user's action plan and evacuation route, collect and analyze disaster information, and recommend optimal detour routes, enabling quick and safe evacuation in the event of a disaster.

[0029] A disaster prevention system according to an embodiment includes a registration unit, a collection unit, an analysis unit, and a recommendation unit. The registration unit registers a user's action plan and evacuation route. The user's action plan includes, for example, a daily action plan and an emergency action plan. The evacuation route includes, for example, evacuation locations and the safety of the route. The registration unit allows, for example, a user to register a route from their home to an evacuation shelter. The registration unit also allows the user to register a method of contacting family members. The collection unit collects disaster information. The disaster information includes, for example, earthquake information, flood information, and weather information. The collection unit can acquire disaster information from, for example, the Japan Meteorological Agency or local governments. The collection unit can also collect disaster information from social networking sites and news sites. The analysis unit analyzes the disaster information collected by the collection unit and the user's action plan and evacuation route. The analysis unit analyzes the collected disaster information and the user's action plan and evacuation route, for example, using a generation AI. The generation AI suggests a safe evacuation route for the user based on, for example, information about road collapses due to earthquakes and floods due to typhoons. The recommendation unit recommends an optimal detour route based on the information analyzed by the analysis unit. The recommendation unit can, for example, notify the user's smartphone of the optimal detour route. As a result, the disaster prevention system according to the embodiment can register the user's action plan and evacuation route, collect and analyze disaster information, and recommend the optimal detour route, thereby enabling the user to evacuate quickly and safely in the event of a disaster.

[0030] The input method unit can diversify the input methods for the user. The input method unit provides multiple input methods, such as voice input, touch input, and keyboard input. For example, by using voice input, the user can register an action plan or an evacuation route without using their hands. Furthermore, by using touch input, the user can input information intuitively using a smartphone or tablet. Furthermore, by using keyboard input, the user can input detailed information accurately. In this way, the input method unit diversifies the input methods for the user, making it easier for more users to use.

[0031] The information source unit can diversify the sources from which disaster information is collected. The information source unit collects disaster information from multiple sources, such as the Japan Meteorological Agency, local governments, social media, and news sites. For example, information from the Japan Meteorological Agency is highly reliable as it is official data. Information from local governments provides detailed information specific to the region. Information from social media allows for real-time information collection, allowing for a quick understanding of the local situation. Information from news sites allows for the provision of information from a broader perspective. This allows the information source unit to collect more accurate and diverse information by diversifying the sources from which disaster information is collected.

[0032] The analysis method unit can indicate an analysis method for the generating AI. The analysis method unit indicates a specific analysis method, such as a machine learning algorithm or a statistical analysis method. For example, by using a machine learning algorithm, it is possible to analyze collected disaster information and a user's action plan and evacuation route, and propose an optimal detour route. Furthermore, by using a statistical analysis method, it is possible to analyze trends and patterns in disaster information and provide appropriate information to the user. In this way, the analysis method unit can improve the accuracy of the analysis by specifically indicating an analysis method for the generating AI.

[0033] The collection unit can collect disaster information from the Japan Meteorological Agency or local governments, social media, and news sites. For example, the collection unit can obtain real-time weather data from the Japan Meteorological Agency using an API. From local governments, the collection unit can collect local disaster information through official websites and emergency notification systems. From social media, the collection unit can collect posts from specific hashtags and reliable users to understand the local situation. From news sites, the collection unit can obtain the latest disaster information from reliable news sources. This allows the collection unit to obtain more accurate and diverse information by collecting disaster information from a variety of sources.

[0034] The recommendation unit can notify the user's smartphone of the optimal detour route. The recommendation unit notifies the user's smartphone of the optimal detour route by methods such as push notification, SMS, and in-app notification. For example, by using push notification, the user can receive detour route information in real time. By using SMS, the user can receive information reliably even if the internet connection is unstable. By using in-app notification, the user can check the latest detour route information when they open the app. As a result, the recommendation unit can notify the user's smartphone of the optimal detour route, allowing for quick and safe evacuation.

[0035] The registration unit can analyze the user's past evacuation history and suggest the optimal registration method. For example, the registration unit automatically displays evacuation routes that the user has frequently used in the past as candidates. The registration unit also preferentially suggests registration methods (voice, text, etc.) that the user has used in the past. Furthermore, the registration unit can predict and suggest evacuation routes to be used at specific times based on the user's past evacuation history. This allows the registration unit to suggest more appropriate registration methods by analyzing the user's past evacuation history.

[0036] At the time of registration, the registration unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user is elderly, the registration unit can prioritize displaying barrier-free information on evacuation routes. Also, if the user has a pet, the registration unit can prioritize suggesting pet-friendly evacuation shelters. Furthermore, if the user lives in a specific area, the registration unit can also suggest evacuation routes based on disaster risk information for that area. In this way, the registration unit can provide more appropriate information by filtering based on the user's living situation and areas of interest.

[0037] During registration, the registration unit can prioritize registering highly relevant information by taking into account the user's geographical location information. For example, the registration unit prioritizes registering information about evacuation shelters closest to the user's current location. Also, if the user is in a specific area, the registration unit prioritizes registering disaster risk information for that area. Furthermore, if the user is traveling, the registration unit can also suggest the optimal evacuation route based on the user's current location. This allows the registration unit to provide more relevant information by taking into account the user's geographical location information.

[0038] At the time of registration, the registration unit can analyze the user's social media activity and register related information. For example, the registration unit can suggest relevant evacuation routes based on evacuation information shared by the user on social media. The registration unit also preferentially registers information from disaster information accounts that the user follows on social media. Furthermore, the registration unit can also suggest optimal evacuation routes based on location information posted by the user on social media. This allows the registration unit to provide more relevant information by analyzing the user's social media activity.

[0039] The collection unit can analyze past disaster information and select the optimal collection method. For example, the collection unit selects the most reliable information source from the past disaster information. The collection unit also analyzes collection patterns of past disaster information and determines the optimal collection timing. Furthermore, the collection unit can evaluate the accuracy of past disaster information and select the most accurate information source. This allows the collection unit to select a more appropriate collection method by analyzing past disaster information.

[0040] The collection unit can apply different collection algorithms depending on the type and scale of the disaster when collecting data. For example, in the case of an earthquake, the collection unit prioritizes collecting seismometer data. In the case of a typhoon, the collection unit prioritizes collecting meteorological satellite data. Furthermore, in the case of a flood, the collection unit can prioritize collecting river water level data. This allows the collection unit to collect more accurate information by applying a collection algorithm depending on the type and scale of the disaster.

[0041] The collection unit can adjust the range of collection based on the location of the disaster when collecting information. For example, the collection unit prioritizes collecting information close to the location of the disaster. Also, the collection unit reduces the frequency of collecting information far from the location of the disaster. Furthermore, the type of information to be collected can also be adjusted based on the location of the disaster. In this way, the collection unit can provide more relevant information by adjusting the collection range based on the location of the disaster.

[0042] The collection unit can improve the accuracy of collection by referring to disaster-related literature during collection. For example, the collection unit evaluates the reliability of the information to be collected based on disaster-related literature. The collection unit also determines the type of information to be collected by referring to disaster-related literature. Furthermore, the collection unit can also determine the priority of the information to be collected based on disaster-related literature. In this way, the collection unit improves the accuracy of collection by referring to disaster-related literature.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between disaster information. For example, the analysis unit performs analysis by taking into account the relationship between earthquakes and tsunamis. Also, the analysis unit performs analysis by taking into account the relationship between typhoons and floods. Furthermore, the analysis unit can also perform analysis by taking into account the relationship between volcanic eruptions and earthquakes. In this way, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships between disaster information.

[0044] The analysis unit can perform the analysis while taking into account the user's attribute information. For example, if the user is elderly, the analysis unit performs the analysis while placing emphasis on the safety of evacuation routes. Also, if the user has pets, the analysis takes into account shelters that allow pets. Furthermore, if the user lives in a specific area, the analysis can also be performed based on disaster risk information for that area. This allows the analysis unit to perform more appropriate analysis by taking into account the user's attribute information.

[0045] The analysis unit can take into account the geographical distribution of disasters during analysis. For example, the analysis unit performs analysis taking into account the epicenter of an earthquake and the extent of damage. The analysis unit also performs analysis taking into account the path of a typhoon and the extent of its impact. Furthermore, the analysis unit can also perform analysis taking into account the location of flood occurrence and the extent of inundation. This allows the analysis unit to perform more accurate analysis by taking into account the geographical distribution of disasters.

[0046] The analysis unit can improve the accuracy of the analysis by referring to disaster-related literature during the analysis. For example, the analysis unit evaluates the reliability of the analysis based on disaster-related literature. The analysis unit also determines analysis criteria by referring to disaster-related literature. Furthermore, the analysis unit can also determine the priority of the analysis based on disaster-related literature. In this way, the analysis unit improves the accuracy of the analysis by referring to disaster-related literature.

[0047] When making a recommendation, the recommendation unit can analyze the user's past evacuation behavior and select the optimal recommendation method. The recommendation unit selects the optimal recommendation method based on, for example, evacuation routes used by the user in the past. Also, the recommendation unit selects a recommendation method that avoids congestion based on the user's past evacuation behavior. Furthermore, the recommendation unit can analyze the user's past evacuation behavior and select the most efficient recommendation method. This allows the recommendation unit to select a more appropriate recommendation method by analyzing past evacuation behavior.

[0048] When making a recommendation, the recommendation unit can customize the recommendation method based on the user's current living situation. For example, if the user is elderly, the recommendation unit makes a recommendation that includes information on barrier-free evacuation routes. Also, if the user has a pet, the recommendation unit can recommend evacuation shelters that allow pets. Furthermore, if the user lives in a specific area, the recommendation unit can also make recommendations based on disaster risk information for that area. This allows the recommendation unit to customize the recommendation method based on the user's living situation, enabling more appropriate recommendations.

[0049] When making a recommendation, the recommendation unit can select the optimal recommendation method by taking into account the user's geographical location information. For example, the recommendation unit prioritizes recommending evacuation shelters that are closest to the user's current location. Also, if the user is in a specific area, the recommendation unit makes recommendations based on disaster risk information for that area. Furthermore, if the user is traveling, the recommendation unit can also recommend the optimal evacuation route based on the user's current location. This allows the recommendation unit to make more appropriate recommendations by taking into account the user's geographical location information.

[0050] When making a recommendation, the recommendation unit can analyze the user's social media activity and suggest a recommendation method. For example, the recommendation unit recommends related evacuation routes based on evacuation information shared by the user on social media. The recommendation unit also makes recommendations based on information from disaster information accounts that the user follows on social media. Furthermore, the recommendation unit can recommend the optimal evacuation route based on location information posted by the user on social media. This enables the recommendation unit to make more appropriate recommendations by analyzing the user's social media activity.

[0051] The input method unit can suggest the most appropriate input method by referring to the user's past input history when inputting information. For example, the input method unit automatically displays as candidates evacuation routes that the user has frequently input in the past. The input method unit also preferentially suggests input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input method unit can predict and suggest evacuation routes to be used during specific time periods based on the user's past input history. This allows the input method unit to suggest more appropriate input methods by referring to the past input history.

[0052] The input method unit can select the optimal input method by taking into account the user's device information when inputting. For example, if the user is using a smartphone, the input method unit provides an input method that matches the screen size. Also, if the user is using a tablet, the input method unit provides an input method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the input method unit can provide an input method that is simple and highly visible. In this way, the input method unit can provide a more appropriate input method by taking into account the user's device information.

[0053] When selecting an information source, the information source unit can refer to past information source data to select the most appropriate information source. The information source unit, for example, selects the most reliable information source from past information source data. Also, the information source unit can analyze past information source data to select the most appropriate information source. Furthermore, the information source unit can evaluate the accuracy of past information source data and select the most accurate information source. In this way, the information source unit can select a more reliable information source by referring to past information source data.

[0054] When selecting information sources, the information source unit can adjust the range of information sources based on the location of the disaster. For example, the information source unit preferentially selects information sources close to the location of the disaster. Also, the information source unit reduces the frequency of selection of information sources far from the location of the disaster. Furthermore, the information source unit can adjust the type of information source based on the location of the disaster. In this way, the information source unit can provide more relevant information by adjusting the range of information sources based on the location of the disaster.

[0055] When selecting an analysis method, the analysis method unit can refer to past analysis data to select the most appropriate analysis method. The analysis method unit, for example, selects the most reliable analysis method from past analysis data. The analysis method unit also analyzes past analysis data to select the most appropriate analysis method. Furthermore, it can evaluate the accuracy of past analysis data and select the most accurate analysis method. In this way, the analysis method unit can select a more reliable analysis method by referring to past analysis data.

[0056] When selecting an analysis method, the analysis method unit can adjust the scope of the analysis method based on the location of the disaster. For example, the analysis method unit prioritizes analysis of information close to the location of the disaster. Also, the analysis method unit reduces the frequency of analysis of information far from the location of the disaster. Furthermore, the type of information to be analyzed can also be adjusted based on the location of the disaster. In this way, the analysis method unit can provide more relevant information by adjusting the scope of the analysis method based on the location of the disaster.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The disaster prevention system can further include a health management unit that monitors the user's health condition. The health management unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and monitors stress and changes in physical condition in real time during a disaster. For example, if the user's heart rate suddenly rises, the system can send a notification urging the user to rest. Also, if the user's blood pressure is abnormally high, the system can suggest a reasonable evacuation route. Furthermore, if the user's body temperature is abnormally high, the system can prioritize cooler evacuation shelters, taking into account the risk of heatstroke. This allows the health management unit to provide evacuation support that takes the user's health condition into consideration.

[0059] The disaster prevention system may further include a pet management unit that manages information about the user's pets. The pet management unit registers information about the user's pets, such as the type, number, and health status, and provides support for evacuating with pets in the event of a disaster. For example, it may preferentially recommend shelters that allow pets. It may also provide a list of necessary medicines and food, taking into account the pet's health status. It may also analyze the pet's behavioral patterns and provide advice to reduce stress. In this way, the pet management unit can support the user and their pet in evacuating safely.

[0060] The disaster prevention system can further include a vehicle management unit that manages user vehicle information. The vehicle management unit registers information such as the user's vehicle's location, remaining fuel, and maintenance status, and supports evacuation using the vehicle in the event of a disaster. For example, if fuel is low, the unit can recommend the nearest gas station. It can also propose a reliable evacuation route taking into account the vehicle's maintenance status. Furthermore, the optimal evacuation route can be updated in real time based on the vehicle's location information. This allows the vehicle management unit to support users in evacuating safely using their vehicle.

[0061] The disaster prevention system can further include a community management unit that manages user community information. The community management unit registers information about the local community to which the user belongs and provides evacuation support for the entire community in the event of a disaster. For example, it can share the evacuation status of neighboring residents and provide information for cooperative evacuation. It can also coordinate support activities within the community and quickly provide necessary supplies and support. It can also secure means of communication within the community and smoothly share information in the event of a disaster. In this way, the community management unit can support safe evacuation throughout the entire community.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The registration unit registers the user's action plan and evacuation route. The user's action plan includes daily action plans and emergency action plans, and the evacuation route includes evacuation locations and route safety. For example, the user can register the route from their home to an evacuation shelter and how to contact their family. Step 2: The collection unit collects disaster information, including earthquake information, flood information, and weather information, and can obtain disaster information from, for example, the Japan Meteorological Agency, local governments, social media, and news sites. Step 3: The analysis unit analyzes the disaster information collected by the collection unit and the user's action plan and evacuation route. For example, using generation AI, it can propose a safe evacuation route for the user based on information such as road collapses caused by earthquakes or floods caused by typhoons. Step 4: The recommendation unit recommends the optimal detour route based on the information analyzed by the analysis unit. For example, the optimal detour route can be notified to the user's smartphone.

[0064] (Example 2) A disaster prevention system according to an embodiment of the present invention allows users to evacuate quickly and safely in the event of a disaster. This disaster prevention system registers the user's action plan and evacuation route, collects and analyzes disaster information, and recommends optimal detour routes. First, in preparation for a disaster, the user registers their action plan and evacuation route as their "My Timeline." For example, they record details such as the route from their home to an evacuation shelter and how to contact their family. This information is stored in the system. Next, when a disaster occurs, the system collects disaster information in real time. For example, disaster information on earthquakes, typhoons, etc. is obtained from the Japan Meteorological Agency and local governments. This information is input into a generation AI. The generation AI analyzes the collected disaster information and the user's "My Timeline" and recommends optimal detour routes. For example, it proposes a safe evacuation route based on information such as road collapses caused by earthquakes or floods caused by typhoons. This proposal is notified to the user's smartphone. This allows the user to evacuate quickly and safely in the event of a disaster. For example, if a road collapses on the user's way to an evacuation shelter, the generation AI proposes a detour route, allowing the user to safely reach the shelter. In addition, contact methods with family members are also registered in My Timeline, allowing for quick contact in the event of a disaster. This allows the disaster prevention system to register the user's action plan and evacuation route, collect and analyze disaster information, and recommend optimal detour routes, enabling quick and safe evacuation in the event of a disaster.

[0065] A disaster prevention system according to an embodiment includes a registration unit, a collection unit, an analysis unit, and a recommendation unit. The registration unit registers a user's action plan and evacuation route. The user's action plan includes, for example, a daily action plan and an emergency action plan. The evacuation route includes, for example, evacuation locations and the safety of the route. The registration unit allows, for example, a user to register a route from their home to an evacuation shelter. The registration unit also allows the user to register a method of contacting family members. The collection unit collects disaster information. The disaster information includes, for example, earthquake information, flood information, and weather information. The collection unit can acquire disaster information from, for example, the Japan Meteorological Agency or local governments. The collection unit can also collect disaster information from social networking sites and news sites. The analysis unit analyzes the disaster information collected by the collection unit and the user's action plan and evacuation route. The analysis unit analyzes the collected disaster information and the user's action plan and evacuation route, for example, using a generation AI. The generation AI suggests a safe evacuation route for the user based on, for example, information about road collapses due to earthquakes and floods due to typhoons. The recommendation unit recommends an optimal detour route based on the information analyzed by the analysis unit. The recommendation unit can, for example, notify the user's smartphone of the optimal detour route. As a result, the disaster prevention system according to the embodiment can register the user's action plan and evacuation route, collect and analyze disaster information, and recommend the optimal detour route, thereby enabling the user to evacuate quickly and safely in the event of a disaster.

[0066] The input method unit can diversify the input methods for the user. The input method unit provides multiple input methods, such as voice input, touch input, and keyboard input. For example, by using voice input, the user can register an action plan or an evacuation route without using their hands. Furthermore, by using touch input, the user can input information intuitively using a smartphone or tablet. Furthermore, by using keyboard input, the user can input detailed information accurately. In this way, the input method unit diversifies the input methods for the user, making it easier for more users to use.

[0067] The information source unit can diversify the sources from which disaster information is collected. The information source unit collects disaster information from multiple sources, such as the Japan Meteorological Agency, local governments, social media, and news sites. For example, information from the Japan Meteorological Agency is highly reliable as it is official data. Information from local governments provides detailed information specific to the region. Information from social media allows for real-time information collection, allowing for a quick understanding of the local situation. Information from news sites allows for the provision of information from a broader perspective. This allows the information source unit to collect more accurate and diverse information by diversifying the sources from which disaster information is collected.

[0068] The analysis method unit can indicate an analysis method for the generating AI. The analysis method unit indicates a specific analysis method, such as a machine learning algorithm or a statistical analysis method. For example, by using a machine learning algorithm, it is possible to analyze collected disaster information and a user's action plan and evacuation route, and propose an optimal detour route. Furthermore, by using a statistical analysis method, it is possible to analyze trends and patterns in disaster information and provide appropriate information to the user. In this way, the analysis method unit can improve the accuracy of the analysis by specifically indicating an analysis method for the generating AI.

[0069] The collection unit can collect disaster information from the Japan Meteorological Agency or local governments, social media, and news sites. For example, the collection unit can obtain real-time weather data from the Japan Meteorological Agency using an API. From local governments, the collection unit can collect local disaster information through official websites and emergency notification systems. From social media, the collection unit can collect posts from specific hashtags and reliable users to understand the local situation. From news sites, the collection unit can obtain the latest disaster information from reliable news sources. This allows the collection unit to obtain more accurate and diverse information by collecting disaster information from a variety of sources.

[0070] The recommendation unit can notify the user's smartphone of the optimal detour route. The recommendation unit notifies the user's smartphone of the optimal detour route by methods such as push notification, SMS, and in-app notification. For example, by using push notification, the user can receive detour route information in real time. By using SMS, the user can receive information reliably even if the internet connection is unstable. By using in-app notification, the user can check the latest detour route information when they open the app. As a result, the recommendation unit can notify the user's smartphone of the optimal detour route, allowing for quick and safe evacuation.

[0071] The registration unit can estimate the user's emotions and adjust the registration method for the action plan and evacuation route based on the estimated user emotions. For example, if the user is feeling anxious, the registration unit provides a simple and intuitive interface to simplify the registration procedure. Furthermore, if the user is relaxed, the registration unit provides detailed input options and suggests a customizable registration method. Furthermore, if the user is in a hurry, the registration unit prioritizes voice input to enable the action plan and evacuation route to be registered quickly. This allows the registration unit to adjust the registration method according to the user's emotions, enabling more appropriate registration. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0072] The registration unit can analyze the user's past evacuation history and suggest the optimal registration method. For example, the registration unit automatically displays evacuation routes that the user has frequently used in the past as candidates. The registration unit also preferentially suggests registration methods (voice, text, etc.) that the user has used in the past. Furthermore, the registration unit can predict and suggest evacuation routes to be used at specific times based on the user's past evacuation history. This allows the registration unit to suggest more appropriate registration methods by analyzing the user's past evacuation history.

[0073] At the time of registration, the registration unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user is elderly, the registration unit can prioritize displaying barrier-free information on evacuation routes. Also, if the user has a pet, the registration unit can prioritize suggesting pet-friendly evacuation shelters. Furthermore, if the user lives in a specific area, the registration unit can also suggest evacuation routes based on disaster risk information for that area. In this way, the registration unit can provide more appropriate information by filtering based on the user's living situation and areas of interest.

[0074] The registration unit can estimate the user's emotions and determine the priority of information to be registered based on the estimated user emotions. For example, if the user is nervous, the registration unit prioritizes registering the most important information. Also, if the user is relaxed, the registration unit suggests registering detailed information. Furthermore, if the user is in a hurry, the registration unit prioritizes registering only the minimum necessary information. In this way, the registration unit can prioritize registering more important information by determining the priority of information according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0075] During registration, the registration unit can prioritize registering highly relevant information by taking into account the user's geographical location information. For example, the registration unit prioritizes registering information about evacuation shelters closest to the user's current location. Also, if the user is in a specific area, the registration unit prioritizes registering disaster risk information for that area. Furthermore, if the user is traveling, the registration unit can also suggest the optimal evacuation route based on the user's current location. This allows the registration unit to provide more relevant information by taking into account the user's geographical location information.

[0076] At the time of registration, the registration unit can analyze the user's social media activity and register related information. For example, the registration unit can suggest relevant evacuation routes based on evacuation information shared by the user on social media. The registration unit also preferentially registers information from disaster information accounts that the user follows on social media. Furthermore, the registration unit can also suggest optimal evacuation routes based on location information posted by the user on social media. This allows the registration unit to provide more relevant information by analyzing the user's social media activity.

[0077] The collection unit can estimate the user's emotions and adjust the timing of collecting disaster information based on the estimated user emotions. For example, if the user feels anxious, the collection unit frequently collects disaster information and provides the latest information. If the user feels relaxed, the collection unit provides disaster information at a normal collection frequency. Furthermore, if the user is in a hurry, the collection unit prioritizes collecting only important disaster information. In this way, the collection unit can provide information at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis.

[0078] The collection unit can analyze past disaster information and select the optimal collection method. For example, the collection unit selects the most reliable information source from the past disaster information. The collection unit also analyzes collection patterns of past disaster information and determines the optimal collection timing. Furthermore, the collection unit can evaluate the accuracy of past disaster information and select the most accurate information source. This allows the collection unit to select a more appropriate collection method by analyzing past disaster information.

[0079] The collection unit can apply different collection algorithms depending on the type and scale of the disaster when collecting data. For example, in the case of an earthquake, the collection unit prioritizes collecting seismometer data. In the case of a typhoon, the collection unit prioritizes collecting meteorological satellite data. Furthermore, in the case of a flood, the collection unit can prioritize collecting river water level data. This allows the collection unit to collect more accurate information by applying a collection algorithm depending on the type and scale of the disaster.

[0080] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting the most important disaster information. If the user is relaxed, the collection unit provides disaster information at a normal collection frequency. If the user is in a hurry, the collection unit prioritizes collecting only important disaster information. In this way, the collection unit can prioritize collecting more important information by determining the priority of information according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0081] The collection unit can adjust the range of collection based on the location of the disaster when collecting information. For example, the collection unit prioritizes collecting information close to the location of the disaster. Also, the collection unit reduces the frequency of collecting information far from the location of the disaster. Furthermore, the type of information to be collected can also be adjusted based on the location of the disaster. In this way, the collection unit can provide more relevant information by adjusting the collection range based on the location of the disaster.

[0082] The collection unit can improve the accuracy of collection by referring to disaster-related literature during collection. For example, the collection unit evaluates the reliability of the information to be collected based on disaster-related literature. The collection unit also determines the type of information to be collected by referring to disaster-related literature. Furthermore, the collection unit can also determine the priority of the information to be collected based on disaster-related literature. In this way, the collection unit improves the accuracy of collection by referring to disaster-related literature.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit performs a detailed analysis and provides highly reliable information. If the user is relaxed, the analysis unit provides information using normal analysis criteria. Furthermore, if the user is in a hurry, the analysis unit prioritizes analysis of only important information. This allows the analysis unit to adjust the analysis criteria according to the user's emotions, enabling more appropriate analysis. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between disaster information. For example, the analysis unit performs analysis by taking into account the relationship between earthquakes and tsunamis. Also, the analysis unit performs analysis by taking into account the relationship between typhoons and floods. Furthermore, the analysis unit can also perform analysis by taking into account the relationship between volcanic eruptions and earthquakes. In this way, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships between disaster information.

[0085] The analysis unit can perform the analysis while taking into account the user's attribute information. For example, if the user is elderly, the analysis unit performs the analysis while placing emphasis on the safety of evacuation routes. Also, if the user has pets, the analysis takes into account shelters that allow pets. Furthermore, if the user lives in a specific area, the analysis can also be performed based on disaster risk information for that area. This allows the analysis unit to perform more appropriate analysis by taking into account the user's attribute information.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit provides a display method including detailed information. If the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows the analysis unit to provide more appropriate information by adjusting the display method according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis.

[0087] The analysis unit can take into account the geographical distribution of disasters during analysis. For example, the analysis unit performs analysis taking into account the epicenter of an earthquake and the extent of damage. The analysis unit also performs analysis taking into account the path of a typhoon and the extent of its impact. Furthermore, the analysis unit can also perform analysis taking into account the location of flood occurrence and the extent of inundation. This allows the analysis unit to perform more accurate analysis by taking into account the geographical distribution of disasters.

[0088] The analysis unit can improve the accuracy of the analysis by referring to disaster-related literature during the analysis. For example, the analysis unit evaluates the reliability of the analysis based on disaster-related literature. The analysis unit also determines analysis criteria by referring to disaster-related literature. Furthermore, the analysis unit can also determine the priority of the analysis based on disaster-related literature. In this way, the analysis unit improves the accuracy of the analysis by referring to disaster-related literature.

[0089] The recommendation unit can estimate the user's emotions and adjust the recommendation method based on the estimated user emotions. For example, if the user is feeling anxious, the recommendation unit provides a simple and intuitive recommendation method. If the user is relaxed, the recommendation unit provides a detailed recommendation method. Furthermore, if the user is in a hurry, the recommendation unit makes a quick recommendation. This allows the recommendation unit to adjust the recommendation method according to the user's emotions, thereby enabling more appropriate recommendations. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0090] When making a recommendation, the recommendation unit can analyze the user's past evacuation behavior and select the optimal recommendation method. The recommendation unit selects the optimal recommendation method based on, for example, evacuation routes used by the user in the past. Also, the recommendation unit selects a recommendation method that avoids congestion based on the user's past evacuation behavior. Furthermore, the recommendation unit can analyze the user's past evacuation behavior and select the most efficient recommendation method. This allows the recommendation unit to select a more appropriate recommendation method by analyzing past evacuation behavior.

[0091] When making a recommendation, the recommendation unit can customize the recommendation method based on the user's current living situation. For example, if the user is elderly, the recommendation unit makes a recommendation that includes information on barrier-free evacuation routes. Also, if the user has a pet, the recommendation unit can recommend evacuation shelters that allow pets. Furthermore, if the user lives in a specific area, the recommendation unit can also make recommendations based on disaster risk information for that area. This allows the recommendation unit to customize the recommendation method based on the user's living situation, enabling more appropriate recommendations.

[0092] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on the estimated user emotions. For example, if the user is feeling anxious, the recommendation unit prioritizes recommending the most important evacuation route. If the user is relaxed, the recommendation unit recommends a detailed evacuation route. If the user is in a hurry, the recommendation unit prioritizes recommending a route that allows for quick evacuation. In this way, the recommendation unit can prioritize providing more important information by determining the priority of recommendations according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0093] When making a recommendation, the recommendation unit can select the optimal recommendation method by taking into account the user's geographical location information. For example, the recommendation unit prioritizes recommending evacuation shelters that are closest to the user's current location. Also, if the user is in a specific area, the recommendation unit makes recommendations based on disaster risk information for that area. Furthermore, if the user is traveling, the recommendation unit can also recommend the optimal evacuation route based on the user's current location. This allows the recommendation unit to make more appropriate recommendations by taking into account the user's geographical location information.

[0094] When making a recommendation, the recommendation unit can analyze the user's social media activity and suggest a recommendation method. For example, the recommendation unit recommends related evacuation routes based on evacuation information shared by the user on social media. The recommendation unit also makes recommendations based on information from disaster information accounts that the user follows on social media. Furthermore, the recommendation unit can recommend the optimal evacuation route based on location information posted by the user on social media. This enables the recommendation unit to make more appropriate recommendations by analyzing the user's social media activity.

[0095] The input method unit can estimate the user's emotion and adjust the input method based on the estimated user's emotion. For example, if the user is feeling anxious, the input method unit provides a simple and intuitive input method. If the user is relaxed, the input method unit provides detailed input options. Furthermore, if the user is in a hurry, the input method unit prioritizes voice input to enable quick input. In this way, the input method unit adjusts the input method according to the user's emotion, enabling more appropriate input. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0096] The input method unit can suggest the most appropriate input method by referring to the user's past input history when inputting information. For example, the input method unit automatically displays as candidates evacuation routes that the user has frequently input in the past. The input method unit also preferentially suggests input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input method unit can predict and suggest evacuation routes to be used during specific time periods based on the user's past input history. This allows the input method unit to suggest more appropriate input methods by referring to the past input history.

[0097] The input method unit can estimate the user's emotions and determine the priority of inputs based on the estimated user's emotions. For example, if the user feels anxious, the input method unit prioritizes input of the most important information. Also, if the user feels relaxed, the input method unit suggests inputs including detailed information. Furthermore, if the user is in a hurry, the input method unit prioritizes input of only the minimum necessary information. In this way, the input method unit can prioritize input of more important information by determining the priority of inputs according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0098] The input method unit can select the optimal input method by taking into account the user's device information when inputting. For example, if the user is using a smartphone, the input method unit provides an input method that matches the screen size. Also, if the user is using a tablet, the input method unit provides an input method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the input method unit can provide an input method that is simple and highly visible. In this way, the input method unit can provide a more appropriate input method by taking into account the user's device information.

[0099] The information source unit can estimate the user's emotions and select an information source based on the estimated user's emotions. For example, if the user is feeling anxious, the information source unit preferentially selects a highly reliable information source. If the user is relaxed, the information source unit selects a normal information source. If the user is in a hurry, the information source unit selects an information source that can provide information quickly. In this way, the information source unit can provide more reliable information by selecting an information source according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0100] When selecting an information source, the information source unit can refer to past information source data to select the most appropriate information source. The information source unit, for example, selects the most reliable information source from past information source data. Also, the information source unit can analyze past information source data to select the most appropriate information source. Furthermore, the information source unit can evaluate the accuracy of past information source data and select the most accurate information source. In this way, the information source unit can select a more reliable information source by referring to past information source data.

[0101] The information source unit can estimate the user's emotions and determine the priority of information sources based on the estimated user emotions. For example, if the user is feeling anxious, the information source unit preferentially selects the most reliable information source. If the user is relaxed, the information source unit preferentially selects a normal information source. If the user is in a hurry, the information source unit preferentially selects an information source that can provide information quickly. In this way, the information source unit can prioritize information sources according to the user's emotions and provide more important information preferentially. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0102] When selecting information sources, the information source unit can adjust the range of information sources based on the location of the disaster. For example, the information source unit preferentially selects information sources close to the location of the disaster. Also, the information source unit reduces the frequency of selection of information sources far from the location of the disaster. Furthermore, the information source unit can adjust the type of information source based on the location of the disaster. In this way, the information source unit can provide more relevant information by adjusting the range of information sources based on the location of the disaster.

[0103] The analysis method unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is feeling anxious, the analysis method unit performs a detailed analysis and provides highly reliable information. If the user is relaxed, the analysis method unit provides information using a normal analysis method. Furthermore, if the user is in a hurry, the analysis method unit prioritizes analysis of only important information. This allows the analysis method unit to adjust the analysis method according to the user's emotions, enabling more appropriate analysis. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0104] When selecting an analysis method, the analysis method unit can refer to past analysis data to select the most appropriate analysis method. The analysis method unit, for example, selects the most reliable analysis method from past analysis data. The analysis method unit also analyzes past analysis data to select the most appropriate analysis method. Furthermore, it can evaluate the accuracy of past analysis data and select the most accurate analysis method. In this way, the analysis method unit can select a more reliable analysis method by referring to past analysis data.

[0105] The analysis method unit can estimate the user's emotions and determine the priority of analysis methods based on the estimated user emotions. For example, if the user is feeling anxious, the analysis method unit preferentially selects the most reliable analysis method. If the user is relaxed, the analysis method unit selects a normal analysis method. If the user is in a hurry, the analysis method unit preferentially selects a method that can perform analysis quickly. In this way, the analysis method unit can prioritize analysis methods according to the user's emotions, thereby prioritizing more important information. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0106] When selecting an analysis method, the analysis method unit can adjust the scope of the analysis method based on the location of the disaster. For example, the analysis method unit prioritizes analysis of information close to the location of the disaster. Also, the analysis method unit reduces the frequency of analysis of information far from the location of the disaster. Furthermore, the type of information to be analyzed can also be adjusted based on the location of the disaster. In this way, the analysis method unit can provide more relevant information by adjusting the scope of the analysis method based on the location of the disaster. === Hard Collateral 1-1 === Each of the multiple elements including the registration unit, collection unit, analysis unit, and recommendation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14 and registers the user's action plan and evacuation route. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects disaster information from the Japan Meteorological Agency and local governments. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected disaster information and the user's action plan and evacuation route. The recommendation unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the user's smartphone of the optimal detour route. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned registration unit, collection unit, analysis unit, and recommendation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 and registers the user's action plan and evacuation route. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects disaster information from the Japan Meteorological Agency and local governments. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected disaster information and the user's action plan and evacuation route. The recommendation unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the user's smartphone of the optimal detour route. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, collection unit, analysis unit, and recommendation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314 and registers the user's action plan and evacuation route. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects disaster information from the Japan Meteorological Agency and local governments. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected disaster information and the user's action plan and evacuation route. The recommendation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and notifies the user's smartphone of the optimal detour route. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, collection unit, analysis unit, and recommendation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414 and registers the user's action plan and evacuation route. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects disaster information from the Japan Meteorological Agency and local governments. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected disaster information and the user's action plan and evacuation route. The recommendation unit is realized, for example, by the control unit 46A of the robot 414 and notifies the user's smartphone of the optimal detour route.

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

[0108] The disaster prevention system can further include a health management unit that monitors the user's health condition. The health management unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and monitors stress and changes in physical condition in real time during a disaster. For example, if the user's heart rate suddenly rises, the system can send a notification urging the user to rest. Also, if the user's blood pressure is abnormally high, the system can suggest a reasonable evacuation route. Furthermore, if the user's body temperature is abnormally high, the system can prioritize cooler evacuation shelters, taking into account the risk of heatstroke. This allows the health management unit to provide evacuation support that takes the user's health condition into consideration.

[0109] The disaster prevention system may further include a pet management unit that manages information about the user's pets. The pet management unit registers information about the user's pets, such as the type, number, and health status, and provides support for evacuating with pets in the event of a disaster. For example, it may preferentially recommend shelters that allow pets. It may also provide a list of necessary medicines and food, taking into account the pet's health status. It may also analyze the pet's behavioral patterns and provide advice to reduce stress. In this way, the pet management unit can support the user and their pet in evacuating safely.

[0110] The disaster prevention system can further include a vehicle management unit that manages user vehicle information. The vehicle management unit registers information such as the user's vehicle's location, remaining fuel, and maintenance status, and supports evacuation using the vehicle in the event of a disaster. For example, if fuel is low, the unit can recommend the nearest gas station. It can also propose a reliable evacuation route taking into account the vehicle's maintenance status. Furthermore, the optimal evacuation route can be updated in real time based on the vehicle's location information. This allows the vehicle management unit to support users in evacuating safely using their vehicle.

[0111] The disaster prevention system can further include a community management unit that manages user community information. The community management unit registers information about the local community to which the user belongs and provides evacuation support for the entire community in the event of a disaster. For example, it can share the evacuation status of neighboring residents and provide information for cooperative evacuation. It can also coordinate support activities within the community and quickly provide necessary supplies and support. It can also secure means of communication within the community and smoothly share information in the event of a disaster. In this way, the community management unit can support safe evacuation throughout the entire community.

[0112] The disaster prevention system can further include a psychology management unit that monitors the user's psychological state. The psychology management unit monitors the user's stress level and anxiety in real time and suggests appropriate measures. For example, if the user is feeling high stress, it can suggest breathing techniques or simple exercises to help them relax. If the user is feeling anxious, it can also provide messages or music to give them a sense of security. Furthermore, it can adjust the selection of evacuation routes and the method of providing information according to the user's psychological state. In this way, the psychology management unit can reduce the user's psychological burden and support a safe evacuation.

[0113] The disaster prevention system may further include an emotion analysis unit that estimates the user's emotions and selects an evacuation route based on the estimated emotions. The emotion analysis unit estimates the user's emotions from their facial expressions and voice and reflects this in the selection of an evacuation route. For example, if the user is feeling anxious, it may preferentially suggest an evacuation route that is safer and more reassuring. Also, if the user is relaxed, it may suggest the shortest evacuation route. Furthermore, if the user is in a hurry, it may preferentially suggest a route that allows for quick evacuation. In this way, the emotion analysis unit can provide the optimal evacuation route according to the user's emotions.

[0114] The disaster prevention system can further include an information providing unit that estimates the user's emotions and adjusts the method of providing evacuation information based on the estimated emotions. The information providing unit adjusts the method of displaying and notifying information according to the user's emotions. For example, if the user is feeling anxious, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a concise display method that focuses on the main points can be provided. This enables the information providing unit to provide appropriate information according to the user's emotions.

[0115] The disaster prevention system may further include an assistance priority unit that estimates the user's emotions and determines the priority of evacuation assistance based on the estimated emotions. The assistance priority unit adjusts the priority of evacuation assistance according to the user's emotions. For example, if the user feels anxious, the most important assistance can be provided with priority. Also, if the user feels relaxed, detailed assistance can be provided. Furthermore, if the user is in a hurry, assistance can be provided quickly. In this way, the assistance priority unit can provide appropriate evacuation assistance according to the user's emotions.

[0116] The disaster prevention system may further include a plan review unit that estimates the user's emotions and reviews the evacuation plan based on the estimated emotions. The plan review unit reviews the evacuation plan according to the user's emotions. For example, if the user feels anxious, the plan review unit can review the evacuation plan to make it simple and intuitive. If the user feels relaxed, the plan review unit can also propose a detailed evacuation plan. Furthermore, if the user is in a hurry, the plan review unit can also propose an evacuation plan that can be quickly implemented. This allows the plan review unit to review the evacuation plan appropriately according to the user's emotions.

[0117] The disaster prevention system may further include a training adjustment unit that estimates the user's emotions and adjusts the content of the evacuation drill based on the estimated emotions. The training adjustment unit adjusts the content of the evacuation drill according to the user's emotions. For example, if the user is feeling anxious, a simple and basic evacuation drill can be provided. Alternatively, if the user is relaxed, a detailed and practical evacuation drill can be provided. Furthermore, if the user is in a hurry, a short and effective evacuation drill can be provided. In this way, the training adjustment unit can provide an appropriate evacuation drill according to the user's emotions.

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

[0119] Step 1: The registration unit registers the user's action plan and evacuation route. The user's action plan includes daily action plans and emergency action plans, and the evacuation route includes evacuation locations and route safety. For example, the user can register the route from their home to an evacuation shelter and how to contact their family. Step 2: The collection unit collects disaster information, including earthquake information, flood information, and weather information, and can obtain disaster information from, for example, the Japan Meteorological Agency, local governments, social media, and news sites. Step 3: The analysis unit analyzes the disaster information collected by the collection unit and the user's action plan and evacuation route. For example, using generation AI, it can propose a safe evacuation route for the user based on information such as road collapses caused by earthquakes or floods caused by typhoons. Step 4: The recommendation unit recommends the optimal detour route based on the information analyzed by the analysis unit. For example, the optimal detour route can be notified to the user's smartphone.

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

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

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

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

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

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

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

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

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

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

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

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

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

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

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

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

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

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

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

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

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

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

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

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

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a registration unit for registering a user's action plan or evacuation route; a collection department that collects disaster information; an analysis unit that analyzes the disaster information collected by the collection unit and the user's action plan or evacuation route; a recommendation unit that recommends a detour route based on the information analyzed by the analysis unit. A system characterized by:

2. Equipped with an input method unit that allows multiple user input methods 2. The system of claim 1.

3. Equipping the information source department with multiple sources of disaster information 2. The system of claim 1.

4. Equipped with an analysis method section that shows the analysis method of the generated AI 2. The system of claim 1.

5. The collecting unit Collect disaster information from the Japan Meteorological Agency, local governments, social media, and news sites.

2. The system of claim 1.

6. The recommendation unit Notify the user of the best detour route on their smartphone 2. The system of claim 1.

7. The registration unit Estimate the user's emotions and adjust the method of registering action plans and evacuation routes based on the estimated user emotions.

2. The system of claim 1.

8. The registration unit Analyzes the user's past evacuation history and suggests the optimal registration method 2. The system of claim 1.

9. The registration unit Filtering based on the user's current life situation and interests upon registration 2. The system of claim 1.

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