system

Abstract

The system according to this embodiment aims to quickly grasp the situation during a disaster and provide users with optimal action instructions and information. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a identification unit, and a provision unit. The acquisition unit acquires video footage during a disaster. The analysis unit analyzes the video footage acquired by the acquisition unit. The identification unit identifies the user's location information based on the information analyzed by the analysis unit. The provision unit provides optimal action instructions and information based on the location information identified by the identification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to quickly grasp the situation and give appropriate action instructions during a disaster.

[0005] The system according to the embodiment aims to quickly grasp the situation during a disaster and provide the user with optimal action instructions and information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a identification unit, and a provision unit. The acquisition unit acquires video footage during a disaster. The analysis unit analyzes the video footage acquired by the acquisition unit. The identification unit identifies the user's location information based on the information analyzed by the analysis unit. The provision unit provides optimal action instructions and information based on the location information identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly grasp the situation during a disaster and provide users with optimal action instructions and information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The disaster response system according to an embodiment of the present invention is a system that quickly grasps the situation during a disaster and provides users with optimal action instructions and information. This disaster response system utilizes images from a "small SAR satellite" launched by QPS Research Institute to quickly grasp the situation during a disaster, and an AI agent issues instructions and shares information to the user's location via their personal mobile device. Specifically, it consists of the following steps. First, QPS Research Institute's "small SAR satellite" acquires images of the disaster. Next, a generating AI agent analyzes the images to grasp the extent of the damage caused by the disaster. Furthermore, based on personal data held by the SoftBank Group, the generating AI agent identifies the location information of individual users and provides optimal action instructions and information tailored to their location. This mechanism enables a rapid response during a disaster and minimizes damage. First, QPS Research Institute's "small SAR satellite" acquires images of the disaster. This satellite provides a "near real-time data provision service" that can observe specific areas at average intervals of 10 minutes, allowing for a rapid grasp of the damage situation when a disaster occurs. For example, in the event of a disaster such as an earthquake or flood, the satellite acquires images of the area and transmits them to the generating AI agent. Next, the generating AI agent analyzes the acquired video footage. Having learned from past disaster data and insights, the generating AI agent can quickly grasp the extent of damage from the video. For example, it analyzes building collapses and flooding areas to identify what kind of response is needed in which areas. Furthermore, based on personal data held by the SoftBank Group, the generating AI agent identifies the location of individual users. This allows for the provision of optimal action instructions and information tailored to the user's location. For example, it can provide guidance on evacuation routes and emergency contact information to help users respond quickly. This system enables a rapid response during disasters, minimizing damage. Because pinpoint information is provided to local governments and individuals, evacuation and rescue operations can be carried out efficiently. Additionally, because the generating AI agent has learned from past disaster data and insights, it can provide more accurate information. For example, it can provide optimal action instructions based on effective evacuation methods and countermeasures from past disasters.This allows the disaster response system to quickly grasp the situation during a disaster and provide users with optimal action instructions and information.

[0029] The disaster response system according to this embodiment comprises an acquisition unit, an analysis unit, an identification unit, and a provision unit. The acquisition unit acquires video footage during a disaster. The acquisition unit can acquire video footage during a disaster using, for example, QPS Research Institute's "small SAR satellite." The acquisition unit observes a specific area at an average interval of 10 minutes and can quickly grasp the extent of damage when a disaster occurs. For example, when a disaster such as an earthquake or flood occurs, the acquisition unit acquires video footage of that area and transmits it to a generating AI agent. The analysis unit analyzes the video footage acquired by the acquisition unit. The analysis unit uses a generating AI to grasp the extent of disaster damage from the video footage. For example, the analysis unit analyzes the extent of building collapse and flooding to identify what kind of response is needed in which area. The analysis unit has learned from past disaster data and knowledge, and can quickly grasp the extent of damage from the video footage. The identification unit identifies the user's location information based on the information analyzed by the analysis unit. The identification unit can identify the location information of individual users based on personal data held by the SoftBank Group. This allows the identification unit to provide optimal action instructions and information tailored to the user's location. The provision unit provides optimal action instructions and information based on location information identified by the identification unit. The provision unit assists users in responding quickly, for example, by providing guidance on evacuation routes and emergency contact information. The provision unit learns from past disaster data and knowledge, enabling it to provide more accurate information. For example, it can provide optimal action instructions based on evacuation methods and countermeasures that were effective in past disasters. As a result, the disaster response system according to this embodiment can quickly grasp the situation during a disaster and provide users with optimal action instructions and information.

[0030] The acquisition unit acquires images during disasters. For example, the acquisition unit can acquire images during disasters using QPS Research Institute's "small SAR satellite." The small SAR satellite uses synthetic aperture radar technology and can acquire high-resolution images regardless of weather or time of day. This allows the acquisition unit to acquire detailed images of disaster areas day or night, and even in bad weather. The acquisition unit observes specific areas at average intervals of 10 minutes, enabling it to quickly grasp the extent of damage when a disaster occurs. For example, when a disaster such as an earthquake or flood occurs, the acquisition unit acquires images of the area and transmits them to the generating AI agent. The acquisition unit continuously observes from immediately after the disaster occurs, enabling it to grasp the progress and changes of damage in real time. This allows the acquisition unit to record the entire process from the initial stages of the disaster to recovery in detail and provide necessary data to the analysis unit and the specific unit. Furthermore, by coordinating multiple small SAR satellites, the acquisition unit can simultaneously observe a wide range of disaster areas. This enables the acquisition unit to respond quickly and accurately to large-scale disasters. The acquisition unit transmits the acquired image data to the analysis unit in real time to support a rapid response. This allows the data acquisition unit to quickly grasp the situation during a disaster and play a crucial role in supporting the efficient operation of the entire system.

[0031] The analysis unit analyzes the video footage acquired by the acquisition unit. The analysis unit uses generative AI to understand the extent of disaster damage from the video. The generative AI utilizes deep learning technology and has learned from vast amounts of past disaster data and images. This allows the analysis unit to analyze building collapses, flooding extents, and other factors with high accuracy. For example, the analysis unit identifies collapsed building locations from the acquired video and evaluates the degree of collapse and the affected area. In the case of flooding, it analyzes the flooded area to understand which areas have suffered the most damage. Using the generative AI's image recognition technology, the analysis unit quickly analyzes the damage and identifies what kind of response is needed in which areas. Because the analysis unit has learned from past disaster data and knowledge, it can quickly grasp the extent of damage from the video. Furthermore, the analysis unit analyzes the acquired video data chronologically, tracking the progress and changes of the disaster in real time. This allows the analysis unit to understand the entire process from the initial stages of the disaster to recovery in detail and provide necessary information to the identification and provision units. The analysis unit plays a crucial role in supporting the efficient operation of the entire system by utilizing the advanced analytical capabilities of the generation AI to quickly and accurately grasp the situation during a disaster.

[0032] The Identification Unit identifies the user's location based on information analyzed by the Analysis Unit. The Identification Unit can identify the location of individual users based on personal data held by the SoftBank Group. The Identification Unit acquires the location information of users' smartphones and mobile phones in real time, accurately determining their location. This allows the Identification Unit to provide optimal action instructions and information tailored to the user's location. The Identification Unit integrates the acquired location information with the Analysis Unit's damage data to identify the specific risks the user is facing. For example, if a user is in a flooded area, the Identification Unit identifies the flooding situation and evacuation routes in that area and provides appropriate action instructions. Furthermore, based on the user's location information, the Identification Unit can identify the locations of evacuation shelters and support facilities during disasters and guide users to the most suitable evacuation destination. The Identification Unit updates the user's location information in real time and takes appropriate action according to the progress of the disaster. This allows the Identification Unit to support users in evacuating quickly and safely. In addition, the Identification Unit manages user location information anonymly, protecting privacy while providing information necessary for disaster response. This means that the specific unit plays a crucial role in ensuring user safety and supporting the efficient operation of the entire system.

[0033] The service provider provides optimal action instructions and information based on location information identified by the identification unit. The service provider assists users in responding quickly, for example, by providing evacuation route guidance and emergency contact information. The service provider notifies users of evacuation routes and emergency contact information in real time via their smartphones or mobile phones. For example, the service provider guides users to the optimal evacuation route based on their location and alerts them through voice guidance and vibration notifications. The service provider also provides users with information on emergency contacts and support facilities during disasters to support a rapid response. The service provider learns from past disaster data and knowledge, enabling it to provide more accurate information. For example, it can provide optimal action instructions based on effective evacuation methods and measures from past disasters. The service provider collects user feedback and can continuously improve the accuracy and effectiveness of the information it provides. For example, it can revise evacuation routes and improve instructions based on feedback from users who received evacuation instructions. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to quickly and reliably provide users with action instructions, minimizing the risk of disaster.

[0034] The analysis unit includes a learning unit that learns from past disaster data and knowledge. The analysis unit can learn from, for example, past disaster reports, research papers, and news articles, enabling it to quickly grasp the extent of disaster damage. The analysis unit can use generative AI to learn from past disaster data and knowledge, enabling it to quickly grasp the extent of damage from video. For example, the analysis unit can learn from past earthquake data and analyze the extent of building collapse caused by earthquakes. The analysis unit can also learn from past flood data and analyze the extent of flooding. Furthermore, the analysis unit can learn from past fire data and analyze the extent of damage caused by fires. As a result, the accuracy of the analysis unit's analysis improves by learning from past disaster data and knowledge. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input past disaster data into the generative AI, which can learn from the data and analyze the extent of disaster damage.

[0035] The service unit includes a guidance unit that provides evacuation route guidance. The service unit can guide users to the optimal evacuation route using, for example, real-time traffic information, map information, and voice guidance. The service unit uses generative AI to guide users to the optimal evacuation route based on the user's location information and the extent of disaster damage. For example, the service unit proposes the safest evacuation route from the user's current location. The service unit can also guide users to evacuation routes that avoid congestion based on real-time traffic information. Furthermore, the service unit can guide users to evacuation routes using voice guidance. As a result, the service unit enables users to evacuate quickly by providing evacuation route guidance. Some or all of the above processing in the service unit may be performed using, for example, generative AI, or without generative AI. For example, the service unit can input the user's location information and the extent of disaster damage into the generative AI, which can then propose the optimal evacuation route.

[0036] The service provider includes a contact unit that provides emergency contact information. The service provider can provide users with emergency contact information such as police, fire departments, hospitals, and family members. Using a generative AI, the service provider provides the most suitable emergency contact information based on the user's location and the extent of the disaster damage. For example, the service provider provides contact information for the nearest police station or fire station to the user's current location. The service provider can also provide contact information for the user's family. Furthermore, the service provider can provide emergency contact information to the user using voice guidance. This allows the service provider to enable the user to contact emergency contacts quickly. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's location and the extent of the disaster damage into the generative AI, which can then suggest the most suitable emergency contact information.

[0037] The acquisition unit observes a specific area at an average interval of 10 minutes. The acquisition unit can observe a specific area at an average interval of 10 minutes, for example, using QPS Research Institute's "small SAR satellite". By observing a specific area, the acquisition unit can quickly grasp the extent of damage in the event of a disaster. For example, when a disaster such as an earthquake or flood occurs, the acquisition unit acquires images of the area and transmits them to a generating AI agent. This allows the acquisition unit to quickly grasp the situation during a disaster by observing a specific area at an average interval of 10 minutes. Some or all of the above processing in the acquisition unit may be performed using a generating AI, for example, or without using a generating AI. For example, the acquisition unit can input images of a specific area into a generating AI, which can analyze the images and grasp the extent of the disaster damage.

[0038] The analysis unit analyzes the extent of building collapse and flooding. The analysis unit can analyze the extent of building collapse and flooding using, for example, image analysis technology. The analysis unit uses generative AI to analyze the extent of building collapse and flooding from disaster footage. For example, the analysis unit analyzes the extent of building collapse caused by an earthquake and identifies what kind of response is needed in which areas. The analysis unit can also analyze the extent of flooding caused by a flood and identify what kind of response is needed in which areas. In this way, the analysis unit can quickly grasp the extent of disaster damage by analyzing the extent of building collapse and flooding. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input disaster footage into generative AI, and the generative AI can analyze the extent of building collapse and flooding.

[0039] The information delivery unit dynamically changes the notification method according to the urgency of the information being delivered. For example, the delivery unit prioritizes voice notifications for highly urgent information. The delivery unit uses a generative AI to evaluate the urgency of the information and select the optimal notification method. For example, the delivery unit sends a text notification for moderately urgent information. The delivery unit can also send in-app notifications for less urgent information. In this way, the delivery unit can provide users with appropriate information by changing the notification method according to the urgency of the information. Some or all of the above processing in the delivery unit may be performed using a generative AI, or not. For example, the delivery unit can input the urgency of the information into the generative AI, which can then select the optimal notification method.

[0040] The service provider provides optimal action instructions based on the user's past behavioral history at the time of service provision. For example, the service provider may suggest the optimal evacuation route based on the user's past evacuation routes. The service provider uses generative AI to analyze the user's past behavioral history and provide optimal action instructions. For example, the service provider may select an evacuation shelter based on the user's past behavioral history. The service provider can also provide optimal action instructions based on the user's past behavioral history. This allows the service provider to provide more appropriate instructions by providing action instructions based on the user's past behavioral history. Some or all of the above processing in the service provider may be performed using generative AI, or without using generative AI. For example, the service provider can input the user's past behavioral history into the generative AI, which can then provide optimal action instructions.

[0041] The service provider selects the optimal display method according to the user's device information at the time of delivery. For example, the service provider provides a display method that matches the screen size to a user using a smartphone. The service provider analyzes the user's device information using a generation AI and selects the optimal display method. For example, the service provider provides a display method optimized for a large screen to a user using a tablet. The service provider can also provide a concise and highly visible display method to a user using a smartwatch. In this way, the service provider can provide highly visible information by selecting a display method according to the user's device information. Some or all of the above processing in the service provider may be performed using a generation AI, for example, or without a generation AI. For example, the service provider can input the user's device information into a generation AI, and the generation AI can select the optimal display method.

[0042] The service provider optimizes evacuation instructions by linking them with other users' behavior information when providing instructions. For example, the service provider provides optimal evacuation instructions by linking them with the evacuation status of other users. The service provider uses a generation AI to analyze other users' behavior information and optimize evacuation instructions. For example, the service provider optimizes evacuation instructions based on other users' behavior information. The service provider can also analyze other users' behavior information and optimize evacuation instructions. In this way, the service provider can optimize evacuation instructions by linking them with other users' behavior information. Some or all of the above processing in the service provider may be performed using a generation AI, for example, or without a generation AI. For example, the service provider can input other users' behavior information into a generation AI, which can then provide optimal evacuation instructions.

[0043] The learning unit optimizes its learning algorithm by referring to past disaster data during the learning process. For example, the learning unit optimizes its learning algorithm by referring to past earthquake data. The learning unit optimizes its learning algorithm by referring to past disaster data using a generative AI. For example, the learning unit optimizes its learning algorithm by referring to past flood data. The learning unit can also optimize its learning algorithm by referring to past fire data. This improves the accuracy of learning by optimizing the learning algorithm by referring to past disaster data. Some or all of the above processing in the learning unit may be performed using a generative AI, for example, or without a generative AI. For example, the learning unit can input past disaster data into a generative AI, which can then optimize the learning algorithm.

[0044] The learning unit weights the training data according to the type and scale of the disaster during training. For example, in the case of an earthquake, the learning unit weights the earthquake data. The learning unit uses a generative AI to weight the training data according to the type and scale of the disaster. For example, in the case of a flood, the learning unit weights the flood data. The learning unit can also weight the fire data in the case of a fire. This improves the accuracy of training by weighting the training data according to the type and scale of the disaster. Some or all of the above processing in the learning unit may be performed using a generative AI, for example, or without a generative AI. For example, the learning unit can input disaster data into a generative AI, and the generative AI can perform the data weighting.

[0045] The guidance unit provides the optimal evacuation route by considering real-time traffic information during guidance. For example, the guidance unit provides the optimal evacuation route based on real-time traffic congestion information. The guidance unit uses a generation AI to analyze real-time traffic information and provide the optimal evacuation route. For example, the guidance unit provides the optimal evacuation route by considering the real-time operation status of public transportation. The guidance unit can also provide detour routes based on real-time road construction information. In this way, the guidance unit can provide evacuation routes considering real-time traffic information, enabling more appropriate evacuations. Some or all of the above processing in the guidance unit may be performed using a generation AI, for example, or without a generation AI. For example, the guidance unit can input real-time traffic information into a generation AI, and the generation AI can provide the optimal evacuation route.

[0046] The guidance unit optimizes evacuation routes by coordinating with the evacuation status of other users during guidance. For example, the guidance unit provides the optimal evacuation route by coordinating with the evacuation status of other users. The guidance unit uses generative AI to analyze the evacuation status of other users and optimize evacuation routes. For example, the guidance unit optimizes evacuation routes based on the evacuation status of other users. The guidance unit can also analyze the evacuation status of other users and optimize evacuation routes. In this way, the guidance unit can optimize evacuation routes by coordinating with the evacuation status of other users. Some or all of the above processing in the guidance unit may be performed using generative AI, for example, or without using generative AI. For example, the guidance unit can input the evacuation status of other users into the generative AI, and the generative AI can provide the optimal evacuation route.

[0047] The contact department provides the most suitable contact based on the user's past contact history when contact is made. For example, the contact department provides the most suitable contact based on the emergency contacts the user has contacted in the past. The contact department uses generative AI to analyze the user's past contact history and provide the most suitable contact. For example, the contact department provides the most suitable contact from the user's past contact history. The contact department can also provide the most suitable contact based on the user's past contact history. This allows the contact department to make more appropriate contact by providing contact based on the user's past contact history. Some or all of the above processing in the contact department may be performed using generative AI, for example, or without generative AI. For example, the contact department can input the user's past contact history into generative AI, and the generative AI can provide the most suitable contact.

[0048] The communication unit optimizes emergency communications by coordinating with other users' communication information when making a call. For example, the communication unit makes the most appropriate emergency communications by coordinating with other users' communication information. The communication unit uses a generation AI to analyze other users' communication information and optimize emergency communications. For example, the communication unit optimizes emergency communications based on other users' communication information. The communication unit can also analyze other users' communication information and optimize emergency communications. In this way, the communication unit can optimize emergency communications by coordinating with other users' communication information. Some or all of the above processing in the communication unit may be performed using a generation AI, for example, or without a generation AI. For example, the communication unit can input other users' communication information into a generation AI, which can then make the most appropriate emergency communications.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The disaster response system can also include a pet tracking unit that tracks the location of the user's pet. The pet tracking unit obtains location information through a GPS device attached to the user's pet, ensuring the pet's safety during a disaster. For example, the pet tracking unit can check whether the pet has reached a shelter and notify the user. It can also quickly arrange for rescue if the pet is in a dangerous area. Furthermore, based on the pet's location information, it can suggest the optimal evacuation route and help the user and pet evacuate together. This ensures the safety of the user's pet even during a disaster and provides an environment where they can evacuate with peace of mind.

[0051] The disaster response system can also include a vehicle tracking unit that tracks the location of the user's vehicle. The vehicle tracking unit obtains location information through a GPS device installed in the user's vehicle and ensures the vehicle's safety during a disaster. For example, the vehicle tracking unit can check whether the vehicle is parked in a safe location and notify the user. It can also prompt the user to move quickly if the vehicle is in a dangerous area. Furthermore, based on the vehicle's location information, it can suggest the optimal evacuation route and help the user evacuate safely using their vehicle. This ensures the safety of the user's vehicle and supports rapid evacuation even during a disaster.

[0052] The disaster response system can also include a family tracking unit that tracks the location of the user's family. The family tracking unit obtains location information through GPS devices worn by the user's family members and ensures their safety during a disaster. For example, the family tracking unit can check whether family members have reached a shelter and notify the user. It can also quickly arrange for rescue if family members are in a dangerous area. Furthermore, based on the family's location information, it can suggest the optimal evacuation route and help the user and family evacuate together. This ensures the safety of the user's family even during a disaster and provides an environment where they can evacuate with peace of mind.

[0053] The disaster response system can also be equipped with a fuel monitoring unit that monitors the fuel status of the user's vehicle. The fuel monitoring unit monitors the user's vehicle's fuel level in real time to prevent running out of fuel during a disaster. For example, if the fuel level is low, the fuel monitoring unit will notify the user of the location of the nearest gas station. It can also suggest the optimal route for refueling if fuel is insufficient. Furthermore, it can analyze fuel consumption and suggest efficient fuel usage methods. This allows for proper management of the user's vehicle's fuel status even during a disaster, supporting rapid evacuation.

[0054] The disaster response system can also include a home monitoring unit that monitors the user's home. This unit monitors the home's condition in real time during a disaster through sensors installed in the user's home. For example, it can check the extent of building damage caused by an earthquake and notify the user. It can also monitor flooding and prompt a quick response. Furthermore, in the event of a fire, it can issue a fire alarm and arrange for contact with the fire department. This ensures the safety of the user's home and supports a rapid response during a disaster.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The acquisition unit acquires images during a disaster. The acquisition unit can acquire images during a disaster using, for example, QPS Research Institute's "small SAR satellite". The acquisition unit observes a specific area at an average interval of 10 minutes, and can quickly grasp the extent of damage when a disaster occurs. For example, when a disaster such as an earthquake or flood occurs, the acquisition unit acquires images of that area and transmits them to the generating AI agent. Step 2: The analysis unit analyzes the video footage acquired by the acquisition unit. The analysis unit uses generating AI to understand the extent of the disaster damage from the video. For example, the analysis unit analyzes the extent of building collapse and flooding to identify what kind of response is needed in which areas. The analysis unit has learned from past disaster data and knowledge, enabling it to quickly understand the extent of the damage from the video. Step 3: The identification unit identifies the user's location based on the information analyzed by the analysis unit. The identification unit can identify the location of individual users based on personal data held by the SoftBank Group. This allows the identification unit to provide optimal action instructions and information tailored to the user's location. Step 4: The service provider provides optimal action instructions and information based on the location information identified by the identification unit. The service provider assists users in responding quickly, for example, by providing guidance on evacuation routes and emergency contact information. The service provider learns from past disaster data and knowledge, enabling it to provide more accurate information. For example, it can provide optimal action instructions based on evacuation methods and measures that were effective in past disasters.

[0057] (Example of form 2) The disaster response system according to an embodiment of the present invention is a system that quickly grasps the situation during a disaster and provides users with optimal action instructions and information. This disaster response system utilizes images from a "small SAR satellite" launched by QPS Research Institute to quickly grasp the situation during a disaster, and an AI agent issues instructions and shares information to the user's location via their personal mobile device. Specifically, it consists of the following steps. First, QPS Research Institute's "small SAR satellite" acquires images of the disaster. Next, a generating AI agent analyzes the images to grasp the extent of the damage caused by the disaster. Furthermore, based on personal data held by the SoftBank Group, the generating AI agent identifies the location information of individual users and provides optimal action instructions and information tailored to their location. This mechanism enables a rapid response during a disaster and minimizes damage. First, QPS Research Institute's "small SAR satellite" acquires images of the disaster. This satellite provides a "near real-time data provision service" that can observe specific areas at average intervals of 10 minutes, allowing for a rapid grasp of the damage situation when a disaster occurs. For example, in the event of a disaster such as an earthquake or flood, the satellite acquires images of the area and transmits them to the generating AI agent. Next, the generating AI agent analyzes the acquired video footage. Having learned from past disaster data and insights, the generating AI agent can quickly grasp the extent of damage from the video. For example, it analyzes building collapses and flooding areas to identify what kind of response is needed in which areas. Furthermore, based on personal data held by the SoftBank Group, the generating AI agent identifies the location of individual users. This allows for the provision of optimal action instructions and information tailored to the user's location. For example, it can provide guidance on evacuation routes and emergency contact information to help users respond quickly. This system enables a rapid response during disasters, minimizing damage. Because pinpoint information is provided to local governments and individuals, evacuation and rescue operations can be carried out efficiently. Additionally, because the generating AI agent has learned from past disaster data and insights, it can provide more accurate information. For example, it can provide optimal action instructions based on effective evacuation methods and countermeasures from past disasters.This allows the disaster response system to quickly grasp the situation during a disaster and provide users with optimal action instructions and information.

[0058] The disaster response system according to this embodiment comprises an acquisition unit, an analysis unit, an identification unit, and a provision unit. The acquisition unit acquires video footage during a disaster. The acquisition unit can acquire video footage during a disaster using, for example, QPS Research Institute's "small SAR satellite." The acquisition unit observes a specific area at an average interval of 10 minutes and can quickly grasp the extent of damage when a disaster occurs. For example, when a disaster such as an earthquake or flood occurs, the acquisition unit acquires video footage of that area and transmits it to a generating AI agent. The analysis unit analyzes the video footage acquired by the acquisition unit. The analysis unit uses a generating AI to grasp the extent of disaster damage from the video footage. For example, the analysis unit analyzes the extent of building collapse and flooding to identify what kind of response is needed in which area. The analysis unit has learned from past disaster data and knowledge, and can quickly grasp the extent of damage from the video footage. The identification unit identifies the user's location information based on the information analyzed by the analysis unit. The identification unit can identify the location information of individual users based on personal data held by the SoftBank Group. This allows the identification unit to provide optimal action instructions and information tailored to the user's location. The provision unit provides optimal action instructions and information based on location information identified by the identification unit. The provision unit assists users in responding quickly, for example, by providing guidance on evacuation routes and emergency contact information. The provision unit learns from past disaster data and knowledge, enabling it to provide more accurate information. For example, it can provide optimal action instructions based on evacuation methods and countermeasures that were effective in past disasters. As a result, the disaster response system according to this embodiment can quickly grasp the situation during a disaster and provide users with optimal action instructions and information.

[0059] The acquisition unit acquires images during disasters. For example, the acquisition unit can acquire images during disasters using QPS Research Institute's "small SAR satellite." The small SAR satellite uses synthetic aperture radar technology and can acquire high-resolution images regardless of weather or time of day. This allows the acquisition unit to acquire detailed images of disaster areas day or night, and even in bad weather. The acquisition unit observes specific areas at average intervals of 10 minutes, enabling it to quickly grasp the extent of damage when a disaster occurs. For example, when a disaster such as an earthquake or flood occurs, the acquisition unit acquires images of the area and transmits them to the generating AI agent. The acquisition unit continuously observes from immediately after the disaster occurs, enabling it to grasp the progress and changes of damage in real time. This allows the acquisition unit to record the entire process from the initial stages of the disaster to recovery in detail and provide necessary data to the analysis unit and the specific unit. Furthermore, by coordinating multiple small SAR satellites, the acquisition unit can simultaneously observe a wide range of disaster areas. This enables the acquisition unit to respond quickly and accurately to large-scale disasters. The acquisition unit transmits the acquired image data to the analysis unit in real time to support a rapid response. This allows the data acquisition unit to quickly grasp the situation during a disaster and play a crucial role in supporting the efficient operation of the entire system.

[0060] The analysis unit analyzes the video footage acquired by the acquisition unit. The analysis unit uses generative AI to understand the extent of disaster damage from the video. The generative AI utilizes deep learning technology and has learned from vast amounts of past disaster data and images. This allows the analysis unit to analyze building collapses, flooding extents, and other factors with high accuracy. For example, the analysis unit identifies collapsed building locations from the acquired video and evaluates the degree of collapse and the affected area. In the case of flooding, it analyzes the flooded area to understand which areas have suffered the most damage. Using the generative AI's image recognition technology, the analysis unit quickly analyzes the damage and identifies what kind of response is needed in which areas. Because the analysis unit has learned from past disaster data and knowledge, it can quickly grasp the extent of damage from the video. Furthermore, the analysis unit analyzes the acquired video data chronologically, tracking the progress and changes of the disaster in real time. This allows the analysis unit to understand the entire process from the initial stages of the disaster to recovery in detail and provide necessary information to the identification and provision units. The analysis unit plays a crucial role in supporting the efficient operation of the entire system by utilizing the advanced analytical capabilities of the generation AI to quickly and accurately grasp the situation during a disaster.

[0061] The Identification Unit identifies the user's location based on information analyzed by the Analysis Unit. The Identification Unit can identify the location of individual users based on personal data held by the SoftBank Group. The Identification Unit acquires the location information of users' smartphones and mobile phones in real time, accurately determining their location. This allows the Identification Unit to provide optimal action instructions and information tailored to the user's location. The Identification Unit integrates the acquired location information with the Analysis Unit's damage data to identify the specific risks the user is facing. For example, if a user is in a flooded area, the Identification Unit identifies the flooding situation and evacuation routes in that area and provides appropriate action instructions. Furthermore, based on the user's location information, the Identification Unit can identify the locations of evacuation shelters and support facilities during disasters and guide users to the most suitable evacuation destination. The Identification Unit updates the user's location information in real time and takes appropriate action according to the progress of the disaster. This allows the Identification Unit to support users in evacuating quickly and safely. In addition, the Identification Unit manages user location information anonymly, protecting privacy while providing information necessary for disaster response. This means that the specific unit plays a crucial role in ensuring user safety and supporting the efficient operation of the entire system.

[0062] The service provider provides optimal action instructions and information based on location information identified by the identification unit. The service provider assists users in responding quickly, for example, by providing evacuation route guidance and emergency contact information. The service provider notifies users of evacuation routes and emergency contact information in real time via their smartphones or mobile phones. For example, the service provider guides users to the optimal evacuation route based on their location and alerts them through voice guidance and vibration notifications. The service provider also provides users with information on emergency contacts and support facilities during disasters to support a rapid response. The service provider learns from past disaster data and knowledge, enabling it to provide more accurate information. For example, it can provide optimal action instructions based on effective evacuation methods and measures from past disasters. The service provider collects user feedback and can continuously improve the accuracy and effectiveness of the information it provides. For example, it can revise evacuation routes and improve instructions based on feedback from users who received evacuation instructions. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to quickly and reliably provide users with action instructions, minimizing the risk of disaster.

[0063] The analysis unit includes a learning unit that learns from past disaster data and knowledge. The analysis unit can learn from, for example, past disaster reports, research papers, and news articles, enabling it to quickly grasp the extent of disaster damage. The analysis unit can use generative AI to learn from past disaster data and knowledge, enabling it to quickly grasp the extent of damage from video. For example, the analysis unit can learn from past earthquake data and analyze the extent of building collapse caused by earthquakes. The analysis unit can also learn from past flood data and analyze the extent of flooding. Furthermore, the analysis unit can learn from past fire data and analyze the extent of damage caused by fires. As a result, the accuracy of the analysis unit's analysis improves by learning from past disaster data and knowledge. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input past disaster data into the generative AI, which can learn from the data and analyze the extent of disaster damage.

[0064] The service unit includes a guidance unit that provides evacuation route guidance. The service unit can guide users to the optimal evacuation route using, for example, real-time traffic information, map information, and voice guidance. The service unit uses generative AI to guide users to the optimal evacuation route based on the user's location information and the extent of disaster damage. For example, the service unit proposes the safest evacuation route from the user's current location. The service unit can also guide users to evacuation routes that avoid congestion based on real-time traffic information. Furthermore, the service unit can guide users to evacuation routes using voice guidance. As a result, the service unit enables users to evacuate quickly by providing evacuation route guidance. Some or all of the above processing in the service unit may be performed using, for example, generative AI, or without generative AI. For example, the service unit can input the user's location information and the extent of disaster damage into the generative AI, which can then propose the optimal evacuation route.

[0065] The service provider includes a contact unit that provides emergency contact information. The service provider can provide users with emergency contact information such as police, fire departments, hospitals, and family members. Using a generative AI, the service provider provides the most suitable emergency contact information based on the user's location and the extent of the disaster damage. For example, the service provider provides contact information for the nearest police station or fire station to the user's current location. The service provider can also provide contact information for the user's family. Furthermore, the service provider can provide emergency contact information to the user using voice guidance. This allows the service provider to enable the user to contact emergency contacts quickly. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's location and the extent of the disaster damage into the generative AI, which can then suggest the most suitable emergency contact information.

[0066] The acquisition unit observes a specific area at an average interval of 10 minutes. The acquisition unit can observe a specific area at an average interval of 10 minutes, for example, using QPS Research Institute's "small SAR satellite". By observing a specific area, the acquisition unit can quickly grasp the extent of damage in the event of a disaster. For example, when a disaster such as an earthquake or flood occurs, the acquisition unit acquires images of the area and transmits them to a generating AI agent. This allows the acquisition unit to quickly grasp the situation during a disaster by observing a specific area at an average interval of 10 minutes. Some or all of the above processing in the acquisition unit may be performed using a generating AI, for example, or without using a generating AI. For example, the acquisition unit can input images of a specific area into a generating AI, which can analyze the images and grasp the extent of the disaster damage.

[0067] The analysis unit analyzes the extent of building collapse and flooding. The analysis unit can analyze the extent of building collapse and flooding using, for example, image analysis technology. The analysis unit uses generative AI to analyze the extent of building collapse and flooding from disaster footage. For example, the analysis unit analyzes the extent of building collapse caused by an earthquake and identifies what kind of response is needed in which areas. The analysis unit can also analyze the extent of flooding caused by a flood and identify what kind of response is needed in which areas. In this way, the analysis unit can quickly grasp the extent of disaster damage by analyzing the extent of building collapse and flooding. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input disaster footage into generative AI, and the generative AI can analyze the extent of building collapse and flooding.

[0068] The information delivery unit dynamically changes the notification method according to the urgency of the information being delivered. For example, the delivery unit prioritizes voice notifications for highly urgent information. The delivery unit uses a generative AI to evaluate the urgency of the information and select the optimal notification method. For example, the delivery unit sends a text notification for moderately urgent information. The delivery unit can also send in-app notifications for less urgent information. In this way, the delivery unit can provide users with appropriate information by changing the notification method according to the urgency of the information. Some or all of the above processing in the delivery unit may be performed using a generative AI, or not. For example, the delivery unit can input the urgency of the information into the generative AI, which can then select the optimal notification method.

[0069] The service provider provides optimal action instructions based on the user's past behavioral history at the time of service provision. For example, the service provider may suggest the optimal evacuation route based on the user's past evacuation routes. The service provider uses generative AI to analyze the user's past behavioral history and provide optimal action instructions. For example, the service provider may select an evacuation shelter based on the user's past behavioral history. The service provider can also provide optimal action instructions based on the user's past behavioral history. This allows the service provider to provide more appropriate instructions by providing action instructions based on the user's past behavioral history. Some or all of the above processing in the service provider may be performed using generative AI, or without using generative AI. For example, the service provider can input the user's past behavioral history into the generative AI, which can then provide optimal action instructions.

[0070] The service provider estimates the user's emotions and determines the priority of information to provide based on the estimated emotions. For example, if the user is feeling anxious, the service provider will prioritize providing important information. The service provider uses generative AI to estimate the user's emotions and determine the optimal information priority. For example, if the user is calm, the service provider will prioritize providing detailed information. The service provider can also prioritize providing information that encourages a quick response if the user is in a state of panic. In this way, the service provider can provide more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 processing in the service provider may be performed using generative AI, for example, or without generative AI. For example, the service provider can input user emotion data into a generative AI, which can then determine the optimal information priority.

[0071] The service provider selects the optimal display method according to the user's device information at the time of delivery. For example, the service provider provides a display method that matches the screen size to a user using a smartphone. The service provider analyzes the user's device information using a generation AI and selects the optimal display method. For example, the service provider provides a display method optimized for a large screen to a user using a tablet. The service provider can also provide a concise and highly visible display method to a user using a smartwatch. In this way, the service provider can provide highly visible information by selecting a display method according to the user's device information. Some or all of the above processing in the service provider may be performed using a generation AI, for example, or without a generation AI. For example, the service provider can input the user's device information into a generation AI, and the generation AI can select the optimal display method.

[0072] The service provider optimizes evacuation instructions by linking them with other users' behavior information when providing instructions. For example, the service provider provides optimal evacuation instructions by linking them with the evacuation status of other users. The service provider uses a generation AI to analyze other users' behavior information and optimize evacuation instructions. For example, the service provider optimizes evacuation instructions based on other users' behavior information. The service provider can also analyze other users' behavior information and optimize evacuation instructions. In this way, the service provider can optimize evacuation instructions by linking them with other users' behavior information. Some or all of the above processing in the service provider may be performed using a generation AI, for example, or without a generation AI. For example, the service provider can input other users' behavior information into a generation AI, which can then provide optimal evacuation instructions.

[0073] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is feeling anxious, the learning unit selects data from past disaster data to alleviate that anxiety. The learning unit uses generative AI to estimate the user's emotions and select the optimal training data. For example, if the user is calm, the learning unit selects detailed disaster data. The learning unit can also select data to encourage a rapid response if the user is in a state of panic. This allows the learning unit to perform more appropriate learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the learning unit may be performed using a generative AI, for example, or without a generative AI. For example, the learning unit can input the user's emotion data into a generative AI, which can then select the optimal training data.

[0074] The learning unit optimizes its learning algorithm by referring to past disaster data during the learning process. For example, the learning unit optimizes its learning algorithm by referring to past earthquake data. The learning unit optimizes its learning algorithm by referring to past disaster data using a generative AI. For example, the learning unit optimizes its learning algorithm by referring to past flood data. The learning unit can also optimize its learning algorithm by referring to past fire data. This improves the accuracy of learning by optimizing the learning algorithm by referring to past disaster data. Some or all of the above processing in the learning unit may be performed using a generative AI, for example, or without a generative AI. For example, the learning unit can input past disaster data into a generative AI, which can then optimize the learning algorithm.

[0075] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is feeling anxious, the learning unit increases the learning frequency to enable a quicker response. The learning unit uses generative AI to estimate the user's emotions and adjust the learning frequency. For example, if the user is calm, the learning unit maintains the learning frequency at a normal level. Alternatively, if the user is in a state of panic, the learning unit can maximize the learning frequency to encourage a quicker response. This allows the learning unit to perform more appropriate learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using, for example, generative AI, or without generative AI. For example, the learning unit can input user emotion data into the generative AI, which can then adjust the learning frequency.

[0076] The learning unit weights the training data according to the type and scale of the disaster during training. For example, in the case of an earthquake, the learning unit weights the earthquake data. The learning unit uses a generative AI to weight the training data according to the type and scale of the disaster. For example, in the case of a flood, the learning unit weights the flood data. The learning unit can also weight the fire data in the case of a fire. This improves the accuracy of training by weighting the training data according to the type and scale of the disaster. Some or all of the above processing in the learning unit may be performed using a generative AI, for example, or without a generative AI. For example, the learning unit can input disaster data into a generative AI, and the generative AI can perform the data weighting.

[0077] The guidance unit estimates the user's emotions and adjusts the evacuation route guidance method based on the estimated emotions. For example, if the user is feeling anxious, the guidance unit provides concise and easy-to-understand evacuation route guidance. The guidance unit uses generative AI to estimate the user's emotions and select the optimal evacuation route guidance method. For example, if the user is calm, the guidance unit provides detailed evacuation route guidance. The guidance unit can also provide evacuation route guidance that encourages a quick response if the user is in a state of panic. In this way, the guidance unit can provide more appropriate guidance by adjusting the evacuation route guidance method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 processing in the guidance unit may be performed using generative AI, for example, or without generative AI. For example, the guidance unit can input user emotion data into a generative AI, and the generative AI can select the optimal evacuation route guidance method.

[0078] The guidance unit provides the optimal evacuation route by considering real-time traffic information during guidance. For example, the guidance unit provides the optimal evacuation route based on real-time traffic congestion information. The guidance unit uses a generation AI to analyze real-time traffic information and provide the optimal evacuation route. For example, the guidance unit provides the optimal evacuation route by considering the real-time operation status of public transportation. The guidance unit can also provide detour routes based on real-time road construction information. In this way, the guidance unit can provide evacuation routes considering real-time traffic information, enabling more appropriate evacuations. Some or all of the above processing in the guidance unit may be performed using a generation AI, for example, or without a generation AI. For example, the guidance unit can input real-time traffic information into a generation AI, and the generation AI can provide the optimal evacuation route.

[0079] The guidance unit estimates the user's emotions and determines the priority of evacuation routes based on the estimated emotions. For example, if the user is feeling anxious, the guidance unit will prioritize guiding them to important evacuation routes. The guidance unit uses generative AI to estimate the user's emotions and determine the optimal priority of evacuation routes. For example, if the user is calm, the guidance unit will prioritize guiding them to detailed evacuation routes. The guidance unit can also prioritize guiding them to evacuation routes that encourage a quick response if the user is in a state of panic. In this way, the guidance unit can ensure more appropriate evacuations by determining the priority of evacuation routes based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 processing in the guidance unit may be performed using generative AI, for example, or without generative AI. For example, the guidance unit can input user emotion data into a generative AI, which can then determine the optimal priority of evacuation routes.

[0080] The guidance unit optimizes evacuation routes by coordinating with the evacuation status of other users during guidance. For example, the guidance unit provides the optimal evacuation route by coordinating with the evacuation status of other users. The guidance unit uses generative AI to analyze the evacuation status of other users and optimize evacuation routes. For example, the guidance unit optimizes evacuation routes based on the evacuation status of other users. The guidance unit can also analyze the evacuation status of other users and optimize evacuation routes. In this way, the guidance unit can optimize evacuation routes by coordinating with the evacuation status of other users. Some or all of the above processing in the guidance unit may be performed using generative AI, for example, or without using generative AI. For example, the guidance unit can input the evacuation status of other users into the generative AI, and the generative AI can provide the optimal evacuation route.

[0081] The communication unit estimates the user's emotions and adjusts how emergency contact information is provided based on the estimated emotions. For example, if the user is feeling anxious, the communication unit will provide concise and easy-to-understand emergency contact information. The communication unit uses generative AI to estimate the user's emotions and select the most appropriate method for providing emergency contact information. For example, if the user is calm, the communication unit will provide detailed emergency contact information. If the user is in a state of panic, the communication unit may also provide emergency contact information that encourages a quick response. In this way, the communication unit can make more appropriate contact by adjusting how emergency contact information is provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 processing in the communication unit may be performed using generative AI, for example, or without generative AI. For example, the communication unit can input user emotion data into a generative AI, which can then select the most appropriate method for providing emergency contact information.

[0082] The contact department provides the most suitable contact based on the user's past contact history when contact is made. For example, the contact department provides the most suitable contact based on the emergency contacts the user has contacted in the past. The contact department uses generative AI to analyze the user's past contact history and provide the most suitable contact. For example, the contact department provides the most suitable contact from the user's past contact history. The contact department can also provide the most suitable contact based on the user's past contact history. This allows the contact department to make more appropriate contact by providing contact based on the user's past contact history. Some or all of the above processing in the contact department may be performed using generative AI, for example, or without generative AI. For example, the contact department can input the user's past contact history into generative AI, and the generative AI can provide the most suitable contact.

[0083] The communication unit estimates the user's emotions and determines the priority of communication based on the estimated emotions. For example, if the user is feeling anxious, the communication unit will prioritize providing important contacts. The communication unit uses generative AI to estimate the user's emotions and determine the optimal priority of communication. For example, if the user is calm, the communication unit will prioritize providing detailed contacts. Also, if the user is in a state of panic, the communication unit can prioritize providing contacts that can prompt a quick response. In this way, the communication unit can make more appropriate communications by prioritizing communication based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 processing in the communication unit may be performed using generative AI, for example, or not using generative AI. For example, the communication unit can input user emotion data into a generative AI, and the generative AI can determine the optimal priority of communication.

[0084] The communication unit optimizes emergency communications by coordinating with other users' communication information when making a call. For example, the communication unit makes the most appropriate emergency communications by coordinating with other users' communication information. The communication unit uses a generation AI to analyze other users' communication information and optimize emergency communications. For example, the communication unit optimizes emergency communications based on other users' communication information. The communication unit can also analyze other users' communication information and optimize emergency communications. In this way, the communication unit can optimize emergency communications by coordinating with other users' communication information. Some or all of the above processing in the communication unit may be performed using a generation AI, for example, or without a generation AI. For example, the communication unit can input other users' communication information into a generation AI, which can then make the most appropriate emergency communications.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The disaster response system can also be equipped with a health monitoring unit to monitor the user's health status. The health monitoring unit acquires vital data such as the user's heart rate, blood pressure, and body temperature, and monitors stress and changes in physical condition in real time during a disaster. For example, if the health monitoring unit detects a sudden increase in the user's heart rate, it can determine that stress and anxiety are high and provide advice on how to relax. It can also prompt the user to contact a medical institution if their blood pressure is abnormally high. Furthermore, if their body temperature is abnormally high, it can warn of the risk of heatstroke and suggest appropriate countermeasures. This allows for proper management of the user's health status even during a disaster, supporting a rapid response.

[0087] The disaster response system can also include a pet tracking unit that tracks the location of the user's pet. The pet tracking unit obtains location information through a GPS device attached to the user's pet, ensuring the pet's safety during a disaster. For example, the pet tracking unit can check whether the pet has reached a shelter and notify the user. It can also quickly arrange for rescue if the pet is in a dangerous area. Furthermore, based on the pet's location information, it can suggest the optimal evacuation route and help the user and pet evacuate together. This ensures the safety of the user's pet even during a disaster and provides an environment where they can evacuate with peace of mind.

[0088] The disaster response system can also include a support unit that estimates the user's emotions and provides support in the evacuation center based on those emotions. This support unit provides assistance to alleviate the stress and anxiety users experience in the evacuation center. For example, if a user is feeling anxious, the support unit can provide relaxing music or meditation guidance. If a user is feeling lonely, it can also provide information about events to encourage interaction with other evacuees. Furthermore, if a user is in a state of panic, the system can arrange contact with a psychological counselor to ensure they receive support quickly. This supports the mental well-being of users in the evacuation center and provides a safe and secure environment.

[0089] The disaster response system can also include a vehicle tracking unit that tracks the location of the user's vehicle. The vehicle tracking unit obtains location information through a GPS device installed in the user's vehicle and ensures the vehicle's safety during a disaster. For example, the vehicle tracking unit can check whether the vehicle is parked in a safe location and notify the user. It can also prompt the user to move quickly if the vehicle is in a dangerous area. Furthermore, based on the vehicle's location information, it can suggest the optimal evacuation route and help the user evacuate safely using their vehicle. This ensures the safety of the user's vehicle and supports rapid evacuation even during a disaster.

[0090] The disaster response system may also include a food service unit that estimates users' emotions and adjusts meal provision at evacuation centers based on those estimated emotions. The food service unit provides appropriate meals according to the user's emotions and health condition. For example, if a user is stressed, it can provide meals using ingredients that have a relaxing effect. If a user is anxious, it can provide nutritionally balanced meals to help them regain their health. Furthermore, if a user is in a state of panic, it can provide easily digestible snacks to prevent them from becoming ill. This supports the health of users in evacuation centers and provides a safe and secure environment.

[0091] The disaster response system can also include a family tracking unit that tracks the location of the user's family. The family tracking unit obtains location information through GPS devices worn by the user's family members and ensures their safety during a disaster. For example, the family tracking unit can check whether family members have reached a shelter and notify the user. It can also quickly arrange for rescue if family members are in a dangerous area. Furthermore, based on the family's location information, it can suggest the optimal evacuation route and help the user and family evacuate together. This ensures the safety of the user's family even during a disaster and provides an environment where they can evacuate with peace of mind.

[0092] The disaster response system may also include a sleep environment unit that estimates the user's emotions and adjusts the sleeping environment in the evacuation center based on those emotions. The sleep environment unit provides an appropriate sleeping environment according to the user's emotions and health condition. For example, if the user is stressed, it can provide relaxing music or aromatherapy. If the user is anxious, it can provide reassuring lighting and bedding to support comfortable sleep. Furthermore, if the user is in a state of panic, it can provide a quiet environment to help them calm down quickly. This can support the user's sleep in the evacuation center and provide a safe and secure environment.

[0093] The disaster response system can also be equipped with a fuel monitoring unit that monitors the fuel status of the user's vehicle. The fuel monitoring unit monitors the user's vehicle's fuel level in real time to prevent running out of fuel during a disaster. For example, if the fuel level is low, the fuel monitoring unit will notify the user of the location of the nearest gas station. It can also suggest the optimal route for refueling if fuel is insufficient. Furthermore, it can analyze fuel consumption and suggest efficient fuel usage methods. This allows for proper management of the user's vehicle's fuel status even during a disaster, supporting rapid evacuation.

[0094] The disaster response system can also include an entertainment unit that estimates users' emotions and provides entertainment in evacuation shelters based on those emotions. The entertainment unit provides appropriate entertainment according to the user's emotions and health condition. For example, if a user is stressed, it can provide relaxing movies or music. If a user is anxious, it can provide games or activities to distract them and alleviate their anxiety. Furthermore, if a user is in a state of panic, it can provide meditation guidance or relaxation programs to calm them down. This supports the mental health of users in evacuation shelters and provides a safe and secure environment.

[0095] The disaster response system can also include a home monitoring unit that monitors the user's home. This unit monitors the home's condition in real time during a disaster through sensors installed in the user's home. For example, it can check the extent of building damage caused by an earthquake and notify the user. It can also monitor flooding and prompt a quick response. Furthermore, in the event of a fire, it can issue a fire alarm and arrange for contact with the fire department. This ensures the safety of the user's home and supports a rapid response during a disaster.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The acquisition unit acquires images during a disaster. The acquisition unit can acquire images during a disaster using, for example, QPS Research Institute's "small SAR satellite". The acquisition unit observes a specific area at an average interval of 10 minutes, and can quickly grasp the extent of damage when a disaster occurs. For example, when a disaster such as an earthquake or flood occurs, the acquisition unit acquires images of that area and transmits them to the generating AI agent. Step 2: The analysis unit analyzes the video footage acquired by the acquisition unit. The analysis unit uses generating AI to understand the extent of the disaster damage from the video. For example, the analysis unit analyzes the extent of building collapse and flooding to identify what kind of response is needed in which areas. The analysis unit has learned from past disaster data and knowledge, enabling it to quickly understand the extent of the damage from the video. Step 3: The identification unit identifies the user's location based on the information analyzed by the analysis unit. The identification unit can identify the location of individual users based on personal data held by the SoftBank Group. This allows the identification unit to provide optimal action instructions and information tailored to the user's location. Step 4: The service provider provides optimal action instructions and information based on the location information identified by the identification unit. The service provider assists users in responding quickly, for example, by providing guidance on evacuation routes and emergency contact information. The service provider learns from past disaster data and knowledge, enabling it to provide more accurate information. For example, it can provide optimal action instructions based on evacuation methods and measures that were effective in past disasters.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] Each of the multiple elements described above, including the acquisition unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can acquire images during a disaster using the camera 42 of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired images to understand the extent of the disaster damage. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the user's location information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the user with optimal action instructions and information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the acquisition unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can acquire images during a disaster using the camera 42 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired images to understand the extent of the disaster damage. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the user's location information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the user with optimal action instructions and information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the acquisition unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can acquire video footage during a disaster using the camera 42 of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired video footage to understand the extent of the disaster damage. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies the user's location information. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides the user with optimal action instructions and information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the acquisition unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can acquire images of a disaster using the camera 42 of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired images to understand the extent of the damage caused by the disaster. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the user's location information. The provision unit is implemented by the control unit 46A of the robot 414 and provides the user with optimal action instructions and information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) The acquisition unit acquires video footage during disasters, An analysis unit analyzes the video acquired by the acquisition unit, A unit that identifies the user's location information based on the information analyzed by the analysis unit, A providing unit that provides optimal action instructions and information based on location information identified by the aforementioned specific unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit, It is equipped with a learning unit that learns from past disaster data and knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Equipped with an information desk to guide people along evacuation routes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Equipped with a liaison department to provide emergency contact information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, Observe a specific area at average intervals of 10 minutes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze the extent of building collapse and flooding. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, The notification method will dynamically change depending on the urgency of the information being provided. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, When providing the service, the system will provide optimal action instructions based on the user's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, At the time of delivery, the optimal display method is selected according to the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, When providing the service, it will be integrated with other users' behavioral information to optimize evacuation orders. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 13) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past disaster data. The system described in Appendix 2, characterized by the features described herein. (Note 14) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 15) The aforementioned learning unit, During training, the training data is weighted according to the type and scale of the disaster. The system described in Appendix 2, characterized by the features described herein. (Note 16) The aforementioned guide section is The system estimates the user's emotions and adjusts the evacuation route guidance method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 17) The aforementioned guide section is When providing guidance, the optimal evacuation route will be provided, taking real-time traffic information into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 18) The aforementioned guide section is It estimates the user's emotions and determines the priority of evacuation routes based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 19) The aforementioned guide section is During the guidance process, the system will optimize evacuation routes by coordinating with the evacuation status of other users. The system described in Appendix 3, characterized by the features described herein. (Note 20) The aforementioned liaison department, The system estimates the user's emotions and adjusts how emergency contact information is provided based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 21) The aforementioned liaison department, When contacting someone, we provide the most suitable contact based on the user's past contact history. The system described in Appendix 4, characterized by the features described herein. (Note 22) The aforementioned liaison department, It estimates the user's emotions and determines the priority of communication based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 23) The aforementioned liaison department, When contacting users, we aim to optimize emergency communications by linking with other users' contact information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The acquisition unit acquires video footage during disasters, An analysis unit analyzes the video acquired by the acquisition unit, A unit that identifies the user's location information based on the information analyzed by the analysis unit, A providing unit that provides optimal action instructions and information based on location information identified by the aforementioned specific unit, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, It is equipped with a learning unit that learns from past disaster data and knowledge. The system according to feature 1.

3. The aforementioned supply unit is, Equipped with an information desk to guide people along evacuation routes. The system according to feature 1.

4. The aforementioned supply unit is, Equipped with a liaison department to provide emergency contact information. The system according to feature 1.

5. The acquisition unit is, Observe a specific area at average intervals of 10 minutes. The system according to feature 1.

6. The aforementioned analysis unit, Analyze the extent of building collapse and flooding. The system according to feature 1.

7. The aforementioned supply unit is, The notification method will dynamically change depending on the urgency of the information being provided. The system according to feature 1.

8. The aforementioned supply unit is, When providing the service, the system will provide optimal action instructions based on the user's past behavioral history. The system according to feature 1.

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