system

Abstract

The system according to the embodiment aims to guide users to safe evacuation routes and grasp their location information in the event of a disaster. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a guidance unit, a situation assessment unit, and an information provision unit. The collection unit collects disaster information. The analysis unit analyzes the information collected by the collection unit. The guidance unit guides the user along an evacuation route based on the analysis results obtained by the analysis unit. The situation assessment unit grasps the location information of the user guided by the guidance unit. The information provision unit provides the information grasped by the situation assessment unit to a rescue team.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately guide users to safe evacuation routes or grasp their location information in the event of a disaster, and there is room for improvement.

[0005] The system according to the embodiment aims to guide users to safe evacuation routes and grasp their location information in the event of a disaster. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a guidance unit, a situation assessment unit, and an information provision unit. The collection unit collects disaster information. The analysis unit analyzes the information collected by the collection unit. The guidance unit guides the user along an evacuation route based on the analysis results obtained by the analysis unit. The situation assessment unit grasps the location information of the user guided by the guidance unit. The information provision unit provides the information grasped by the situation assessment unit to a rescue team. [Effects of the Invention]

[0007] The system according to the embodiment can guide users to safe evacuation routes and grasp their location information in the event of a disaster. [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 rescue system according to an embodiment of the present invention is a mechanism for ensuring user safety during disasters and is provided as a function of a general messaging app. This disaster rescue system is provided free of charge, contributing to the establishment of account services and the improvement of brand image. When a disaster occurs, the system guides users to safe actions using the following flow: First, when a disaster occurs, AI collects and analyzes disaster information. Next, based on the analysis results, it guides users to appropriate evacuation routes and actions. For example, when an earthquake occurs, AI analyzes the epicenter and damage status and presents the optimal evacuation route to the user. It also provides information necessary for users during evacuation (e.g., the location of evacuation centers and the distribution status of relief supplies). Furthermore, the disaster rescue system utilizes the user's location information to grasp the situation in real time and provide information to rescue teams as needed. This enables rapid rescue operations and contributes to saving lives. This mechanism prevents disruptions to social infrastructure during disasters and ensures user safety. Furthermore, providing the system free of charge contributes to the establishment of account services and the improvement of brand image. This allows the disaster rescue system to ensure the safety of users in the event of a disaster and support rapid rescue operations.

[0029] A disaster rescue system according to an embodiment includes a collection unit, an analysis unit, a guidance unit, a situation assessment unit, and an information provision unit. The collection unit collects disaster information. Examples of disaster information include, but are not limited to, earthquake information, flood information, and fire information. The collection unit collects disaster information using, for example, a sensor. The collection unit can also collect disaster information by receiving reports from users. The collection unit can also collect publicly available information on the Internet. For example, the collection unit collects disaster information in real time using a seismometer or a weather sensor. Users can easily report the information through an app, and the collection unit processes the reports immediately. Publicly available information on the Internet is automatically collected, for example, from government agencies and news sites. The analysis unit analyzes the disaster information collected by the collection unit. The analysis can be performed using, for example, data mining or statistical analysis, but is not limited to, examples. For example, the analysis unit analyzes the collected earthquake information to identify the epicenter and seismic intensity. The analysis unit can also analyze flood information to predict rising river water levels. Furthermore, the analysis unit can analyze fire information and predict the extent of the fire's expansion. For example, the analysis unit identifies the epicenter based on earthquake information and predicts the extent of damage. Flood information is analyzed by analyzing river water level data and assessing the risk of flooding. Fire information is used to predict the extent of the fire's expansion taking wind direction and wind speed into account. The guidance unit guides the user to an evacuation route based on the analysis results obtained by the analysis unit. Guidance can be provided by, for example, voice guidance or map display, but is not limited to these examples. For example, the guidance unit guides the user to the optimal evacuation route by voice. The guidance unit can also display the evacuation route on a map. Furthermore, the guidance unit can guide the user to the nearest evacuation shelter from the user's current location. For example, the guidance unit instructs the user on the evacuation route using voice guidance. The map display visually shows the evacuation route on the smartphone screen. The nearest evacuation shelter is automatically searched for based on the user's current location. The situation understanding unit understands the location information of the user guided by the guidance unit. The location information is obtained by, for example, but not limited to, a method such as GPS or Wi-Fi location information. For example, the situation grasping unit obtains GPS information from the user's smartphone.The situation assessment unit can also identify the user's location using Wi-Fi location information. Furthermore, the situation assessment unit can record the user's movement history and assess the situation in real time. For example, the situation assessment unit tracks the user's current location in real time using GPS. Wi-Fi location information is effective for locating indoor locations. The movement history is used to record the user's evacuation route and analyze it later. The information providing unit provides the information assessed by the situation assessment unit to a rescue team. Information provision can be performed, for example, by real-time notification or periodic reporting, but is not limited to these examples. For example, the information providing unit notifies the rescue team of the user's location information in real time. The information providing unit can also periodically report the user's status. Furthermore, the information providing unit can immediately provide the rescue team with necessary information. For example, the information providing unit transmits the user's location information to the rescue team in real time. The periodic reporting updates the user's status at regular intervals. The necessary information includes detailed data useful for rescue operations. This allows the disaster rescue system according to the embodiment to ensure the safety of users during disasters and support rapid rescue operations.

[0030] The collection unit can analyze past disaster data and select an information collection method. For example, the collection unit analyzes past earthquake data and selects an information collection method according to the distance from the epicenter. For example, the collection unit can also analyze past typhoon data and select an information collection method based on wind speed and rainfall. For example, the collection unit can analyze past flood data and select an information collection method according to a rise in river water level. This makes it possible to select an optimal information collection method based on past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past disaster data into the generation AI and cause the generation AI to select an optimal information collection method.

[0031] When collecting disaster information, the collection unit can filter the information based on the user's current location information and areas of interest. For example, if the user's current location is close to the epicenter, the collection unit can prioritize collecting earthquake information. For example, if the user is interested in flooding, the collection unit can also prioritize collecting river water level information. For example, if the user's current location is in the path of a typhoon, the collection unit can also prioritize collecting wind speed and rainfall information. This makes it possible to collect appropriate information based on the user's location information and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location information to the generation AI and have the generation AI perform filtering.

[0032] When collecting disaster information, the collection unit can prioritize collection of information based on the user's geographical location information. For example, if the user's current location is close to the epicenter, the collection unit prioritizes collection of earthquake information. For example, if the user's current location is in a flood-prone area, the collection unit can also prioritize collection of river water level information. For example, if the user's current location is in the path of a typhoon, the collection unit can also prioritize collection of wind speed and rainfall information. This makes it possible to collect highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information to a generation AI and have the generation AI execute the information prioritization.

[0033] The collection unit can analyze the user's social media activities and collect information when collecting disaster information. For example, if the user posts about earthquakes on social media, the collection unit can prioritize collecting earthquake information. For example, if the user posts about floods, the collection unit can also prioritize collecting river water level information. For example, if the user posts about typhoons, the collection unit can also prioritize collecting wind speed and rainfall information. This makes it possible to collect related information based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related information.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the disaster information. For example, if the epicenter is nearby, the analysis unit analyzes detailed earthquake information. For example, if there is a high risk of flooding, the analysis unit can also analyze river water level information in detail. For example, if the path of a typhoon is close to the user's current location, the analysis unit can also analyze wind speed and rainfall information in detail. This allows analysis at an appropriate level of detail depending on the importance of the disaster information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input disaster information to a generation AI and have the generation AI adjust the level of detail.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the type of disaster. For example, in the case of an earthquake, the analysis unit applies an analysis algorithm based on the epicenter and seismic intensity. For example, in the case of a flood, the analysis unit can also apply an analysis algorithm based on the water level of a river and the amount of rainfall. For example, in the case of a typhoon, the analysis unit can also apply an analysis algorithm based on the wind speed and path. This makes it possible to apply an appropriate analysis algorithm depending on the type of disaster. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the type of disaster into the generation AI and cause the generation AI to apply the analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time when disaster information was collected. The analysis unit, for example, prioritizes analysis of the latest earthquake information. The analysis unit can also prioritize analysis of the latest flood information. The analysis unit can also prioritize analysis of the latest typhoon information. This allows analysis to be performed with appropriate priority based on the time when disaster information was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when disaster information was collected into the generation AI and have the generation AI determine the priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of disaster information. For example, the analysis unit prioritizes analysis of earthquake information related to the user's current location. For example, the analysis unit can also prioritize analysis of flood information related to the user's area of ​​interest. For example, the analysis unit can also prioritize analysis of typhoon information related to the user's current location. This allows analysis to be performed in an appropriate order based on the relevance of disaster information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of disaster information to the generation AI and have the generation AI execute the analysis order.

[0038] The guidance unit can adjust the level of detail of the guidance based on the importance of the evacuation route when providing guidance. For example, if the epicenter is nearby, the guidance unit provides guidance on a detailed evacuation route. For example, if there is a high risk of flooding, the guidance unit can also provide guidance on a detailed evacuation route. For example, if the path of a typhoon is close to the user's current location, the guidance unit can also provide guidance on a detailed evacuation route. This makes it possible to provide guidance with an appropriate level of detail depending on the importance of the evacuation route. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can input the importance of the evacuation route to the generation AI and cause the generation AI to adjust the level of detail.

[0039] The guidance unit can apply different guidance algorithms depending on the type of disaster when providing guidance. For example, in the case of an earthquake, the guidance unit applies a guidance algorithm based on the epicenter and seismic intensity. For example, in the case of a flood, the guidance unit can also apply a guidance algorithm based on the water level of a river and the amount of rainfall. For example, in the case of a typhoon, the guidance unit can also apply a guidance algorithm based on the wind speed and path. This makes it possible to apply an appropriate guidance algorithm depending on the type of disaster. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can input the type of disaster into the generation AI and cause the generation AI to apply the guidance algorithm.

[0040] When providing guidance, the guidance unit can determine the priority of guidance based on the time of submission of the evacuation route. The guidance unit, for example, prioritizes guidance of the latest evacuation route information. The guidance unit can also prioritize guidance of the latest flood evacuation route information. The guidance unit can also prioritize guidance of the latest typhoon evacuation route information. This makes it possible to provide guidance with appropriate priority based on the time of submission of the evacuation route. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can input the time of submission of the evacuation route to the generation AI and have the generation AI determine the priority.

[0041] The guidance unit can adjust the order of guidance based on the relevance of evacuation routes when providing guidance. For example, the guidance unit prioritizes guidance of evacuation route information related to the user's current location. For example, the guidance unit can also prioritize guidance of evacuation route information related to the user's area of ​​interest. For example, the guidance unit can also prioritize guidance of typhoon evacuation route information related to the user's current location. This makes it possible to provide guidance in an appropriate order based on the relevance of evacuation routes. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the relevance of evacuation routes to a generation AI and have the generation AI execute the order of guidance.

[0042] When assessing a situation, the situation assessment unit can select an optimal situation assessment method by analyzing the user's past behavioral history. The situation assessment unit selects the optimal situation assessment method, for example, based on the user's past evacuation routes. The situation assessment unit can also select a situation assessment method that avoids congestion, for example, based on the user's past behavioral history. The situation assessment unit can also analyze the user's past behavioral history and select the most efficient situation assessment method. This makes it possible to select an optimal situation assessment method based on the user's past behavioral history. Some or all of the above-described processing in the situation assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the situation assessment unit can input the user's past behavioral history data into the generation AI and cause the generation AI to select an optimal situation assessment method.

[0043] When grasping the situation, the situation grasping unit can customize the means of grasping the situation based on the user's current living situation. For example, when the user is at home, the situation grasping unit prioritizes grasping disaster information around the home. For example, when the user is at work, the situation grasping unit can also prioritize grasping disaster information around the workplace. For example, when the user is out, the situation grasping unit can also prioritize grasping disaster information around the current location. This makes it possible to grasp the situation using an appropriate means depending on the user's current living situation. Some or all of the above-mentioned processing in the situation grasping unit may be performed using AI, for example, or may be performed without using AI. For example, the situation grasping unit can input the user's living situation data into the generation AI and cause the generation AI to customize the means of grasping the situation.

[0044] When assessing a situation, the situation assessment unit can select an optimal situation assessment method by taking into account the user's geographical location information. For example, if the user's current location is close to the epicenter, the situation assessment unit prioritizes assessing earthquake information. For example, if the user's current location is in a flood-prone area, the situation assessment unit can also prioritize assessing river water level information. For example, if the user's current location is in the path of a typhoon, the situation assessment unit can also prioritize assessing wind speed and rainfall information. This allows the optimal situation assessment method to be selected based on the user's geographical location information. Some or all of the above-described processing in the situation assessment unit may be performed using, or without, AI, for example. For example, the situation assessment unit can input the user's geographical location information to the generation AI and cause the generation AI to select an optimal situation assessment method.

[0045] When assessing a situation, the situation assessment unit can analyze the user's social media activity and suggest means for assessing the situation. For example, if the user posts about an earthquake on social media, the situation assessment unit can prioritize assessing earthquake information. For example, if the user posts about a flood, the situation assessment unit can also prioritize assessing river water level information. For example, if the user posts about a typhoon, the situation assessment unit can also prioritize assessing wind speed and rainfall information. This makes it possible to assess the situation using appropriate means based on the user's social media activity. Some or all of the above-described processing in the situation assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the situation assessment unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest means for assessing the situation.

[0046] When providing information, the information providing unit can select the optimal information providing method by analyzing the user's past behavioral history. The information providing unit selects the optimal information providing method, for example, based on the user's past evacuation route. The information providing unit can also select an information providing method that avoids congestion, for example, based on the user's past behavioral history. The information providing unit can also analyze the user's past behavioral history and select the most efficient information providing method. This makes it possible to select the optimal information providing method based on the user's past behavioral history. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's past behavioral history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0047] The information providing unit can customize the means of providing information based on the user's current living situation when providing information. For example, when the user is at home, the information providing unit can prioritize providing disaster information around the user's home. For example, when the user is at work, the information providing unit can also prioritize providing disaster information around the workplace. For example, when the user is out, the information providing unit can also prioritize providing disaster information around the user's current location. This makes it possible to provide information by an appropriate means depending on the user's current living situation. Some or all of the above-mentioned processing in the information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the information providing unit can input the user's living situation data into the generation AI and cause the generation AI to customize the means of providing information.

[0048] When providing information, the information providing unit can select the optimal information providing method by taking into consideration the user's geographical location information. For example, if the user's current location is close to the epicenter, the information providing unit can prioritize providing earthquake information. For example, if the user's current location is in a flood-prone area, the information providing unit can also prioritize providing river water level information. For example, if the user's current location is in the path of a typhoon, the information providing unit can also prioritize providing wind speed and rainfall information. This makes it possible to select the optimal information providing method based on the user's geographical location information. Some or all of the above-described processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal information providing method.

[0049] When providing information, the information providing unit can analyze the user's social media activity and suggest a means of providing the information. For example, if the user posts about earthquakes on social media, the information providing unit can prioritize providing earthquake information. For example, if the user posts about floods, the information providing unit can also prioritize providing river water level information. For example, if the user posts about typhoons, the information providing unit can also prioritize providing information about wind speed and rainfall. This makes it possible to provide information by an appropriate means based on the user's social media activity. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest a means of providing information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The disaster rescue system can also be equipped with a health management unit that monitors the user's health condition. The health management unit monitors the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time and can immediately issue an alert if an abnormality is detected. For example, if the user's heart rate suddenly rises during evacuation, the health management unit can send that information to the analysis unit and guide the user on appropriate measures. Also, if the user has a chronic illness, registering that information in advance can enable the system to quickly provide necessary medical assistance in the event of a disaster. Furthermore, the health management unit can monitor the user's stress level and suggest relaxation methods as needed.

[0052] The disaster rescue system may further include a pet management unit to ensure the safety of the user's pet. The pet management unit can track the location information of the user's pet and check whether the pet is in a safe place during a disaster. For example, if the pet has not arrived at an evacuation shelter, the pet management unit can identify its location and notify the user. It can also monitor the pet's health and provide necessary medical assistance. Furthermore, the pet management unit can guide the pet's evacuation route and support the user and pet in evacuating together.

[0053] The disaster rescue system may further include a communication unit that helps the user contact family and friends. The communication unit provides a means for the user to contact family and friends during a disaster. For example, even if the communication infrastructure is down, messages can be sent using satellite communication. The communication unit may also share the user's location information with family and friends to quickly confirm their safety. Furthermore, the communication unit may have a function to automatically send a notification when the user arrives at an evacuation shelter. This increases the user's sense of security and helps maintain ties with family and friends.

[0054] The disaster rescue system may further include an asset management unit for protecting the user's assets. The asset management unit can track the location information of the user's important assets and provide guidance for moving to a safe location in the event of a disaster. For example, if the user's car is in a flood-prone area, the asset management unit can send that information to the analysis unit and guide the user to an appropriate evacuation site. Also, if the user's house is affected by an earthquake, the asset management unit can assess the damage and support repair and insurance claim procedures. Furthermore, the asset management unit can manage the storage locations of the user's important documents and valuables and help them be quickly retrieved in the event of a disaster.

[0055] The disaster rescue system can further include a planning support unit that creates an evacuation plan for the user in advance. The planning support unit can plan the optimal evacuation route in advance based on information such as the user's residence, workplace, and school route. For example, if the user lives in an area prone to earthquakes, the planning support unit can provide detailed evacuation route information in the event of an earthquake. Also, if the user lives in an area at risk of flooding, the planning support unit can create an evacuation plan for flooding and list the necessary items to prepare. Furthermore, the planning support unit can integrate the evacuation plans of all members of the user's family and support everyone in evacuating safely.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects disaster information. Disaster information includes earthquake information, flood information, and fire information. The collection unit uses sensors to collect disaster information, as well as user reports and public information on the Internet. For example, the unit collects disaster information in real time using seismometers and weather sensors, receives user reports through an app, and automatically collects public information on the Internet from government agencies and news sites. Step 2: The analysis unit analyzes the disaster information collected by the collection unit. The analysis is performed using methods such as data mining and statistical analysis. For example, earthquake information can be analyzed to identify the epicenter and seismic intensity, flood information can be analyzed to predict rising river water levels, and fire information can be analyzed to predict the extent of the fire. Step 3: The guidance unit guides the user to an evacuation route based on the analysis results obtained by the analysis unit. The guidance is provided by voice guidance, map display, or other methods. For example, the unit may guide the user to the optimal evacuation route by voice, display the evacuation route on a map, and guide the user to the nearest evacuation shelter from the user's current location. Step 4: The situation grasping unit grasps the location information of the user guided by the guidance unit. The location information is acquired by methods such as GPS or Wi-Fi location information. For example, GPS information is acquired from the user's smartphone, and the indoor location is identified using Wi-Fi location information, and the user's movement history is recorded to grasp the situation in real time. Step 5: The information providing unit provides the information grasped by the situation grasping unit to the rescue team. The information is provided by methods such as real-time notification and periodic reports. For example, the information of the user's location is notified to the rescue team in real time, the user's situation is reported periodically, and necessary information is provided immediately.

[0058] (Example 2) A disaster rescue system according to an embodiment of the present invention is a mechanism for ensuring user safety during disasters and is provided as a function of a general messaging app. This disaster rescue system is provided free of charge, contributing to the establishment of account services and the improvement of brand image. When a disaster occurs, the system guides users to safe actions using the following flow: First, when a disaster occurs, AI collects and analyzes disaster information. Next, based on the analysis results, it guides users to appropriate evacuation routes and actions. For example, when an earthquake occurs, AI analyzes the epicenter and damage status and presents the optimal evacuation route to the user. It also provides information necessary for users during evacuation (e.g., the location of evacuation centers and the distribution status of relief supplies). Furthermore, the disaster rescue system utilizes the user's location information to grasp the situation in real time and provide information to rescue teams as needed. This enables rapid rescue operations and contributes to saving lives. This mechanism prevents disruptions to social infrastructure during disasters and ensures user safety. Furthermore, providing the system free of charge contributes to the establishment of account services and the improvement of brand image. This allows the disaster rescue system to ensure the safety of users in the event of a disaster and support rapid rescue operations.

[0059] A disaster rescue system according to an embodiment includes a collection unit, an analysis unit, a guidance unit, a situation assessment unit, and an information provision unit. The collection unit collects disaster information. Examples of disaster information include, but are not limited to, earthquake information, flood information, and fire information. The collection unit collects disaster information using, for example, a sensor. The collection unit can also collect disaster information by receiving reports from users. The collection unit can also collect publicly available information on the Internet. For example, the collection unit collects disaster information in real time using a seismometer or a weather sensor. Users can easily report the information through an app, and the collection unit processes the reports immediately. Publicly available information on the Internet is automatically collected, for example, from government agencies and news sites. The analysis unit analyzes the disaster information collected by the collection unit. The analysis can be performed using, for example, data mining or statistical analysis, but is not limited to, examples. For example, the analysis unit analyzes the collected earthquake information to identify the epicenter and seismic intensity. The analysis unit can also analyze flood information to predict rising river water levels. Furthermore, the analysis unit can analyze fire information and predict the extent of the fire's expansion. For example, the analysis unit identifies the epicenter based on earthquake information and predicts the extent of damage. Flood information is analyzed by analyzing river water level data and assessing the risk of flooding. Fire information is used to predict the extent of the fire's expansion taking wind direction and wind speed into account. The guidance unit guides the user to an evacuation route based on the analysis results obtained by the analysis unit. Guidance can be provided by, for example, voice guidance or map display, but is not limited to these examples. For example, the guidance unit guides the user to the optimal evacuation route by voice. The guidance unit can also display the evacuation route on a map. Furthermore, the guidance unit can guide the user to the nearest evacuation shelter from the user's current location. For example, the guidance unit instructs the user on the evacuation route using voice guidance. The map display visually shows the evacuation route on the smartphone screen. The nearest evacuation shelter is automatically searched for based on the user's current location. The situation understanding unit understands the location information of the user guided by the guidance unit. The location information is obtained by, for example, but not limited to, a method such as GPS or Wi-Fi location information. For example, the situation grasping unit obtains GPS information from the user's smartphone.The situation assessment unit can also identify the user's location using Wi-Fi location information. Furthermore, the situation assessment unit can record the user's movement history and assess the situation in real time. For example, the situation assessment unit tracks the user's current location in real time using GPS. Wi-Fi location information is effective for locating indoor locations. The movement history is used to record the user's evacuation route and analyze it later. The information providing unit provides the information assessed by the situation assessment unit to a rescue team. Information provision can be performed, for example, by real-time notification or periodic reporting, but is not limited to these examples. For example, the information providing unit notifies the rescue team of the user's location information in real time. The information providing unit can also periodically report the user's status. Furthermore, the information providing unit can immediately provide the rescue team with necessary information. For example, the information providing unit transmits the user's location information to the rescue team in real time. The periodic reporting updates the user's status at regular intervals. The necessary information includes detailed data useful for rescue operations. This allows the disaster rescue system according to the embodiment to ensure the safety of users during disasters and support rapid rescue operations.

[0060] 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 is feeling anxious, the collection unit can frequently collect disaster information and provide the latest information. For example, if the user is calm, the collection unit can collect disaster information at regular intervals and provide only the necessary information. For example, if the user is in a panic, the collection unit can immediately collect disaster information and quickly provide countermeasures. This allows disaster information to be collected at an appropriate time depending on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0061] The collection unit can analyze past disaster data and select an information collection method. For example, the collection unit analyzes past earthquake data and selects an information collection method according to the distance from the epicenter. For example, the collection unit can also analyze past typhoon data and select an information collection method based on wind speed and rainfall. For example, the collection unit can analyze past flood data and select an information collection method according to a rise in river water level. This makes it possible to select an optimal information collection method based on past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past disaster data into the generation AI and cause the generation AI to select an optimal information collection method.

[0062] When collecting disaster information, the collection unit can filter the information based on the user's current location information and areas of interest. For example, if the user's current location is close to the epicenter, the collection unit can prioritize collecting earthquake information. For example, if the user is interested in flooding, the collection unit can also prioritize collecting river water level information. For example, if the user's current location is in the path of a typhoon, the collection unit can also prioritize collecting wind speed and rainfall information. This makes it possible to collect appropriate information based on the user's location information and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location information to the generation AI and have the generation AI perform filtering.

[0063] The collection unit can estimate the user's emotions and determine the priority of disaster information to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting important disaster information. For example, if the user is calm, the collection unit can also collect detailed disaster information. For example, if the user is in a panic, the collection unit can also prioritize collecting disaster information with high urgency. This allows important information to be collected preferentially according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0064] When collecting disaster information, the collection unit can prioritize collection of information based on the user's geographical location information. For example, if the user's current location is close to the epicenter, the collection unit prioritizes collection of earthquake information. For example, if the user's current location is in a flood-prone area, the collection unit can also prioritize collection of river water level information. For example, if the user's current location is in the path of a typhoon, the collection unit can also prioritize collection of wind speed and rainfall information. This makes it possible to collect highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information to a generation AI and have the generation AI execute the information prioritization.

[0065] The collection unit can analyze the user's social media activities and collect information when collecting disaster information. For example, if the user posts about earthquakes on social media, the collection unit can prioritize collecting earthquake information. For example, if the user posts about floods, the collection unit can also prioritize collecting river water level information. For example, if the user posts about typhoons, the collection unit can also prioritize collecting wind speed and rainfall information. This makes it possible to collect related information based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related information.

[0066] The analysis unit can estimate the user's emotions and adjust the way the analysis is expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit uses a concise and easy-to-understand expression. For example, if the user is calm, the analysis unit can provide detailed analysis results. For example, if the user is in a panic, the analysis unit can provide information that is highly urgent by emphasizing it. This allows the analysis results to be provided in an appropriate expression depending on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0067] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the disaster information. For example, if the epicenter is nearby, the analysis unit analyzes detailed earthquake information. For example, if there is a high risk of flooding, the analysis unit can also analyze river water level information in detail. For example, if the path of a typhoon is close to the user's current location, the analysis unit can also analyze wind speed and rainfall information in detail. This allows analysis at an appropriate level of detail depending on the importance of the disaster information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input disaster information to a generation AI and have the generation AI adjust the level of detail.

[0068] During analysis, the analysis unit can apply different analysis algorithms depending on the type of disaster. For example, in the case of an earthquake, the analysis unit applies an analysis algorithm based on the epicenter and seismic intensity. For example, in the case of a flood, the analysis unit can also apply an analysis algorithm based on the water level of a river and the amount of rainfall. For example, in the case of a typhoon, the analysis unit can also apply an analysis algorithm based on the wind speed and path. This makes it possible to apply an appropriate analysis algorithm depending on the type of disaster. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the type of disaster into the generation AI and cause the generation AI to apply the analysis algorithm.

[0069] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a short and to-the-point analysis result. For example, if the user is calm, the analysis unit can provide a detailed analysis result. For example, if the user is in a panic, the analysis unit can provide short, concise information with high urgency. This allows the analysis result to be provided at an appropriate length depending on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0070] During analysis, the analysis unit can determine the priority of analysis based on the time when disaster information was collected. The analysis unit, for example, prioritizes analysis of the latest earthquake information. The analysis unit can also prioritize analysis of the latest flood information. The analysis unit can also prioritize analysis of the latest typhoon information. This allows analysis to be performed with appropriate priority based on the time when disaster information was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when disaster information was collected into the generation AI and have the generation AI determine the priority.

[0071] During analysis, the analysis unit can adjust the order of analysis based on the relevance of disaster information. For example, the analysis unit prioritizes analysis of earthquake information related to the user's current location. For example, the analysis unit can also prioritize analysis of flood information related to the user's area of ​​interest. For example, the analysis unit can also prioritize analysis of typhoon information related to the user's current location. This allows analysis to be performed in an appropriate order based on the relevance of disaster information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of disaster information to the generation AI and have the generation AI execute the analysis order.

[0072] The guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the guidance unit uses a concise and easy-to-understand presentation. For example, if the user is calm, the guidance unit can provide detailed guidance. For example, if the user is in a panic, the guidance unit can provide information that emphasizes urgency. This allows the guidance to be presented in an appropriate presentation depending on the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0073] The guidance unit can adjust the level of detail of the guidance based on the importance of the evacuation route when providing guidance. For example, if the epicenter is nearby, the guidance unit provides guidance on a detailed evacuation route. For example, if there is a high risk of flooding, the guidance unit can also provide guidance on a detailed evacuation route. For example, if the path of a typhoon is close to the user's current location, the guidance unit can also provide guidance on a detailed evacuation route. This makes it possible to provide guidance with an appropriate level of detail depending on the importance of the evacuation route. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can input the importance of the evacuation route to the generation AI and cause the generation AI to adjust the level of detail.

[0074] The guidance unit can apply different guidance algorithms depending on the type of disaster when providing guidance. For example, in the case of an earthquake, the guidance unit applies a guidance algorithm based on the epicenter and seismic intensity. For example, in the case of a flood, the guidance unit can also apply a guidance algorithm based on the water level of a river and the amount of rainfall. For example, in the case of a typhoon, the guidance unit can also apply a guidance algorithm based on the wind speed and path. This makes it possible to apply an appropriate guidance algorithm depending on the type of disaster. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can input the type of disaster into the generation AI and cause the generation AI to apply the guidance algorithm.

[0075] The guidance unit can estimate the user's emotions and adjust the length of the guidance based on the estimated user's emotions. For example, if the user is feeling anxious, the guidance unit can provide short, to-the-point guidance. For example, if the user is calm, the guidance unit can provide detailed guidance. For example, if the user is in a panic, the guidance unit can provide short, concise information with high urgency. This allows guidance to be provided at an appropriate length depending on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0076] When providing guidance, the guidance unit can determine the priority of guidance based on the time of submission of the evacuation route. The guidance unit, for example, prioritizes guidance of the latest evacuation route information. The guidance unit can also prioritize guidance of the latest flood evacuation route information. The guidance unit can also prioritize guidance of the latest typhoon evacuation route information. This makes it possible to provide guidance with appropriate priority based on the time of submission of the evacuation route. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can input the time of submission of the evacuation route to the generation AI and have the generation AI determine the priority.

[0077] The guidance unit can adjust the order of guidance based on the relevance of evacuation routes when providing guidance. For example, the guidance unit prioritizes guidance of evacuation route information related to the user's current location. For example, the guidance unit can also prioritize guidance of evacuation route information related to the user's area of ​​interest. For example, the guidance unit can also prioritize guidance of typhoon evacuation route information related to the user's current location. This makes it possible to provide guidance in an appropriate order based on the relevance of evacuation routes. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the relevance of evacuation routes to a generation AI and have the generation AI execute the order of guidance.

[0078] The situation understanding unit can estimate the user's emotions and adjust the method of understanding the situation based on the estimated user emotions. For example, if the user is feeling anxious, the situation understanding unit can frequently understand the situation and provide the latest information. For example, if the user is calm, the situation understanding unit can understand the situation at regular intervals and provide only the necessary information. For example, if the user is in a panic, the situation understanding unit can immediately understand the situation and quickly provide a countermeasure. This allows the situation to be understood in an appropriate manner depending on the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. Some or all of the above-mentioned processing in the situation understanding unit may be performed using, for example, AI, or may be performed without AI. For example, the situation understanding unit can input the user's facial expression data into a generation AI and have the generation AI execute emotion estimation.

[0079] When assessing a situation, the situation assessment unit can select an optimal situation assessment method by analyzing the user's past behavioral history. The situation assessment unit selects the optimal situation assessment method, for example, based on the user's past evacuation routes. The situation assessment unit can also select a situation assessment method that avoids congestion, for example, based on the user's past behavioral history. The situation assessment unit can also analyze the user's past behavioral history and select the most efficient situation assessment method. This makes it possible to select an optimal situation assessment method based on the user's past behavioral history. Some or all of the above-described processing in the situation assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the situation assessment unit can input the user's past behavioral history data into the generation AI and cause the generation AI to select an optimal situation assessment method.

[0080] When grasping the situation, the situation grasping unit can customize the means of grasping the situation based on the user's current living situation. For example, when the user is at home, the situation grasping unit prioritizes grasping disaster information around the home. For example, when the user is at work, the situation grasping unit can also prioritize grasping disaster information around the workplace. For example, when the user is out, the situation grasping unit can also prioritize grasping disaster information around the current location. This makes it possible to grasp the situation using an appropriate means depending on the user's current living situation. Some or all of the above-mentioned processing in the situation grasping unit may be performed using AI, for example, or may be performed without using AI. For example, the situation grasping unit can input the user's living situation data into the generation AI and cause the generation AI to customize the means of grasping the situation.

[0081] The situation understanding unit can estimate the user's emotions and determine the priority of situation understanding based on the estimated user emotions. For example, if the user is feeling anxious, the situation understanding unit prioritizes understanding of important situations. For example, if the user is calm, the situation understanding unit can also grasp detailed situations. For example, if the user is in a panic, the situation understanding unit can also prioritize understanding of situations with high urgency. This allows important situations to be prioritized according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. Some or all of the above-mentioned processing in the situation understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the situation understanding unit can input the user's facial expression data into a generation AI and cause the generation AI to estimate emotions.

[0082] When assessing a situation, the situation assessment unit can select an optimal situation assessment method by taking into account the user's geographical location information. For example, if the user's current location is close to the epicenter, the situation assessment unit prioritizes assessing earthquake information. For example, if the user's current location is in a flood-prone area, the situation assessment unit can also prioritize assessing river water level information. For example, if the user's current location is in the path of a typhoon, the situation assessment unit can also prioritize assessing wind speed and rainfall information. This allows the optimal situation assessment method to be selected based on the user's geographical location information. Some or all of the above-described processing in the situation assessment unit may be performed using, or without, AI, for example. For example, the situation assessment unit can input the user's geographical location information to the generation AI and cause the generation AI to select an optimal situation assessment method.

[0083] When assessing a situation, the situation assessment unit can analyze the user's social media activity and suggest means for assessing the situation. For example, if the user posts about an earthquake on social media, the situation assessment unit can prioritize assessing earthquake information. For example, if the user posts about a flood, the situation assessment unit can also prioritize assessing river water level information. For example, if the user posts about a typhoon, the situation assessment unit can also prioritize assessing wind speed and rainfall information. This makes it possible to assess the situation using appropriate means based on the user's social media activity. Some or all of the above-described processing in the situation assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the situation assessment unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest means for assessing the situation.

[0084] The information providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is feeling anxious, the information providing unit uses a concise and easy-to-understand information providing method. For example, if the user is calm, the information providing unit can also provide detailed information. For example, if the user is in a panic, the information providing unit can also provide information that emphasizes urgency. This makes it possible to provide information in an appropriate manner depending on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's facial expression data into a generation AI and cause the generation AI to estimate emotions.

[0085] When providing information, the information providing unit can select the optimal information providing method by analyzing the user's past behavioral history. The information providing unit selects the optimal information providing method, for example, based on the user's past evacuation route. The information providing unit can also select an information providing method that avoids congestion, for example, based on the user's past behavioral history. The information providing unit can also analyze the user's past behavioral history and select the most efficient information providing method. This makes it possible to select the optimal information providing method based on the user's past behavioral history. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's past behavioral history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0086] The information providing unit can customize the means of providing information based on the user's current living situation when providing information. For example, when the user is at home, the information providing unit can prioritize providing disaster information around the user's home. For example, when the user is at work, the information providing unit can also prioritize providing disaster information around the workplace. For example, when the user is out, the information providing unit can also prioritize providing disaster information around the user's current location. This makes it possible to provide information by an appropriate means depending on the user's current living situation. Some or all of the above-mentioned processing in the information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the information providing unit can input the user's living situation data into the generation AI and cause the generation AI to customize the means of providing information.

[0087] The information providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. For example, when the user is feeling anxious, the information providing unit can prioritize providing important information. For example, when the user is calm, the information providing unit can also provide detailed information. For example, when the user is in a panic, the information providing unit can also prioritize providing information with high urgency. This makes it possible to prioritize providing important information according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotion.

[0088] When providing information, the information providing unit can select the optimal information providing method by taking into consideration the user's geographical location information. For example, if the user's current location is close to the epicenter, the information providing unit can prioritize providing earthquake information. For example, if the user's current location is in a flood-prone area, the information providing unit can also prioritize providing river water level information. For example, if the user's current location is in the path of a typhoon, the information providing unit can also prioritize providing wind speed and rainfall information. This makes it possible to select the optimal information providing method based on the user's geographical location information. Some or all of the above-described processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal information providing method.

[0089] When providing information, the information providing unit can analyze the user's social media activity and suggest a means of providing the information. For example, if the user posts about earthquakes on social media, the information providing unit can prioritize providing earthquake information. For example, if the user posts about floods, the information providing unit can also prioritize providing river water level information. For example, if the user posts about typhoons, the information providing unit can also prioritize providing information about wind speed and rainfall. This makes it possible to provide information by an appropriate means based on the user's social media activity. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest a means of providing information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, guidance unit, situation assessment unit, and information provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects disaster information using the sensors and camera 42 of the smart device 14, and the collected disaster information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected disaster information. The guidance unit is realized, for example, by the control unit 46A of the smart device 14 and guides the user to an evacuation route. The situation assessment unit is realized, for example, by the GPS function of the smart device 14 and assesses the situation in real time by the specific processing unit 290 of the data processing device 12. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides the user's location information to a rescue team. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting disaster information based on the estimated emotions. The emotion estimation is performed using, for example, the camera 42 and microphone 38B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, guidance unit, situation assessment unit, and information provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects disaster information using the sensors and camera 42 of the smart glasses 214, and the collected disaster information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected disaster information. The guidance unit is realized, for example, by the control unit 46A of the smart glasses 214 and guides the user to an evacuation route. The situation assessment unit is realized, for example, by the GPS function of the smart glasses 214 and assesses the situation in real time by the specific processing unit 290 of the data processing device 12. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides the user's location information to a rescue team. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting disaster information based on the estimated emotions. Emotion estimation is performed using, for example, the camera 42 and microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, guidance unit, situation assessment unit, and information provision unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects disaster information using the sensors and camera 42 of the headset terminal 314, and the collected disaster information is analyzed by the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected disaster information. The guidance unit is realized, for example, by the control unit 46A of the headset terminal 314 and guides the user to an evacuation route. The situation assessment unit is realized, for example, by the GPS function of the headset terminal 314 and assesses the situation in real time by the identification processing unit 290 of the data processing device 12. The information provision unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and provides the user's location information to a rescue team. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting disaster information based on the estimated emotions. The emotion estimation is performed using, for example, the camera 42 and microphone 238 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, guidance unit, situation assessment unit, and information provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects disaster information using the sensors and camera 42 of the robot 414, and the collected disaster information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected disaster information. The guidance unit is realized, for example, by the control unit 46A of the robot 414 and guides the user to an evacuation route. The situation assessment unit, for example, uses the GPS function of the robot 414 to assess the user's location information and the specific processing unit 290 of the data processing device 12 to assess the situation in real time. The information provision unit, for example, is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides the user's location information to a rescue team. The collection unit, for example, can estimate the user's emotions and adjust the timing of collecting disaster information based on the estimated emotions. The emotion estimation is performed using, for example, the camera 42 and microphone 238 of the robot 414 .

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The disaster rescue system can also be equipped with a health management unit that monitors the user's health condition. The health management unit monitors the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time and can immediately issue an alert if an abnormality is detected. For example, if the user's heart rate suddenly rises during evacuation, the health management unit can send that information to the analysis unit and guide the user on appropriate measures. Also, if the user has a chronic illness, registering that information in advance can enable the system to quickly provide necessary medical assistance in the event of a disaster. Furthermore, the health management unit can monitor the user's stress level and suggest relaxation methods as needed.

[0092] The disaster rescue system can estimate a user's emotions and support communication at evacuation centers based on the estimated emotions. For example, if a user feels anxious, the system can send a message to encourage interaction with other evacuees. If a user feels lonely, the system can put the user in touch with a psychological counselor. Furthermore, if a user is in a state of panic, the system can provide relaxing music or a meditation guide. This can strengthen psychological support at evacuation centers and increase users' sense of security.

[0093] The disaster rescue system may further include a pet management unit to ensure the safety of the user's pet. The pet management unit can track the location information of the user's pet and check whether the pet is in a safe place during a disaster. For example, if the pet has not arrived at an evacuation shelter, the pet management unit can identify its location and notify the user. It can also monitor the pet's health and provide necessary medical assistance. Furthermore, the pet management unit can guide the pet's evacuation route and support the user and pet in evacuating together.

[0094] The disaster rescue system can estimate a user's emotions and provide entertainment at evacuation shelters based on the estimated emotions. For example, if a user feels anxious, the system can suggest relaxing movies or music. If a user feels bored, the system can provide games or reading content. Furthermore, if a user feels lonely, the system can provide a platform where they can interact with other evacuees online. This allows users to spend their time at evacuation shelters meaningfully and reduces the mental burden on users.

[0095] The disaster rescue system may further include a communication unit that helps the user contact family and friends. The communication unit provides a means for the user to contact family and friends during a disaster. For example, even if the communication infrastructure is down, messages can be sent using satellite communication. The communication unit may also share the user's location information with family and friends to quickly confirm their safety. Furthermore, the communication unit may have a function to automatically send a notification when the user arrives at an evacuation shelter. This increases the user's sense of security and helps maintain ties with family and friends.

[0096] The disaster rescue system can estimate a user's emotions and adjust the food provided at evacuation centers based on the estimated emotions. For example, if a user is feeling anxious, the system can prioritize providing nutritious meals. If a user is feeling stressed, the system can suggest relaxing herbal teas and light meals. Furthermore, if a user is in a state of panic, the system can provide easy-to-digest meals to help them improve their physical condition. This helps maintain the user's health and mental stability.

[0097] The disaster rescue system may further include an asset management unit for protecting the user's assets. The asset management unit can track the location information of the user's important assets and provide guidance for moving to a safe location in the event of a disaster. For example, if the user's car is in a flood-prone area, the asset management unit can send that information to the analysis unit and guide the user to an appropriate evacuation site. Also, if the user's house is affected by an earthquake, the asset management unit can assess the damage and support repair and insurance claim procedures. Furthermore, the asset management unit can manage the storage locations of the user's important documents and valuables and help them be quickly retrieved in the event of a disaster.

[0098] A disaster rescue system can estimate a user's emotions and coordinate medical assistance at evacuation centers based on the estimated emotions. For example, if a user feels anxious, the system can provide a psychological counseling session. If a user feels stressed, the system can also refer the user to a medical professional who can teach relaxation techniques. Furthermore, if a user is in a state of panic, the system can quickly arrange for emergency medical assistance and provide the necessary medication or treatment. This helps ensure the user's health and safety and promotes mental stability.

[0099] The disaster rescue system can further include a planning support unit that creates an evacuation plan for the user in advance. The planning support unit can plan the optimal evacuation route in advance based on information such as the user's residence, workplace, and school route. For example, if the user lives in an area prone to earthquakes, the planning support unit can provide detailed evacuation route information in the event of an earthquake. Also, if the user lives in an area at risk of flooding, the planning support unit can create an evacuation plan for flooding and list the necessary items to prepare. Furthermore, the planning support unit can integrate the evacuation plans of all members of the user's family and support everyone in evacuating safely.

[0100] The disaster rescue system can estimate a user's emotions and provide educational support at evacuation shelters based on the estimated emotions. For example, if a user feels anxious, the system can provide relaxing learning content. If a user feels bored, the system can suggest an interesting educational program. Furthermore, if a user feels lonely, the system can provide an online class where they can study together with other evacuees. This allows users to spend their time at evacuation shelters meaningfully and reduces the mental burden on users.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects disaster information. Disaster information includes earthquake information, flood information, and fire information. The collection unit uses sensors to collect disaster information, as well as user reports and public information on the Internet. For example, the unit collects disaster information in real time using seismometers and weather sensors, receives user reports through an app, and automatically collects public information on the Internet from government agencies and news sites. Step 2: The analysis unit analyzes the disaster information collected by the collection unit. The analysis is performed using methods such as data mining and statistical analysis. For example, earthquake information can be analyzed to identify the epicenter and seismic intensity, flood information can be analyzed to predict rising river water levels, and fire information can be analyzed to predict the extent of the fire. Step 3: The guidance unit guides the user to an evacuation route based on the analysis results obtained by the analysis unit. The guidance is provided by voice guidance, map display, or other methods. For example, the unit may guide the user to the optimal evacuation route by voice, display the evacuation route on a map, and guide the user to the nearest evacuation shelter from the user's current location. Step 4: The situation grasping unit grasps the location information of the user guided by the guidance unit. The location information is acquired by methods such as GPS or Wi-Fi location information. For example, GPS information is acquired from the user's smartphone, and the indoor location is identified using Wi-Fi location information, and the user's movement history is recorded to grasp the situation in real time. Step 5: The information providing unit provides the information grasped by the situation grasping unit to the rescue team. The information is provided by methods such as real-time notification and periodic reports. For example, the information of the user's location is notified to the rescue team in real time, the user's situation is reported periodically, and necessary information is provided immediately.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[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 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.

[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 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.

[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 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.

[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] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects disaster information; an analysis unit that analyzes the information collected by the collection unit; a guidance unit that guides a user through an evacuation route based on the analysis result obtained by the analysis unit; a situation grasping unit that grasps location information of the user guided by the guidance unit; an information providing unit that provides the information grasped by the situation grasping unit to a rescue team; A system comprising:

2. The collecting unit Estimates user emotions and adjusts the timing of disaster information collection based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze past disaster data and select information collection methods 2. The system of claim 1.

4. The collecting unit When collecting disaster information, filtering is performed based on the user's current location and areas of interest.

2. The system of claim 1.

5. The collecting unit Estimate the user's emotions and determine the priority of disaster information to be collected based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit When collecting disaster information, prioritize collection of information based on the user's geographical location information.

2. The system of claim 1.

7. The collecting unit When collecting disaster information, analyze users' social media activities and collect information.

2. The system of claim 1.

8. The analysis unit Estimate the user's emotions and adjust the analysis presentation method based on the estimated user emotions.

2. The system of claim 1.

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