System

Abstract

An object of a system according to an embodiment is to provide a quick and appropriate evacuation route when an emergency occurs.SOLUTION: A system includes a generation unit, a navigation unit, a guide unit, and an evacuation route providing unit. The generation unit generates information corresponding to the emergency situation scenario. The navigation unit performs navigation based on the information generated by the generation unit. The guiding unit guides the user based on the information provided by the navigation unit. The evacuation route providing unit is activated when an emergency occurs, and provides an appropriate evacuation route based on the video from the drone.SELECTED DRAWING: Figure 1

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 have had the problem of making it difficult to provide quick and appropriate evacuation routes in the event of an emergency.

[0005] The system according to the embodiment aims to provide a quick and appropriate evacuation route in the event of an emergency. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a navigation unit, a guidance unit, and an evacuation route providing unit. The generation unit generates information corresponding to an emergency scenario. The navigation unit performs navigation based on the information generated by the generation unit. The guidance unit guides a user based on the information provided by the navigation unit. The evacuation route providing unit is activated when an emergency occurs and provides an appropriate evacuation route based on footage from a drone. [Effects of the Invention]

[0007] The system according to the embodiment can provide a quick and appropriate evacuation route in the event of an emergency. [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 touch of 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) An emergency support system according to an embodiment of the present invention generates information corresponding to an emergency scenario and guides a user to a safe evacuation shelter by combining navigation via AR glasses and audio guidance. The emergency support system generates information corresponding to an emergency scenario and guides a user to a safe evacuation shelter by combining navigation via AR glasses and audio guidance. Furthermore, when an emergency occurs, a drone is activated, and a generation AI provides an optimal evacuation route based on the image captured by the drone, taking into account the terrain and obstacles. For example, when an emergency such as a fire or earthquake occurs, the emergency support system generates evacuation information tailored to the situation. This information includes instructions for the user to evacuate quickly and safely. The emergency support system then displays an evacuation route on the AR glasses' display and provides specific instructions to the user via audio guidance. This allows the user to confirm the evacuation route using both vision and hearing. Furthermore, when an emergency occurs, a drone is activated, and the drone transmits video of the scene in real time to the generation AI. The generation AI analyzes the video and provides an optimal evacuation route that takes into account the terrain and obstacles. For example, the drone monitors the progress of a building collapse or fire and adjusts the evacuation route accordingly. This allows the emergency support system to enable users to evacuate quickly and safely. This allows the emergency support system to assist users in evacuating safely and quickly. For example, when a user faces an emergency, the system can provide comprehensive support by combining visual, auditory, and real-time information.

[0029] An emergency support system according to an embodiment includes a generation unit, a navigation unit, a guidance unit, and an evacuation route provision unit. The generation unit generates information corresponding to an emergency scenario. The generation unit generates information corresponding to an emergency, such as a fire or an earthquake. The generation unit can also generate evacuation information corresponding to the emergency using a generation AI. For example, the generation AI generates information indicating an evacuation route in the event of a fire. The generation unit can also generate information indicating a safe evacuation location in the event of an earthquake. The navigation unit performs navigation based on the information generated by the generation unit. For example, the navigation unit displays an evacuation route on the display of the AR glasses and provides audio guidance. The navigation unit can also provide specific instructions to the user using the generation AI. For example, the navigation unit displays an evacuation route on the display of the AR glasses and provides specific instructions to the user via audio guidance, such as "Turn right." The guidance unit guides the user based on the information provided by the navigation unit. For example, the guidance unit allows the user to confirm the evacuation route using the user's vision and hearing. The guidance unit can also provide visual and audio instructions to the user using the generation AI. For example, the guidance unit displays an evacuation route on the display of the AR glasses, and an audio guide provides the user with specific instructions such as "Turn left at the next intersection." The evacuation route providing unit is activated when an emergency occurs and provides an optimal evacuation route based on video footage from a drone. For example, the drone transmits video footage of the scene to a generation AI in real time, and the generation AI provides an optimal evacuation route taking into account the terrain and obstacles. The evacuation route providing unit can also use the generation AI to analyze video footage from the drone and adjust the evacuation route. For example, the evacuation route providing unit checks the progress of a building collapse or a fire using the drone, and adjusts the evacuation route based on the results. This allows the emergency support system according to the embodiment to support a user's quick and safe evacuation. Some or all of the above-described processing in the evacuation route providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI.For example, the evacuation route provision unit can input footage from a drone into the generation AI and have the generation AI generate an evacuation route that takes into account the terrain and obstacles.

[0030] The generation unit can generate information corresponding to a fire or earthquake emergency. For example, the generation unit generates information indicating an evacuation route in the event of a fire. The generation unit can also generate information indicating a safe evacuation location in the event of an earthquake. For example, the generation unit generates an optimal evacuation route taking into account the progression of the fire. The generation unit can also generate a safe evacuation location taking into account the seismic intensity and damage status of the earthquake. This allows the generation unit to provide appropriate information according to the emergency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input fire or earthquake data into the generation AI and cause the generation AI to generate evacuation information.

[0031] The navigation unit can display an evacuation route on the display of the AR glasses and provide audio guidance. The navigation unit, for example, displays the evacuation route on the display of the AR glasses. The navigation unit can also provide audio guidance. For example, the navigation unit displays the evacuation route on the display of the AR glasses, and the audio guidance provides specific instructions to the user, such as "Turn right." The navigation unit can also provide visual and audio instructions to the user using a generation AI. For example, the navigation unit displays the evacuation route on the display of the AR glasses, and the audio guidance provides specific instructions to the user, such as "Turn left at the next intersection." This allows the navigation unit to guide the user using both vision and hearing. Some or all of the above-described processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can cause the generation AI to display the evacuation route and provide audio guidance.

[0032] The guidance unit can allow the user to confirm an evacuation route using both their vision and hearing. For example, the guidance unit displays the evacuation route on the display of the AR glasses to the user's vision. The guidance unit can also provide audio guidance to the user's hearing. For example, the guidance unit displays the evacuation route on the display of the AR glasses, and audio guidance provides specific instructions to the user, such as "Turn right." The guidance unit can also provide visual and audio instructions to the user using a generation AI. For example, the guidance unit displays the evacuation route on the display of the AR glasses, and audio guidance provides specific instructions to the user, such as "Turn left at the next intersection." This allows the guidance unit to allow the user to confirm an evacuation route using both their vision and hearing. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can cause the generation AI to provide visual and audio instructions.

[0033] The evacuation route providing unit can analyze video from a drone and provide an appropriate evacuation route that takes into account terrain and obstacles. For example, the drone transmits video of the scene to the generation AI in real time, and the generation AI provides an optimal evacuation route that takes into account terrain and obstacles. The evacuation route providing unit can also use the generation AI to analyze video from the drone and adjust the evacuation route. For example, the drone checks the status of building collapse or the progression of a fire and adjusts the evacuation route based on that. This allows the evacuation route providing unit to provide an optimal evacuation route based on real-time video analysis. Some or all of the above-described processing in the evacuation route providing unit may be performed using, or without, the generation AI. For example, the evacuation route providing unit can input video from a drone to the generation AI and cause the generation AI to generate an evacuation route that takes into account terrain and obstacles.

[0034] The evacuation route providing unit can use a drone to check the collapsed state of a building or the progression of a fire and adjust the evacuation route based on that. For example, the evacuation route providing unit can use a drone to check the collapsed state of a building and adjust the evacuation route based on that. The evacuation route providing unit can also use a drone to check the progression of a fire and adjust the evacuation route based on that. For example, the evacuation route providing unit can use a drone to check the collapsed state of a building and adjust the evacuation route based on that. The evacuation route providing unit can also use a drone to check the progression of a fire and adjust the evacuation route based on that. This allows the evacuation route providing unit to adjust the evacuation route according to the real-time situation. Some or all of the above-mentioned processing in the evacuation route providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evacuation route providing unit can input video from a drone to the generation AI and cause the generation AI to adjust the evacuation route taking into account the collapsed state of a building or the progression of a fire.

[0035] The generation unit can apply different information generation algorithms depending on the type of emergency. For example, in the case of a fire, the generation AI generates an evacuation route that takes into account the spread of smoke and the progression of the fire. In addition, in the case of an earthquake, the generation AI can generate an evacuation route that takes into account the risk of building collapse. Furthermore, in the case of a flood, the generation AI can generate an evacuation route that takes into account rising water levels. This allows the generation unit to provide appropriate information depending on the type of emergency. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input data depending on the type of emergency into the generation AI and cause the generation AI to apply an information generation algorithm.

[0036] The generation unit can improve the accuracy of information generation by referring to past emergency situation data. The generation unit, for example, optimizes evacuation routes in the event of a fire based on past fire data. The generation unit can also optimize evacuation routes in the event of an earthquake based on past earthquake data. Furthermore, the generation unit can also optimize evacuation routes in the event of a flood based on past flood data. This allows the generation unit to improve the accuracy of information generation by utilizing past data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past emergency situation data into the generation AI and cause the generation AI to improve the accuracy of information generation.

[0037] The generation unit can update information in real time according to the progress of the emergency situation. The generation unit updates evacuation routes in real time according to the progress of a fire, for example. The generation unit can also update evacuation routes in real time based on aftershock information of an earthquake. Furthermore, the generation unit can also update evacuation routes in real time according to rising water levels of a flood. This allows the generation unit to respond to the latest situation by updating information in real time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the progress of the emergency situation to the generation AI and cause the generation AI to perform real-time updates of the information.

[0038] The generation unit can generate region-specific information based on the location of the emergency. For example, in the case of a fire in an urban area, the generation unit generates information that takes into account evacuation routes for buildings. In addition, in the case of an earthquake in a mountainous area, the generation unit can generate information that takes into account the risk of landslides. Furthermore, in the case of a tsunami along the coast, the generation unit can generate information that takes into account evacuation routes to higher ground. In this way, the generation unit can provide more appropriate evacuation information by providing region-specific information. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the location of the emergency into the generation AI and cause the generation AI to generate region-specific information.

[0039] The generation unit can analyze the user's past evacuation history and select the optimal information generation method. The generation unit can generate optimal information based on, for example, evacuation routes used by the user in the past. The generation unit can also generate information to avoid congestion from the user's past evacuation history. Furthermore, the generation unit can analyze the user's past evacuation history and generate the most efficient information. This allows the generation unit to provide optimal information by utilizing the past evacuation history. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past evacuation history data into the generation AI and cause the generation AI to select an information generation method.

[0040] The generation unit can adjust the content of the information generation depending on the time period during which the emergency occurs. For example, in the case of a fire at night, the generation unit generates highly visible information. In addition, in the case of an earthquake during the daytime, the generation unit can also generate information that takes into account the surrounding conditions. Furthermore, in the case of a flood in the early morning, the generation unit can generate information that takes into account the opening status of evacuation shelters. This allows the generation unit to provide appropriate information depending on the time period during which the emergency occurs. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the time period during which the emergency occurs into the generation AI and cause the generation AI to adjust the content of the information generation.

[0041] The navigation unit can update the user's current location information in real time during navigation. For example, the navigation unit updates the user's current location in real time while the user is moving and performs navigation. The navigation unit can also update the current location in real time as the user approaches the destination and suggest an optimal route. Furthermore, if the user gets lost, the navigation unit can update the current location in real time and perform navigation again. This allows the navigation unit to provide accurate navigation by updating the current location information in real time. Some or all of the above-described processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's current location information to the generation AI and have the generation AI perform real-time updates.

[0042] The navigation unit can optimize the route during navigation, taking into account the congestion status of the evacuation route. For example, the navigation unit can optimize the route based on real-time congestion information to avoid congested evacuation routes. The navigation unit can also optimize the route based on past congestion data to avoid routes that are expected to be congested. Furthermore, the navigation unit can also optimize the route based on real-time congestion information to preferentially propose routes where congestion has been alleviated. This allows the navigation unit to provide the optimal route taking into account the congestion status. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input congestion information into the generation AI and cause the generation AI to optimize the route.

[0043] During navigation, the navigation unit can adjust the timing of guidance according to the user's moving speed. For example, if the user is walking fast, the navigation unit can advance the timing of guidance. Also, if the user is walking slowly, the navigation unit can delay the timing of guidance. Furthermore, the navigation unit can pause guidance when the user stops and resume when the user starts walking again. This allows the navigation unit to provide appropriate guidance according to the user's moving speed. Some or all of the above-mentioned processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input user's moving speed data into the generation AI and have the generation AI adjust the timing of guidance.

[0044] During navigation, the navigation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can also provide a display method that is simple and highly visible. This allows the navigation unit to provide the optimal display method according to the user's device. Some or all of the above-mentioned processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input the user's device information into the generation AI and have the generation AI select the display method.

[0045] During navigation, the navigation unit can suggest an optimal route by referring to the user's past evacuation history. For example, the navigation unit can suggest an optimal route based on evacuation routes used by the user in the past. The navigation unit can also suggest a route that avoids crowded areas based on the user's past evacuation history. Furthermore, the navigation unit can analyze the user's past evacuation history and suggest the most efficient route. This allows the navigation unit to provide an optimal route by utilizing the past evacuation history. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's past evacuation history data into the generation AI and have the generation AI suggest a route.

[0046] The navigation unit can provide a multilingual guide during navigation according to the user's language setting. The navigation unit, for example, automatically sets the navigation language based on the language setting of the user's device. The navigation unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the navigation unit can provide navigation in that language. By providing a multilingual guide, the navigation unit can thereby provide information that is easy for the user to understand. Some or all of the above-described processing in the navigation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input the user's language setting data into the generation AI and cause the generation AI to provide a multilingual guide.

[0047] The guidance unit can provide information to the user using both their vision and hearing when guiding them. For example, the guidance unit displays an evacuation route on the display of the AR glasses to the user's vision. The guidance unit can also guide the user about the evacuation route using audio guidance to the user's hearing. Furthermore, the guidance unit can provide more effective guidance by combining the user's vision and hearing. This allows the guidance unit to provide effective guidance using both vision and hearing. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can cause the generation AI to provide visual and auditory information.

[0048] The guidance unit can customize the content of the guidance according to the user's current situation when guiding. For example, if the user is inside a building, the guidance unit provides guidance that takes into account the building's structure. Furthermore, if the user is outdoors, the guidance unit can also provide guidance that takes into account the surrounding terrain. Furthermore, if the user is using a wheelchair, the guidance unit can also guide the user along a barrier-free route. This allows the guidance unit to provide appropriate guidance according to the user's situation. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input the user's current situation data into the generation AI and have the generation AI customize the content of the guidance.

[0049] The guidance unit can improve the guidance method by reflecting user feedback during guidance. For example, the guidance unit improves the next guidance method based on feedback provided by the user. The guidance unit can also reflect user feedback in real time and adjust the current guidance method. Furthermore, the guidance unit can analyze user feedback and make improvements to solve common problems. In this way, the guidance unit can improve the guidance method by utilizing user feedback. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input user feedback data into the generation AI and have the generation AI improve the guidance method.

[0050] The guidance unit can select the optimal guidance method by taking into account the user's geographical location information when guiding. For example, if the user is in an urban area, the guidance unit can provide guidance that takes into account the building's evacuation route. Furthermore, if the user is in a mountainous area, the guidance unit can provide guidance that takes into account the risk of landslides. Furthermore, if the user is on the coast, the guidance unit can provide guidance that takes into account the risk of tsunamis. This allows the guidance unit to provide appropriate guidance based on the geographical location information. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the guidance unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal guidance method.

[0051] The guidance unit can analyze the user's social media activities and provide relevant information when guiding the user. For example, the guidance unit can suggest an evacuation route based on the location where the user checked in on social media. The guidance unit can also analyze the content of the user's social media posts and provide relevant evacuation information. Furthermore, the guidance unit can provide relevant evacuation information by referring to the activities of the user's friends on social media. This allows the guidance unit to provide relevant information by utilizing social media activities. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input the user's social media data into the generation AI and have the generation AI provide the relevant information.

[0052] The guidance unit can customize the guidance method by reflecting the user's past feedback during guidance. For example, the guidance unit improves the next guidance method based on feedback provided by the user in the past. The guidance unit can also adjust the current guidance method by reflecting the user's past feedback in real time. Furthermore, the guidance unit can analyze the user's past feedback and make improvements to solve common problems. This allows the guidance unit to customize the guidance method by utilizing the past feedback. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input the user's past feedback data into the generation AI and have the generation AI customize the guidance method.

[0053] The evacuation route providing unit can analyze video from the drone in real time and update the evacuation route. For example, the evacuation route providing unit uses the drone to confirm the collapse status of a building and update the evacuation route based on that. The evacuation route providing unit can also use the drone to confirm the progress of a fire and update the evacuation route based on that. Furthermore, the evacuation route providing unit can also use the drone to confirm rising flood water levels and update the evacuation route based on that. This allows the evacuation route providing unit to update the evacuation route based on real-time video analysis. Some or all of the above-mentioned processing in the evacuation route providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evacuation route providing unit can input video from the drone to the generation AI and have the generation AI perform real-time analysis.

[0054] When providing an evacuation route, the evacuation route providing unit can optimize the route by taking into account changes in terrain and obstacles. The evacuation route providing unit can optimize the evacuation route by taking into account changes in terrain due to an earthquake, for example. The evacuation route providing unit can also optimize the evacuation route by taking into account the occurrence of obstacles due to a fire. Furthermore, the evacuation route providing unit can also optimize the evacuation route by taking into account changes in water levels due to a flood. In this way, the evacuation route providing unit can provide an optimal route by taking into account changes in terrain and obstacles. Some or all of the above-mentioned processing in the evacuation route providing unit may be performed using, or without, a generation AI, for example. For example, the evacuation route providing unit can input data on terrain and obstacles into the generation AI and cause the generation AI to optimize the route.

[0055] When providing an evacuation route, the evacuation route providing unit can adjust the route taking into account the user's movement speed and physical strength. For example, if the user can move quickly, the evacuation route providing unit provides the shortest route. Furthermore, if the user moves slowly, the evacuation route providing unit can also provide a route that includes rest points. Furthermore, the evacuation route providing unit can provide a reasonable route taking into account the user's physical strength. This allows the evacuation route providing unit to provide an appropriate route according to the user's movement speed and physical strength. Some or all of the above-mentioned processing in the evacuation route providing unit may be performed using, or without, a generation AI. For example, the evacuation route providing unit can input the user's movement speed and physical strength data into the generation AI and have the generation AI adjust the route.

[0056] When providing an evacuation route, the evacuation route providing unit can select an optimal route by taking into account the user's geographical location information. For example, if the user is in an urban area, the evacuation route providing unit can provide a route that takes into account the building's evacuation route. Furthermore, if the user is in a mountainous area, the evacuation route providing unit can also provide a route that takes into account the risk of landslides. Furthermore, if the user is on the coast, the evacuation route providing unit can also provide a route that takes into account the risk of tsunamis. This allows the evacuation route providing unit to provide an appropriate route based on the geographical location information. Some or all of the above-described processing in the evacuation route providing unit may be performed using, or without, a generation AI. For example, the evacuation route providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select an optimal route.

[0057] When providing an evacuation route, the evacuation route providing unit can suggest an optimal route by referring to the user's past evacuation history. The evacuation route providing unit can suggest an optimal route, for example, based on evacuation routes used by the user in the past. The evacuation route providing unit can also suggest a route that avoids congestion based on the user's past evacuation history. Furthermore, the evacuation route providing unit can analyze the user's past evacuation history and suggest the most efficient route. This allows the evacuation route providing unit to provide an optimal route by utilizing the past evacuation history. Some or all of the above-mentioned processing in the evacuation route providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evacuation route providing unit can input the user's past evacuation history data into the generation AI and have the generation AI suggest a route.

[0058] When providing an evacuation route, the evacuation route providing unit can analyze the user's social media activities and provide related information. The evacuation route providing unit can suggest an evacuation route based on, for example, the location where the user checked in on social media. The evacuation route providing unit can also analyze the content of the user's social media posts and provide related evacuation information. Furthermore, the evacuation route providing unit can provide related evacuation information by referring to the activities of the user's friends on social media. In this way, the evacuation route providing unit can provide related information by utilizing social media activities. Some or all of the above-described processing in the evacuation route providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evacuation route providing unit can input the user's social media data into the generation AI and cause the generation AI to provide related information.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The emergency support system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires the user's vital signs, such as heart rate, blood pressure, and oxygen saturation, in real time and transmits this data to the generation unit. The generation unit can generate evacuation routes and instructions based on the user's physical condition based on this health data. For example, if the user's heart rate is high, the generation unit can provide an evacuation route that includes rest points. Furthermore, if the user's oxygen saturation is low, the generation unit can prioritize guidance to evacuation shelters that can provide oxygen. Furthermore, if the user's blood pressure is abnormally high, the generation unit can also suggest evacuation shelters where medical support is available. This allows the emergency support system to provide appropriate evacuation support based on the user's health condition.

[0061] The emergency support system may further include a distance measurement unit that measures the distance between the user and other surrounding users based on the user's location information. The distance measurement unit measures the distance between users in real time and transmits the data to the generation unit. The generation unit may generate an evacuation route to avoid collisions between users based on this distance data. For example, if an evacuation route is congested, the generation unit may suggest an alternative route to maintain distance between users. The generation unit may also provide a route that passes through a wide open space to avoid areas where users are crowded together. Furthermore, if users are concentrated in a specific area, the generation unit may adjust the route to avoid that area. This allows the emergency support system to avoid collisions between users and support safe evacuation.

[0062] The emergency support system can further analyze the user's past evacuation history and select the optimal information generation method. The generation unit generates optimal information based on, for example, evacuation routes used by the user in the past. The generation unit can also generate information to avoid congestion from the user's past evacuation history. The generation unit can also analyze the user's past evacuation history and generate the most efficient information. This allows the generation unit to provide optimal information by utilizing the past evacuation history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past evacuation history data into the generation AI and have the generation AI select an information generation method.

[0063] The emergency support system can further select the optimal display method based on the user's device information. For example, if the user is using a smartphone, the navigation unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can also provide a simple and highly visible display method. This allows the navigation unit to provide the optimal display method according to the user's device. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's device information into the generation AI and have the generation AI select the display method.

[0064] The emergency support system can further analyze the user's social media activity to provide relevant information. The guidance unit, for example, suggests an evacuation route based on the location where the user checked in on social media. The guidance unit can also analyze the content of the user's social media posts to provide relevant evacuation information. Furthermore, the guidance unit can provide relevant evacuation information by referring to the activities of the user's friends on social media. In this way, the guidance unit can utilize social media activity to provide relevant information. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input the user's social media data into the generation AI and have the generation AI provide relevant information.

[0065] The emergency support system can further select the optimal guidance method based on the user's geographical location information. For example, if the user is in an urban area, the guidance unit provides guidance taking into account the building's evacuation route. Furthermore, if the user is in a mountainous area, the guidance unit can provide guidance taking into account the risk of landslides. Furthermore, if the user is on the coast, the guidance unit can provide guidance taking into account the risk of tsunamis. This allows the guidance unit to provide appropriate guidance based on the geographical location information. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, a generation AI. For example, the guidance unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal guidance method.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The generator generates information corresponding to emergency scenarios. The generator generates information corresponding to emergency situations such as fires and earthquakes, and can use the generation AI to generate evacuation information according to the emergency. For example, it generates information indicating evacuation routes in the event of a fire, or information indicating safe evacuation locations in the event of an earthquake. Step 2: The navigation unit navigates based on the information generated by the generation unit. The navigation unit displays evacuation routes on the AR glasses' display and provides audio guidance. It can also use the generation AI to provide specific instructions to the user. For example, it can provide specific instructions such as "Turn right." Step 3: The guidance unit guides the user based on the information provided by the navigation unit. The guidance unit uses the user's vision and hearing to confirm the evacuation route. It can also use generative AI to provide visual and auditory instructions. For example, it can provide specific instructions such as "Turn left at the next intersection." Step 4: The evacuation route provider is activated in the event of an emergency and provides the optimal evacuation route based on the footage from the drone. The drone sends footage of the scene in real time to the generation AI, which then provides the optimal evacuation route taking into account the terrain and obstacles. The drone's footage can also be analyzed to adjust the evacuation route. For example, it can check the status of building collapses or the progression of a fire and adjust the evacuation route accordingly.

[0068] (Example 2) An emergency support system according to an embodiment of the present invention generates information corresponding to an emergency scenario and guides a user to a safe evacuation shelter by combining navigation via AR glasses and audio guidance. The emergency support system generates information corresponding to an emergency scenario and guides a user to a safe evacuation shelter by combining navigation via AR glasses and audio guidance. Furthermore, when an emergency occurs, a drone is activated, and a generation AI provides an optimal evacuation route based on the image captured by the drone, taking into account the terrain and obstacles. For example, when an emergency such as a fire or earthquake occurs, the emergency support system generates evacuation information tailored to the situation. This information includes instructions for the user to evacuate quickly and safely. The emergency support system then displays an evacuation route on the AR glasses' display and provides specific instructions to the user via audio guidance. This allows the user to confirm the evacuation route using both vision and hearing. Furthermore, when an emergency occurs, a drone is activated, and the drone transmits video of the scene in real time to the generation AI. The generation AI analyzes the video and provides an optimal evacuation route that takes into account the terrain and obstacles. For example, the drone monitors the progress of a building collapse or fire and adjusts the evacuation route accordingly. This allows the emergency support system to enable users to evacuate quickly and safely. This allows the emergency support system to assist users in evacuating safely and quickly. For example, when a user faces an emergency, the system can provide comprehensive support by combining visual, auditory, and real-time information.

[0069] An emergency support system according to an embodiment includes a generation unit, a navigation unit, a guidance unit, and an evacuation route provision unit. The generation unit generates information corresponding to an emergency scenario. The generation unit generates information corresponding to an emergency, such as a fire or an earthquake. The generation unit can also generate evacuation information corresponding to the emergency using a generation AI. For example, the generation AI generates information indicating an evacuation route in the event of a fire. The generation unit can also generate information indicating a safe evacuation location in the event of an earthquake. The navigation unit performs navigation based on the information generated by the generation unit. For example, the navigation unit displays an evacuation route on the display of the AR glasses and provides audio guidance. The navigation unit can also provide specific instructions to the user using the generation AI. For example, the navigation unit displays an evacuation route on the display of the AR glasses and provides specific instructions to the user via audio guidance, such as "Turn right." The guidance unit guides the user based on the information provided by the navigation unit. For example, the guidance unit allows the user to confirm the evacuation route using the user's vision and hearing. The guidance unit can also provide visual and audio instructions to the user using the generation AI. For example, the guidance unit displays an evacuation route on the display of the AR glasses, and an audio guide provides the user with specific instructions such as "Turn left at the next intersection." The evacuation route providing unit is activated when an emergency occurs and provides an optimal evacuation route based on video footage from a drone. For example, the drone transmits video footage of the scene to a generation AI in real time, and the generation AI provides an optimal evacuation route taking into account the terrain and obstacles. The evacuation route providing unit can also use the generation AI to analyze video footage from the drone and adjust the evacuation route. For example, the evacuation route providing unit checks the progress of a building collapse or a fire using the drone, and adjusts the evacuation route based on the results. This allows the emergency support system according to the embodiment to support a user's quick and safe evacuation. Some or all of the above-described processing in the evacuation route providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI.For example, the evacuation route provision unit can input footage from a drone into the generation AI and have the generation AI generate an evacuation route that takes into account the terrain and obstacles.

[0070] The generation unit can generate information corresponding to a fire or earthquake emergency. For example, the generation unit generates information indicating an evacuation route in the event of a fire. The generation unit can also generate information indicating a safe evacuation location in the event of an earthquake. For example, the generation unit generates an optimal evacuation route taking into account the progression of the fire. The generation unit can also generate a safe evacuation location taking into account the seismic intensity and damage status of the earthquake. This allows the generation unit to provide appropriate information according to the emergency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input fire or earthquake data into the generation AI and cause the generation AI to generate evacuation information.

[0071] The navigation unit can display an evacuation route on the display of the AR glasses and provide audio guidance. The navigation unit, for example, displays the evacuation route on the display of the AR glasses. The navigation unit can also provide audio guidance. For example, the navigation unit displays the evacuation route on the display of the AR glasses, and the audio guidance provides specific instructions to the user, such as "Turn right." The navigation unit can also provide visual and audio instructions to the user using a generation AI. For example, the navigation unit displays the evacuation route on the display of the AR glasses, and the audio guidance provides specific instructions to the user, such as "Turn left at the next intersection." This allows the navigation unit to guide the user using both vision and hearing. Some or all of the above-described processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can cause the generation AI to display the evacuation route and provide audio guidance.

[0072] The guidance unit can allow the user to confirm an evacuation route using both their vision and hearing. For example, the guidance unit displays the evacuation route on the display of the AR glasses to the user's vision. The guidance unit can also provide audio guidance to the user's hearing. For example, the guidance unit displays the evacuation route on the display of the AR glasses, and audio guidance provides specific instructions to the user, such as "Turn right." The guidance unit can also provide visual and audio instructions to the user using a generation AI. For example, the guidance unit displays the evacuation route on the display of the AR glasses, and audio guidance provides specific instructions to the user, such as "Turn left at the next intersection." This allows the guidance unit to allow the user to confirm an evacuation route using both their vision and hearing. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can cause the generation AI to provide visual and audio instructions.

[0073] The evacuation route providing unit can analyze video from a drone and provide an appropriate evacuation route that takes into account terrain and obstacles. For example, the drone transmits video of the scene to the generation AI in real time, and the generation AI provides an optimal evacuation route that takes into account terrain and obstacles. The evacuation route providing unit can also use the generation AI to analyze video from the drone and adjust the evacuation route. For example, the drone checks the status of building collapse or the progression of a fire and adjusts the evacuation route based on that. This allows the evacuation route providing unit to provide an optimal evacuation route based on real-time video analysis. Some or all of the above-described processing in the evacuation route providing unit may be performed using, or without, the generation AI. For example, the evacuation route providing unit can input video from a drone to the generation AI and cause the generation AI to generate an evacuation route that takes into account terrain and obstacles.

[0074] The evacuation route providing unit can use a drone to check the collapsed state of a building or the progression of a fire and adjust the evacuation route based on that. For example, the evacuation route providing unit can use a drone to check the collapsed state of a building and adjust the evacuation route based on that. The evacuation route providing unit can also use a drone to check the progression of a fire and adjust the evacuation route based on that. For example, the evacuation route providing unit can use a drone to check the collapsed state of a building and adjust the evacuation route based on that. The evacuation route providing unit can also use a drone to check the progression of a fire and adjust the evacuation route based on that. This allows the evacuation route providing unit to adjust the evacuation route according to the real-time situation. Some or all of the above-mentioned processing in the evacuation route providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evacuation route providing unit can input video from a drone to the generation AI and cause the generation AI to adjust the evacuation route taking into account the collapsed state of a building or the progression of a fire.

[0075] The generation unit can estimate the user's emotions and adjust the information generation for emergency scenarios based on the estimated user emotions. For example, if the user is in a panic, the generation AI can generate information including concise and clear instructions. If the user is calm, the generation unit can also generate information including detailed evacuation routes and additional safety information. Furthermore, if the user is feeling anxious, the generation unit can generate information including reassuring language and tone. This allows the generation unit to provide appropriate information according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the information generation based on the emotion.

[0076] The generation unit can apply different information generation algorithms depending on the type of emergency. For example, in the case of a fire, the generation AI generates an evacuation route that takes into account the spread of smoke and the progression of the fire. In addition, in the case of an earthquake, the generation AI can generate an evacuation route that takes into account the risk of building collapse. Furthermore, in the case of a flood, the generation AI can generate an evacuation route that takes into account rising water levels. This allows the generation unit to provide appropriate information depending on the type of emergency. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input data depending on the type of emergency into the generation AI and cause the generation AI to apply an information generation algorithm.

[0077] The generation unit can improve the accuracy of information generation by referring to past emergency situation data. The generation unit, for example, optimizes evacuation routes in the event of a fire based on past fire data. The generation unit can also optimize evacuation routes in the event of an earthquake based on past earthquake data. Furthermore, the generation unit can also optimize evacuation routes in the event of a flood based on past flood data. This allows the generation unit to improve the accuracy of information generation by utilizing past data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past emergency situation data into the generation AI and cause the generation AI to improve the accuracy of information generation.

[0078] The generation unit can update information in real time according to the progress of the emergency situation. The generation unit updates evacuation routes in real time according to the progress of a fire, for example. The generation unit can also update evacuation routes in real time based on aftershock information of an earthquake. Furthermore, the generation unit can also update evacuation routes in real time according to rising water levels of a flood. This allows the generation unit to respond to the latest situation by updating information in real time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the progress of the emergency situation to the generation AI and cause the generation AI to perform real-time updates of the information.

[0079] The generation unit can estimate the user's emotions and determine the priority of information to be generated based on the estimated user emotions. For example, if the user is in a panic, the generation unit can prioritize providing the most important evacuation information. Furthermore, if the user is calm, the generation unit can prioritize providing detailed evacuation information. Furthermore, if the user is feeling anxious, the generation unit can prioritize providing information that gives a sense of security. This allows the generation unit to determine the priority of information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0080] The generation unit can generate region-specific information based on the location of the emergency. For example, in the case of a fire in an urban area, the generation unit generates information that takes into account evacuation routes for buildings. In addition, in the case of an earthquake in a mountainous area, the generation unit can generate information that takes into account the risk of landslides. Furthermore, in the case of a tsunami along the coast, the generation unit can generate information that takes into account evacuation routes to higher ground. In this way, the generation unit can provide more appropriate evacuation information by providing region-specific information. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the location of the emergency into the generation AI and cause the generation AI to generate region-specific information.

[0081] The generation unit can analyze the user's past evacuation history and select the optimal information generation method. The generation unit can generate optimal information based on, for example, evacuation routes used by the user in the past. The generation unit can also generate information to avoid congestion from the user's past evacuation history. Furthermore, the generation unit can analyze the user's past evacuation history and generate the most efficient information. This allows the generation unit to provide optimal information by utilizing the past evacuation history. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past evacuation history data into the generation AI and cause the generation AI to select an information generation method.

[0082] The generation unit can adjust the content of the information generation depending on the time period during which the emergency occurs. For example, in the case of a fire at night, the generation unit generates highly visible information. In addition, in the case of an earthquake during the daytime, the generation unit can also generate information that takes into account the surrounding conditions. Furthermore, in the case of a flood in the early morning, the generation unit can generate information that takes into account the opening status of evacuation shelters. This allows the generation unit to provide appropriate information depending on the time period during which the emergency occurs. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the time period during which the emergency occurs into the generation AI and cause the generation AI to adjust the content of the information generation.

[0083] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated user's emotions. For example, when the user is nervous, the navigation unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the navigation unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the navigation unit can provide a display method that focuses on the main points. This allows the navigation unit to provide a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the navigation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0084] The navigation unit can update the user's current location information in real time during navigation. For example, the navigation unit updates the user's current location in real time while the user is moving and performs navigation. The navigation unit can also update the current location in real time as the user approaches the destination and suggest an optimal route. Furthermore, if the user gets lost, the navigation unit can update the current location in real time and perform navigation again. This allows the navigation unit to provide accurate navigation by updating the current location information in real time. Some or all of the above-described processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's current location information to the generation AI and have the generation AI perform real-time updates.

[0085] The navigation unit can optimize the route during navigation, taking into account the congestion status of the evacuation route. For example, the navigation unit can optimize the route based on real-time congestion information to avoid congested evacuation routes. The navigation unit can also optimize the route based on past congestion data to avoid routes that are expected to be congested. Furthermore, the navigation unit can also optimize the route based on real-time congestion information to preferentially propose routes where congestion has been alleviated. This allows the navigation unit to provide the optimal route taking into account the congestion status. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input congestion information into the generation AI and cause the generation AI to optimize the route.

[0086] During navigation, the navigation unit can adjust the timing of guidance according to the user's moving speed. For example, if the user is walking fast, the navigation unit can advance the timing of guidance. Also, if the user is walking slowly, the navigation unit can delay the timing of guidance. Furthermore, the navigation unit can pause guidance when the user stops and resume when the user starts walking again. This allows the navigation unit to provide appropriate guidance according to the user's moving speed. Some or all of the above-mentioned processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input user's moving speed data into the generation AI and have the generation AI adjust the timing of guidance.

[0087] The navigation unit can estimate the user's emotions and adjust the tone of the navigation voice guidance based on the estimated user emotions. For example, if the user is nervous, the navigation unit can provide guidance in a calm voice. Furthermore, if the user is relaxed, the navigation unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the navigation unit can provide quick and concise voice guidance. This allows the navigation unit to provide voice guidance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI adjust the tone of the voice guidance.

[0088] During navigation, the navigation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can also provide a display method that is simple and highly visible. This allows the navigation unit to provide the optimal display method according to the user's device. Some or all of the above-mentioned processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input the user's device information into the generation AI and have the generation AI select the display method.

[0089] During navigation, the navigation unit can suggest an optimal route by referring to the user's past evacuation history. For example, the navigation unit can suggest an optimal route based on evacuation routes used by the user in the past. The navigation unit can also suggest a route that avoids crowded areas based on the user's past evacuation history. Furthermore, the navigation unit can analyze the user's past evacuation history and suggest the most efficient route. This allows the navigation unit to provide an optimal route by utilizing the past evacuation history. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's past evacuation history data into the generation AI and have the generation AI suggest a route.

[0090] The navigation unit can provide a multilingual guide during navigation according to the user's language setting. The navigation unit, for example, automatically sets the navigation language based on the language setting of the user's device. The navigation unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the navigation unit can provide navigation in that language. By providing a multilingual guide, the navigation unit can thereby provide information that is easy for the user to understand. Some or all of the above-described processing in the navigation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input the user's language setting data into the generation AI and cause the generation AI to provide a multilingual guide.

[0091] The guidance unit can estimate the user's emotions and adjust the guidance method based on the estimated user emotions. For example, if the user is in a panicked state, the guidance unit can provide concise and clear instructions. Furthermore, if the user is calm, the guidance unit can provide detailed instructions. Furthermore, if the user is feeling anxious, the guidance unit can provide instructions in a reassuring language and tone. This allows the guidance unit to provide an appropriate guidance method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guidance unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the guidance unit can input the user's emotion data into the generation AI and have the generation AI adjust the guidance method.

[0092] The guidance unit can provide information to the user using both their vision and hearing when guiding them. For example, the guidance unit displays an evacuation route on the display of the AR glasses to the user's vision. The guidance unit can also guide the user about the evacuation route using audio guidance to the user's hearing. Furthermore, the guidance unit can provide more effective guidance by combining the user's vision and hearing. This allows the guidance unit to provide effective guidance using both vision and hearing. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can cause the generation AI to provide visual and auditory information.

[0093] The guidance unit can customize the content of the guidance according to the user's current situation when guiding. For example, if the user is inside a building, the guidance unit provides guidance that takes into account the building's structure. Furthermore, if the user is outdoors, the guidance unit can also provide guidance that takes into account the surrounding terrain. Furthermore, if the user is using a wheelchair, the guidance unit can also guide the user along a barrier-free route. This allows the guidance unit to provide appropriate guidance according to the user's situation. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input the user's current situation data into the generation AI and have the generation AI customize the content of the guidance.

[0094] The guidance unit can improve the guidance method by reflecting user feedback during guidance. For example, the guidance unit improves the next guidance method based on feedback provided by the user. The guidance unit can also reflect user feedback in real time and adjust the current guidance method. Furthermore, the guidance unit can analyze user feedback and make improvements to solve common problems. In this way, the guidance unit can improve the guidance method by utilizing user feedback. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input user feedback data into the generation AI and have the generation AI improve the guidance method.

[0095] The guidance unit can estimate the user's emotions and determine the priority of guidance based on the estimated user emotions. For example, if the user is in a panic, the guidance unit can prioritize providing the most important evacuation information. Furthermore, if the user is calm, the guidance unit can prioritize providing detailed evacuation information. Furthermore, if the user is feeling anxious, the guidance unit can prioritize providing information that gives a sense of security. This allows the guidance unit to determine priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0096] The guidance unit can select the optimal guidance method by taking into account the user's geographical location information when guiding. For example, if the user is in an urban area, the guidance unit can provide guidance that takes into account the building's evacuation route. Furthermore, if the user is in a mountainous area, the guidance unit can provide guidance that takes into account the risk of landslides. Furthermore, if the user is on the coast, the guidance unit can provide guidance that takes into account the risk of tsunamis. This allows the guidance unit to provide appropriate guidance based on the geographical location information. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the guidance unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal guidance method.

[0097] The guidance unit can analyze the user's social media activities and provide relevant information when guiding the user. For example, the guidance unit can suggest an evacuation route based on the location where the user checked in on social media. The guidance unit can also analyze the content of the user's social media posts and provide relevant evacuation information. Furthermore, the guidance unit can provide relevant evacuation information by referring to the activities of the user's friends on social media. This allows the guidance unit to provide relevant information by utilizing social media activities. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input the user's social media data into the generation AI and have the generation AI provide the relevant information.

[0098] The guidance unit can customize the guidance method by reflecting the user's past feedback during guidance. For example, the guidance unit improves the next guidance method based on feedback provided by the user in the past. The guidance unit can also adjust the current guidance method by reflecting the user's past feedback in real time. Furthermore, the guidance unit can analyze the user's past feedback and make improvements to solve common problems. This allows the guidance unit to customize the guidance method by utilizing the past feedback. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input the user's past feedback data into the generation AI and have the generation AI customize the guidance method.

[0099] The evacuation route providing unit can estimate the user's emotions and adjust the evacuation route provision method based on the estimated user's emotions. For example, if the user is in a panic, the evacuation route providing unit can provide a concise and clear evacuation route. Furthermore, if the user is calm, the evacuation route providing unit can also provide a detailed evacuation route. Furthermore, if the user is feeling anxious, the evacuation route providing unit can provide an evacuation route using language and a tone that gives a sense of security. This allows the evacuation route providing unit to provide an appropriate evacuation route according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evacuation route providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the evacuation route providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the provision method.

[0100] The evacuation route providing unit can analyze video from the drone in real time and update the evacuation route. For example, the evacuation route providing unit uses the drone to confirm the collapse status of a building and update the evacuation route based on that. The evacuation route providing unit can also use the drone to confirm the progress of a fire and update the evacuation route based on that. Furthermore, the evacuation route providing unit can also use the drone to confirm rising flood water levels and update the evacuation route based on that. This allows the evacuation route providing unit to update the evacuation route based on real-time video analysis. Some or all of the above-mentioned processing in the evacuation route providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evacuation route providing unit can input video from the drone to the generation AI and have the generation AI perform real-time analysis.

[0101] When providing an evacuation route, the evacuation route providing unit can optimize the route by taking into account changes in terrain and obstacles. The evacuation route providing unit can optimize the evacuation route by taking into account changes in terrain due to an earthquake, for example. The evacuation route providing unit can also optimize the evacuation route by taking into account the occurrence of obstacles due to a fire. Furthermore, the evacuation route providing unit can also optimize the evacuation route by taking into account changes in water levels due to a flood. In this way, the evacuation route providing unit can provide an optimal route by taking into account changes in terrain and obstacles. Some or all of the above-mentioned processing in the evacuation route providing unit may be performed using, or without, a generation AI, for example. For example, the evacuation route providing unit can input data on terrain and obstacles into the generation AI and cause the generation AI to optimize the route.

[0102] When providing an evacuation route, the evacuation route providing unit can adjust the route taking into account the user's movement speed and physical strength. For example, if the user can move quickly, the evacuation route providing unit provides the shortest route. Furthermore, if the user moves slowly, the evacuation route providing unit can also provide a route that includes rest points. Furthermore, the evacuation route providing unit can provide a reasonable route taking into account the user's physical strength. This allows the evacuation route providing unit to provide an appropriate route according to the user's movement speed and physical strength. Some or all of the above-mentioned processing in the evacuation route providing unit may be performed using, or without, a generation AI. For example, the evacuation route providing unit can input the user's movement speed and physical strength data into the generation AI and have the generation AI adjust the route.

[0103] The evacuation route providing unit can estimate the user's emotions and prioritize evacuation routes based on the estimated user's emotions. For example, when the user is in a panic, the evacuation route providing unit can prioritize providing the safest evacuation route. Furthermore, when the user is calm, the evacuation route providing unit can prioritize providing the shortest route. Furthermore, when the user is feeling anxious, the evacuation route providing unit can prioritize providing an evacuation route that provides a sense of security. This allows the evacuation route providing unit to determine priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evacuation route providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the evacuation route providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities.

[0104] When providing an evacuation route, the evacuation route providing unit can select an optimal route by taking into account the user's geographical location information. For example, if the user is in an urban area, the evacuation route providing unit can provide a route that takes into account the building's evacuation route. Furthermore, if the user is in a mountainous area, the evacuation route providing unit can also provide a route that takes into account the risk of landslides. Furthermore, if the user is on the coast, the evacuation route providing unit can also provide a route that takes into account the risk of tsunamis. This allows the evacuation route providing unit to provide an appropriate route based on the geographical location information. Some or all of the above-described processing in the evacuation route providing unit may be performed using, or without, a generation AI. For example, the evacuation route providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select an optimal route.

[0105] When providing an evacuation route, the evacuation route providing unit can suggest an optimal route by referring to the user's past evacuation history. The evacuation route providing unit can suggest an optimal route, for example, based on evacuation routes used by the user in the past. The evacuation route providing unit can also suggest a route that avoids congestion based on the user's past evacuation history. Furthermore, the evacuation route providing unit can analyze the user's past evacuation history and suggest the most efficient route. This allows the evacuation route providing unit to provide an optimal route by utilizing the past evacuation history. Some or all of the above-mentioned processing in the evacuation route providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evacuation route providing unit can input the user's past evacuation history data into the generation AI and have the generation AI suggest a route.

[0106] When providing an evacuation route, the evacuation route providing unit can analyze the user's social media activities and provide related information. The evacuation route providing unit can suggest an evacuation route based on, for example, the location where the user checked in on social media. The evacuation route providing unit can also analyze the content of the user's social media posts and provide related evacuation information. Furthermore, the evacuation route providing unit can provide related evacuation information by referring to the activities of the user's friends on social media. In this way, the evacuation route providing unit can provide related information by utilizing social media activities. Some or all of the above-described processing in the evacuation route providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evacuation route providing unit can input the user's social media data into the generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned generation unit, navigation unit, guidance unit, and evacuation route providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the navigation unit is realized by the control unit 46A of the smart device 14. For example, the guidance unit is realized by the control unit 46A of the smart device 14. For example, the evacuation route providing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the evacuation route providing unit can input video from a drone to the generation AI and cause the generation AI to generate an evacuation route that takes into account terrain and obstacles. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned generation unit, navigation unit, guidance unit, and evacuation route providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the navigation unit is realized by the control unit 46A of the smart glasses 214. For example, the guidance unit is realized by the control unit 46A of the smart glasses 214. For example, the evacuation route providing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the evacuation route providing unit can input video from a drone to the generation AI and cause the generation AI to generate an evacuation route that takes into account terrain and obstacles. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, navigation unit, guidance unit, and evacuation route providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the navigation unit is realized by the control unit 46A of the headset type terminal 314. For example, the guidance unit is realized by the control unit 46A of the headset type terminal 314. For example, the evacuation route providing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the evacuation route providing unit can input video from a drone to the generation AI and cause the generation AI to generate an evacuation route that takes into account terrain and obstacles. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, navigation unit, guidance unit, and evacuation route providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the navigation unit is realized by the control unit 46A of the robot 414. For example, the guidance unit is realized by the control unit 46A of the robot 414. For example, the evacuation route providing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the evacuation route providing unit can input video from a drone to the generation AI and cause the generation AI to generate an evacuation route that takes into account terrain and obstacles.

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

[0108] The emergency support system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires the user's vital signs, such as heart rate, blood pressure, and oxygen saturation, in real time and transmits this data to the generation unit. The generation unit can generate evacuation routes and instructions based on the user's physical condition based on this health data. For example, if the user's heart rate is high, the generation unit can provide an evacuation route that includes rest points. Furthermore, if the user's oxygen saturation is low, the generation unit can prioritize guidance to evacuation shelters that can provide oxygen. Furthermore, if the user's blood pressure is abnormally high, the generation unit can also suggest evacuation shelters where medical support is available. This allows the emergency support system to provide appropriate evacuation support based on the user's health condition.

[0109] The emergency support system may further include a distance measurement unit that measures the distance between the user and other surrounding users based on the user's location information. The distance measurement unit measures the distance between users in real time and transmits the data to the generation unit. The generation unit may generate an evacuation route to avoid collisions between users based on this distance data. For example, if an evacuation route is congested, the generation unit may suggest an alternative route to maintain distance between users. The generation unit may also provide a route that passes through a wide open space to avoid areas where users are crowded together. Furthermore, if users are concentrated in a specific area, the generation unit may adjust the route to avoid that area. This allows the emergency support system to avoid collisions between users and support safe evacuation.

[0110] The emergency support system can further estimate the user's emotions and adjust the display method of the evacuation route based on the estimated user's emotions. For example, if the user is nervous, the navigation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the navigation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the navigation unit can provide a display method that focuses on the main points. This allows the navigation unit to provide a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the navigation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0111] The emergency support system can further analyze the user's past evacuation history and select the optimal information generation method. The generation unit generates optimal information based on, for example, evacuation routes used by the user in the past. The generation unit can also generate information to avoid congestion from the user's past evacuation history. The generation unit can also analyze the user's past evacuation history and generate the most efficient information. This allows the generation unit to provide optimal information by utilizing the past evacuation history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past evacuation history data into the generation AI and have the generation AI select an information generation method.

[0112] The emergency support system can further estimate the user's emotions and prioritize evacuation routes based on the estimated user's emotions. For example, when the user is in a panic, the evacuation route providing unit can prioritize providing the safest evacuation route. Furthermore, when the user is calm, the evacuation route providing unit can prioritize providing the shortest route. Furthermore, when the user is feeling anxious, the evacuation route providing unit can prioritize providing an evacuation route that provides a sense of security. This allows the evacuation route providing unit to determine priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evacuation route providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the evacuation route providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities.

[0113] The emergency support system can further select the optimal display method based on the user's device information. For example, if the user is using a smartphone, the navigation unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can also provide a simple and highly visible display method. This allows the navigation unit to provide the optimal display method according to the user's device. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's device information into the generation AI and have the generation AI select the display method.

[0114] The emergency support system can further estimate the user's emotions and adjust the tone of the navigation voice guidance based on the estimated user emotions. For example, if the user is nervous, the navigation unit can provide guidance in a calm voice. Furthermore, if the user is relaxed, the navigation unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the navigation unit can provide quick and concise voice guidance. This allows the navigation unit to provide voice guidance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the navigation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI adjust the tone of the voice guidance.

[0115] The emergency support system can further analyze the user's social media activity to provide relevant information. The guidance unit, for example, suggests an evacuation route based on the location where the user checked in on social media. The guidance unit can also analyze the content of the user's social media posts to provide relevant evacuation information. Furthermore, the guidance unit can provide relevant evacuation information by referring to the activities of the user's friends on social media. In this way, the guidance unit can utilize social media activity to provide relevant information. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input the user's social media data into the generation AI and have the generation AI provide relevant information.

[0116] The emergency support system can further estimate the user's emotions and adjust the guidance method based on the estimated user emotions. For example, if the user is in a panicked state, the guidance unit can provide concise and clear instructions. Furthermore, if the user is calm, the guidance unit can provide detailed instructions. Furthermore, if the user is feeling anxious, the guidance unit can provide instructions in a reassuring language and tone. This allows the guidance unit to provide an appropriate guidance method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guidance unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the guidance unit can input the user's emotion data into the generation AI and have the generation AI adjust the guidance method.

[0117] The emergency support system can further select the optimal guidance method based on the user's geographical location information. For example, if the user is in an urban area, the guidance unit provides guidance taking into account the building's evacuation route. Furthermore, if the user is in a mountainous area, the guidance unit can provide guidance taking into account the risk of landslides. Furthermore, if the user is on the coast, the guidance unit can provide guidance taking into account the risk of tsunamis. This allows the guidance unit to provide appropriate guidance based on the geographical location information. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, a generation AI. For example, the guidance unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal guidance method.

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

[0119] Step 1: The generator generates information corresponding to emergency scenarios. The generator generates information corresponding to emergency situations such as fires and earthquakes, and can use the generation AI to generate evacuation information according to the emergency. For example, it generates information indicating evacuation routes in the event of a fire, or information indicating safe evacuation locations in the event of an earthquake. Step 2: The navigation unit navigates based on the information generated by the generation unit. The navigation unit displays evacuation routes on the AR glasses' display and provides audio guidance. It can also use the generation AI to provide specific instructions to the user. For example, it can provide specific instructions such as "Turn right." Step 3: The guidance unit guides the user based on the information provided by the navigation unit. The guidance unit uses the user's vision and hearing to confirm the evacuation route. It can also use generative AI to provide visual and auditory instructions. For example, it can provide specific instructions such as "Turn left at the next intersection." Step 4: The evacuation route provider is activated in the event of an emergency and provides the optimal evacuation route based on the footage from the drone. The drone sends footage of the scene in real time to the generation AI, which then provides the optimal evacuation route taking into account the terrain and obstacles. The drone's footage can also be analyzed to adjust the evacuation route. For example, it can check the status of building collapses or the progression of a fire and adjust the evacuation route accordingly.

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

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

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

[0191] [Explanation of symbols]

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

Claims

1. a generating unit that generates information corresponding to an emergency scenario; a navigation unit that performs navigation based on the information generated by the generation unit; a guidance unit that guides a user based on information provided by the navigation unit; and an evacuation route providing unit that is activated in the event of an emergency and provides an appropriate evacuation route based on images from the drone. A system characterized by:

2. The generation unit Generate information to respond to fire or earthquake emergencies 2. The system of claim 1.

3. The navigation unit Evacuation routes are displayed on the AR glasses and audio guidance is provided.

2. The system of claim 1.

4. The induction section is Use the user's sight and hearing to confirm evacuation routes 2. The system of claim 1.

5. The evacuation route providing unit Analyzing images from drones and providing appropriate evacuation routes that take into account terrain and obstacles 2. The system of claim 1.

6. The evacuation route providing unit Drones can monitor building collapses or fire progression and adjust evacuation routes accordingly.

2. The system of claim 1.

7. The generation unit Estimating user emotions and adjusting information generation for emergency scenarios based on the estimated user emotions 2. The system of claim 1.

8. The generation unit Apply different information generation algorithms depending on the type of emergency.

2. The system of claim 1.

Images