system

Abstract

The system according to this embodiment aims to quickly process rescue requests during disasters and provide information to local governments in an appropriate manner. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a notification unit, an integration unit, and a guidance unit. The reception unit receives rescue requests from users. The analysis unit analyzes the information received by the reception unit. The notification unit notifies the local government of the appropriate location and situation based on the information analyzed by the analysis unit. The integration unit reflects the information notified by the notification unit in the local government system and integrates the information in real time. The guidance unit presents the user with the optimal evacuation route based on the information integrated by the integration unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the processing of rescue requests in disasters is delayed and real-time rescue activities are difficult.

[0005] The system according to the embodiment aims to quickly process rescue requests in disasters and provide information to local governments in an appropriate form.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a notification unit, an integration unit, and a guidance unit. The reception unit receives rescue requests from users. The analysis unit analyzes the information received by the reception unit. The notification unit notifies the local government of the appropriate location and situation based on the information analyzed by the analysis unit. The integration unit reflects the information notified by the notification unit in the local government system and integrates the information in real time. The guidance unit presents the user with the optimal evacuation route based on the information integrated by the integration unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly process rescue requests during disasters and provide information to local governments in an appropriate manner. [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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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 evacuation support system according to an embodiment of the present invention is a system that utilizes a generation AI to quickly process rescue requests from users via a messaging application and provide information to local governments in an appropriate format. This evacuation support system uses a generation AI to analyze rescue requests sent by users via a messaging application and grasp disaster information accurately and quickly. Next, based on the information analyzed by the generation AI, it quickly notifies local governments of the appropriate location and situation to facilitate rescue responses. Furthermore, it reflects data on damage reports and rescue requests via the messaging application in the local government system and integrates information in real time. Finally, based on the damage information, it constantly presents the user with the optimal evacuation route and supports safe evacuation. For example, the generation AI analyzes rescue requests sent by users via a messaging application. At this time, the user's location information and damage report are input to the generation AI. For example, if a user sends a message such as "My house is flooded," the generation AI analyzes the message and grasps the accurate location information and damage situation. Next, based on the information analyzed by the generation AI, it quickly notifies local governments of the appropriate location and situation. For example, the generating AI can analyze information such as "my home is flooded" and notify the local government of its location, enabling the government to respond quickly to rescue efforts. Furthermore, data from damage reports and rescue requests can be reflected in the local government system via messaging apps, integrating information in real time. This allows local governments to organize disaster information from multiple sources and respond efficiently. For example, damage reports from multiple users can be integrated to grasp the overall picture of the damage. Finally, based on the damage information, the system can always present the user with the most suitable evacuation route to support safe evacuation. For example, the generating AI can analyze damage information and send a message to the user such as, "The nearest evacuation center is XX. Please evacuate using this route," enabling the user to evacuate safely. This system supports real-time rescue operations and enables local governments to respond quickly. It also supports safe evacuation by presenting the user with the most suitable evacuation route. This minimizes damage during disasters.This allows the evacuation support system to quickly process users' rescue requests and provide information to local authorities in an appropriate format.

[0029] The evacuation support system according to this embodiment comprises a reception unit, an analysis unit, a notification unit, an integration unit, and a guidance unit. The reception unit receives rescue requests from users. The reception unit can, for example, receive rescue requests sent by users through messaging apps. The reception unit can also receive user location information and damage reports. For example, if a user sends a message such as "My house is flooded," the reception unit receives that message. The analysis unit analyzes the information received by the reception unit using a generation AI. The analysis unit inputs, for example, the user's location information and damage report into the generation AI, which then analyzes the information. For example, the generation AI analyzes the message "My house is flooded" and obtains accurate location information and damage status. The notification unit notifies the local government of the appropriate location and situation based on the information analyzed by the analysis unit. The notification unit can, for example, notify the local government of the information analyzed by the generation AI. For example, the notification unit notifies the local government of the information "My house is flooded." The integration unit reflects the information notified by the notification unit into the local government system, integrating the information in real time. The integration unit can, for example, integrate damage reports from multiple users to grasp the overall picture of the damage. For example, the integration unit can organize damage reports from multiple users and respond efficiently. The guidance unit presents the user with the optimal evacuation route based on the information integrated by the integration unit. For example, the guidance unit can use a generating AI to analyze damage information and send a message to the user such as, "The nearest evacuation center is XX. Please evacuate using this route." As a result, the evacuation support system according to the embodiment can quickly process the user's rescue request and provide information to the local government in an appropriate format.

[0030] The reception desk receives rescue requests from users. For example, the reception desk can receive rescue requests sent by users through messaging apps. Specifically, users can send rescue requests using a dedicated application on their smartphones or tablets. This application has an easy-to-use interface and has a function to send rescue requests with a single tap in emergencies. The reception desk can also receive users' location information and damage reports. For example, if a user sends a message such as "My house is flooded," the reception desk will receive the message. Location information is automatically obtained using the smartphone's GPS function, and damage reports are sent in the form of text messages, photos, videos, etc. This allows the reception desk to quickly collect detailed information from users and pass it on to the next processing step. Furthermore, the reception desk can also receive voice rescue requests using voice recognition technology. For example, if a user says "Help" aloud, the system can recognize the voice and receive it as a rescue request. This makes it easy for users with their hands full or those with visual impairments to request rescue.

[0031] The analysis unit uses a generation AI to analyze information received by the reception unit. For example, the analysis unit inputs user location information and damage reports into the generation AI, which then analyzes the information. Specifically, the generation AI uses natural language processing technology to analyze text messages from users and determine the nature and urgency of the damage. For example, it analyzes a message such as "My house is flooded" to estimate the extent of the flooding and the affected area. It also identifies the specific location where the damage occurred based on the location information. Furthermore, the generation AI uses image recognition technology to analyze photos and videos sent by users to confirm visual evidence of the damage. For example, it can determine the depth and extent of the flooding from images and grasp the detailed situation of the damage. This allows the analysis unit to analyze the collected information from multiple angles and grasp the accurate extent of the damage. In addition, the analysis unit can also utilize past data and statistical information to analyze damage patterns and trends. For example, based on past flood data, it can predict fluctuations in risk in specific areas and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The notification unit notifies local governments of the appropriate locations and situations based on the information analyzed by the analysis unit. For example, the notification unit can notify local governments of information analyzed by a generation AI. Specifically, the notification unit automatically transmits the analyzed damage information to the local government's disaster response headquarters. The transmitted information includes detailed information about the damage, the location of the incident, and the urgency level, enabling local governments to respond quickly. The notification unit can also link with local government systems and update information in real time. For example, if the damage situation changes or a new rescue request is made, the notification unit immediately notifies the local government of this information, reflecting the latest situation. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, it can use email, SMS, and dedicated applications in combination to ensure that important information is delivered reliably. In this way, the notification unit can provide local governments with rapid and accurate information and support effective disaster response.

[0033] The Integration Department integrates information received by the Notification Department into the local government system, providing real-time information consolidation. For example, the Integration Department can consolidate damage reports from multiple users to grasp the overall picture of the damage. Specifically, the Integration Department works in conjunction with the local government's disaster response system to centrally manage the received damage information. This allows local governments to quickly grasp the scale and scope of the damage and take appropriate countermeasures. The Integration Department can also visualize damage information on a map, allowing for visual confirmation of the distribution of damage and evacuation routes. For example, it can map the locations of damage on a map to identify areas where damage is concentrated and areas requiring evacuation. Furthermore, the Integration Department can utilize historical data and statistical information to analyze damage patterns and trends. This allows local governments to obtain valuable information for formulating long-term disaster countermeasures and preventive measures. Because the Integration Department continuously updates information in real time, it can always grasp the latest situation. This enables the Integration Department to improve the disaster response capabilities of local governments and support a rapid and effective response.

[0034] The guidance unit presents the user with the optimal evacuation route based on information integrated by the integration unit. For example, the guidance unit can use a generating AI to analyze damage information and send a message to the user such as, "The nearest evacuation center is XX. Please evacuate using this route." Specifically, the guidance unit calculates the optimal evacuation route based on the user's current location and evacuation destination. The calculation considers road conditions, traffic information, and the extent of damage to select the safest and fastest route. Furthermore, the guidance unit can dynamically modify the evacuation route based on information updated in real time. For example, if new damage occurs or roads are blocked during evacuation, the guidance unit immediately calculates a new route and notifies the user. The guidance unit also provides means to ensure that users receive information reliably during evacuation, such as voice guidance and vibration notifications. This allows users to act with peace of mind even during evacuation. In addition, the guidance unit can select an appropriate evacuation destination by considering the congestion level and capacity of evacuation centers. This prevents overcrowding at evacuation centers and supports efficient evacuation. The guidance unit provides users with quick and accurate evacuation instructions, minimizing the risk of disaster.

[0035] The reception desk can receive user location information and damage reports. For example, the reception desk can receive location information and damage reports sent by users through messaging apps. For instance, if a user sends a message such as "My house is flooded," the reception desk will receive that message. The reception desk can also obtain user location information using GPS data or Wi-Fi location information. For example, the reception desk can obtain GPS data from the user's smartphone to determine their location. The reception desk can also obtain location information using the user's Wi-Fi location information. As a result, the reception desk can obtain accurate disaster information by receiving user location information and damage reports.

[0036] The analysis unit can analyze user location information and damage reports using a generative AI. For example, the analysis unit inputs user location information and damage reports into the generative AI, which then analyzes the information. For instance, the generative AI analyzes a message such as "My house is flooded" to determine the precise location and extent of the damage. The generative AI can analyze user location information and damage reports using deep learning models and natural language processing technologies. For example, the generative AI uses a deep learning model to analyze user location information and determine the precise location. The generative AI can also use natural language processing technologies to analyze the user's damage report and understand the extent of the damage. As a result, the analysis unit can accurately and quickly analyze user location information and damage reports using the generative AI.

[0037] The notification unit can notify local governments based on information provided by the analysis unit. For example, the notification unit notifies local governments of information analyzed by the generation AI. For instance, the notification unit notifies local governments that "a home is flooded." The notification unit can also directly transmit information to local government systems. For example, the notification unit transmits information analyzed by the generation AI to the local government's disaster response system, enabling local governments to respond quickly. In this way, the notification unit can facilitate rapid rescue responses by notifying local governments based on information provided by the analysis unit.

[0038] The Integration Department can reflect information provided by the Notification Department into the local government system and integrate information in real time. For example, the Integration Department can integrate damage reports from multiple users to grasp the overall picture of the damage. For example, the Integration Department can organize damage reports from multiple users and respond efficiently. In addition, the Integration Department can update information in real time and provide the latest disaster information. For example, the Integration Department can update information on a second- or minute-by-minute basis, enabling local governments to respond based on the latest information. As a result, by integrating information provided by the Notification Department in real time, the Integration Department can organize disaster information received from multiple sources and respond efficiently.

[0039] The guidance unit can present the user with the optimal evacuation route based on information provided by the integration unit. For example, the guidance unit can use a generating AI to analyze damage information and send a message to the user such as, "The nearest evacuation center is XX. Please evacuate using this route." The guidance unit can also update the optimal evacuation route in real time based on the user's location and the extent of the damage. For example, the guidance unit can present the safest evacuation route based on the user's location. Furthermore, the guidance unit can change the evacuation route according to the extent of the damage to ensure the user's safe evacuation. In this way, the guidance unit can support the user's safe evacuation by presenting the user with the optimal evacuation route based on information provided by the integration unit.

[0040] The reception unit can analyze the user's past rescue request history and select the optimal reception method. For example, the reception unit can analyze patterns of rescue requests frequently made by the user in the past and propose the optimal reception method. The reception unit can also select the optimal reception method for a specific situation based on the user's past rescue request history. Furthermore, the reception unit can provide a quick and efficient reception method based on the user's past rescue request history. Thus, the reception unit can provide the optimal reception method by analyzing the user's past rescue request history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past rescue request history into a generating AI and have the generating AI select the optimal reception method.

[0041] The reception unit can filter rescue requests based on the user's current situation and environment. For example, the reception unit can provide the optimal method for receiving rescue requests based on the user's current location information. The reception unit can also analyze the user's surrounding environment information and select an appropriate method for receiving rescue requests. Furthermore, the reception unit can provide the optimal method for receiving rescue requests based on the user's current situation (e.g., weather, time of day). This allows the reception unit to receive more appropriate rescue requests by filtering based on the user's current situation and environment. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation and environment data into a generating AI and have the generating AI perform the filtering.

[0042] The reception unit can prioritize receiving rescue requests based on their relevance, taking into account the user's geographical location. For example, if the user's current location is close to the center of a disaster, the reception unit will prioritize receiving the rescue request. The reception unit can also prioritize requests from the most severely affected areas based on the user's location. Furthermore, the reception unit can analyze the user's geographical location and set the optimal priority for rescue requests. This allows the reception unit to prioritize receiving requests based on their relevance by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into a generating AI and have the generating AI prioritize the most relevant requests.

[0043] The reception department can analyze a user's social media activity when receiving a rescue request and accept relevant requests. For example, the reception department can analyze a user's social media posts and prioritize requests with high urgency. The reception department can also understand the current situation from the user's social media activity and provide an appropriate reception method. Furthermore, the reception department can accept relevant rescue requests based on the user's social media data. In this way, the reception department can accept relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the user's social media data into a generating AI and have the generating AI perform the reception of relevant requests.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the damage information during the analysis. For example, if the damage information is serious, the analysis unit will perform a detailed analysis to provide accurate information. Conversely, if the damage information is minor, the analysis unit can perform a concise analysis to provide information quickly. Furthermore, the analysis unit can set an appropriate level of detail of the analysis according to the importance of the damage information. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the damage information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input damage information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of damage information during the analysis. For example, in the case of flood damage, the analysis unit can apply an algorithm to analyze the depth and extent of the flooding. In the case of fire damage, the analysis unit can also apply an algorithm to analyze the spread and extent of the fire. Furthermore, in the case of earthquake damage, the analysis unit can apply an algorithm to analyze the degree of building collapse and the extent of the damage. In this way, the analysis unit can provide more accurate analysis results by applying different analysis algorithms depending on the category of damage information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input damage information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the timing of damage information submission during the analysis. For example, if damage information is submitted early, the analysis unit will prioritize the analysis. Furthermore, if damage information is submitted late, the analysis unit can prioritize other information of higher urgency. In addition, the analysis unit can set appropriate analysis priorities based on the timing of damage information submission. This allows the analysis unit to prioritize the analysis of information of higher urgency by determining the analysis priority based on the timing of damage information submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the timing of damage information submission into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the damage information during the analysis process. For example, the analysis unit can prioritize the analysis of damage information that is highly relevant to other information. It can also postpone the analysis of damage information that is less relevant to other information. Furthermore, the analysis unit can set an appropriate order of analysis based on the relevance of the damage information. In this way, the analysis unit can prioritize the analysis of highly relevant information by adjusting the order of analysis based on the relevance of the damage information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance data of the damage information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The notification unit can adjust the level of detail of a notification based on the importance of the damage information. For example, if the damage information is serious, the notification unit will provide a detailed notification. Conversely, if the damage information is minor, the notification unit can provide a concise notification. Furthermore, the notification unit can set an appropriate level of detail for the notification according to the importance of the damage information. This allows the notification unit to provide appropriate notifications by adjusting the level of detail based on the importance of the damage information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input damage information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail for the notification.

[0049] The notification unit can apply different notification algorithms depending on the category of damage information when issuing a notification. For example, in the case of flood damage, the notification unit can apply an algorithm to notify the depth and extent of the flooding. In the case of fire damage, the notification unit can also apply an algorithm to notify the spread of the fire and the extent of the damage. Furthermore, in the case of earthquake damage, the notification unit can apply an algorithm to notify the extent of building collapse and the extent of the damage. In this way, the notification unit can provide more accurate notifications by applying different notification algorithms depending on the category of damage information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI. For example, the notification unit can input damage information category data into a generating AI and have the generating AI execute the application of different notification algorithms.

[0050] The notification unit can adjust the order of notifications based on when the damage information was submitted. For example, if the damage information is submitted early, the notification unit will prioritize the notification. Also, if the damage information is submitted late, the notification unit can prioritize other information of higher urgency. Furthermore, the notification unit can set an appropriate order of notifications based on when the damage information was submitted. In this way, the notification unit can send notifications in the appropriate order by adjusting the order of notifications based on when the damage information was submitted. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input data on when the damage information was submitted into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0051] The notification unit can adjust the order of notifications based on the relevance of the damage information when sending notifications. For example, the notification unit will prioritize notifications if the damage information is highly relevant to other information. It can also postpone notifications if the damage information is less relevant to other information. Furthermore, the notification unit can set an appropriate order of notifications based on the relevance of the damage information. In this way, the notification unit can prioritize notifications for highly relevant information by adjusting the order of notifications based on the relevance of the damage information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the relevance of the damage information into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0052] The integration unit can adjust the level of detail of the integration based on the importance of the damage information during the integration process. For example, if the damage information is severe, the integration unit will perform detailed information integration. Conversely, if the damage information is minor, the integration unit can perform concise information integration. Furthermore, the integration unit can set an appropriate level of detail for information integration according to the importance of the damage information. This allows the integration unit to perform appropriate information integration by adjusting the level of detail of the integration based on the importance of the damage information. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input damage information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the integration.

[0053] The integration unit can apply different integration algorithms depending on the category of damage information during the integration process. For example, in the case of flood damage, the integration unit can apply an algorithm to integrate the depth and extent of the flooding. It can also apply an algorithm to integrate the spread and extent of the fire in the case of fire damage. Furthermore, in the case of earthquake damage, the integration unit can apply an algorithm to integrate the building collapse status and extent of the damage. This allows the integration unit to perform more accurate information integration by applying different integration algorithms depending on the category of damage information. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input damage information category data into a generating AI and have the generating AI execute the application of different integration algorithms.

[0054] The integration unit can adjust the order of integration based on the timing of the submission of damage information. For example, if damage information is submitted early, the integration unit will prioritize its integration. If damage information is submitted late, the integration unit can also prioritize other information of higher urgency. Furthermore, the integration unit can set an appropriate order of integration based on the timing of the submission of damage information. This allows the integration unit to integrate information in an appropriate order by adjusting the order of integration based on the timing of the submission of damage information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data on the timing of damage information submission into a generating AI and have the generating AI perform the adjustment of the integration order.

[0055] The integration unit can adjust the integration order based on the relevance of the damage information during the integration process. For example, the integration unit prioritizes the integration of damage information that is highly relevant to other information. It can also postpone the integration of damage information that is less relevant to other information. Furthermore, the integration unit can set an appropriate integration order based on the relevance of the damage information. This allows the integration unit to prioritize the integration of highly relevant information by adjusting the integration order based on the relevance of the damage information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input relevance data of the damage information into a generating AI and have the generating AI perform the adjustment of the integration order.

[0056] The guidance unit can adjust the level of detail of the evacuation route based on the importance of the damage information when presenting an evacuation route. For example, if the damage information is severe, the guidance unit will present a detailed evacuation route. Conversely, if the damage information is minor, the guidance unit can also present a simplified evacuation route. Furthermore, the guidance unit can set an appropriate level of detail for the evacuation route according to the importance of the damage information. In this way, the guidance unit can provide an appropriate evacuation route by adjusting the level of detail of the route based on the importance of the damage information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI. For example, the guidance unit can input damage information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the route.

[0057] The guidance unit can apply different route suggestion algorithms depending on the category of damage information when presenting evacuation routes. For example, in the case of flooding, the guidance unit can apply an algorithm that suggests a route that avoids flooding. In the case of fire damage, the guidance unit can also apply an algorithm that suggests a route that avoids fire. Furthermore, in the case of earthquake damage, the guidance unit can apply an algorithm that suggests a route with a low risk of collapse. In this way, the guidance unit can provide more appropriate evacuation routes by applying different route suggestion algorithms depending on the category of damage information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input damage information category data into a generating AI and cause the generating AI to execute the application of different route suggestion algorithms.

[0058] The guidance unit can adjust the order of evacuation routes based on the timing of damage information submission when presenting evacuation routes. For example, if damage information is submitted early, the guidance unit will prioritize presenting evacuation routes. If damage information is submitted late, the guidance unit can also prioritize other information of higher urgency. Furthermore, the guidance unit can set an appropriate order for evacuation routes based on the timing of damage information submission. In this way, the guidance unit can provide evacuation routes in the appropriate order by adjusting the order of routes based on the timing of damage information submission. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the timing of damage information submission into a generating AI and have the generating AI perform the adjustment of the route order.

[0059] The guidance unit can adjust the order of evacuation routes based on the relevance of damage information when presenting evacuation routes. For example, if damage information is highly relevant to other information, the guidance unit will prioritize presenting that evacuation route. Conversely, if damage information is less relevant to other information, the guidance unit may postpone presenting that route. Furthermore, the guidance unit can set an appropriate order for evacuation routes based on the relevance of the damage information. In this way, the guidance unit can prioritize providing highly relevant evacuation routes by adjusting the order of routes based on the relevance of the damage information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input relevance data of damage information into a generating AI and have the generating AI perform the adjustment of the route order.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception department can analyze a user's past rescue request history and select the optimal reception method. For example, it can analyze patterns of rescue requests frequently made by the user in the past and suggest the most suitable reception method. It can also select the optimal reception method for a specific situation based on the user's past rescue request history. Furthermore, it can provide a quick and efficient reception method based on the user's past rescue request history. In this way, the reception department can provide the optimal reception method by analyzing the user's past rescue request history.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the damage information during the analysis. For example, if the damage information is severe, a detailed analysis is performed to provide accurate information. Conversely, if the damage information is minor, a concise analysis can be performed to provide information quickly. Furthermore, the analysis unit can set an appropriate level of detail according to the importance of the damage information. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the damage information.

[0063] The notification unit can apply different notification algorithms depending on the category of damage information when issuing a notification. For example, in the case of flooding, it can apply an algorithm that notifies the depth and extent of the flooding. In the case of fire damage, it can also apply an algorithm that notifies the spread of the fire and the extent of the damage. Furthermore, in the case of earthquake damage, it can also apply an algorithm that notifies the extent of building collapse and the extent of the damage. As a result, the notification unit can provide more accurate notifications by applying different notification algorithms depending on the category of damage information.

[0064] The integration unit can adjust the order of integration based on when the damage information was submitted. For example, if damage information is submitted early, it will be prioritized for integration. Conversely, if damage information is submitted late, other information of higher urgency can be prioritized. Furthermore, the integration unit can set an appropriate order of integration based on when the damage information was submitted. As a result, the integration unit can integrate information in the appropriate order by adjusting the order of integration based on when the damage information was submitted.

[0065] The guidance unit can apply different route suggestion algorithms depending on the category of damage information when presenting evacuation routes. For example, in the case of flooding, it can apply an algorithm that suggests a route that avoids flooding. In the case of fire damage, it can also apply an algorithm that suggests a route that avoids fire. Furthermore, in the case of earthquake damage, it can apply an algorithm that suggests a route with a low risk of collapse. In this way, the guidance unit can provide more appropriate evacuation routes by applying different route suggestion algorithms depending on the category of damage information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives rescue requests from users. For example, it can receive rescue requests sent by users via messaging apps, as well as user location information and damage reports. When a user sends a message such as "My house is flooded," the reception desk receives that message. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit. For example, the user's location information and damage report are input into the generation AI, which then analyzes the information. The generation AI analyzes the message "My house is flooded" to determine the exact location and extent of the damage. Step 3: The notification unit notifies the local government of the appropriate location and situation based on the information analyzed by the analysis unit. For example, it notifies the local government of the information analyzed by the generation AI, such as "Your home is flooded." Step 4: The Integration Department reflects the information notified by the Notification Department into the local government system, integrating the information in real time. For example, it can integrate damage reports from multiple users to grasp the overall picture of the damage. Damage reports from multiple users can be organized and responded to efficiently. Step 5: The guidance unit presents the user with the optimal evacuation route based on the information integrated by the integration unit. For example, the generation AI can analyze damage information and send a message to the user such as, "The nearest evacuation center is XX. Please evacuate using this route."

[0068] (Example of form 2) The evacuation support system according to an embodiment of the present invention is a system that utilizes a generation AI to quickly process rescue requests from users via a messaging application and provide information to local governments in an appropriate format. This evacuation support system uses a generation AI to analyze rescue requests sent by users via a messaging application and grasp disaster information accurately and quickly. Next, based on the information analyzed by the generation AI, it quickly notifies local governments of the appropriate location and situation to facilitate rescue responses. Furthermore, it reflects data on damage reports and rescue requests via the messaging application in the local government system and integrates information in real time. Finally, based on the damage information, it constantly presents the user with the optimal evacuation route and supports safe evacuation. For example, the generation AI analyzes rescue requests sent by users via a messaging application. At this time, the user's location information and damage report are input to the generation AI. For example, if a user sends a message such as "My house is flooded," the generation AI analyzes the message and grasps the accurate location information and damage situation. Next, based on the information analyzed by the generation AI, it quickly notifies local governments of the appropriate location and situation. For example, the generating AI can analyze information such as "my home is flooded" and notify the local government of its location, enabling the government to respond quickly to rescue efforts. Furthermore, data from damage reports and rescue requests can be reflected in the local government system via messaging apps, integrating information in real time. This allows local governments to organize disaster information from multiple sources and respond efficiently. For example, damage reports from multiple users can be integrated to grasp the overall picture of the damage. Finally, based on the damage information, the system can always present the user with the most suitable evacuation route to support safe evacuation. For example, the generating AI can analyze damage information and send a message to the user such as, "The nearest evacuation center is XX. Please evacuate using this route," enabling the user to evacuate safely. This system supports real-time rescue operations and enables local governments to respond quickly. It also supports safe evacuation by presenting the user with the most suitable evacuation route. This minimizes damage during disasters.This allows the evacuation support system to quickly process users' rescue requests and provide information to local authorities in an appropriate format.

[0069] The evacuation support system according to this embodiment comprises a reception unit, an analysis unit, a notification unit, an integration unit, and a guidance unit. The reception unit receives rescue requests from users. The reception unit can, for example, receive rescue requests sent by users through messaging apps. The reception unit can also receive user location information and damage reports. For example, if a user sends a message such as "My house is flooded," the reception unit receives that message. The analysis unit analyzes the information received by the reception unit using a generation AI. The analysis unit inputs, for example, the user's location information and damage report into the generation AI, which then analyzes the information. For example, the generation AI analyzes the message "My house is flooded" and obtains accurate location information and damage status. The notification unit notifies the local government of the appropriate location and situation based on the information analyzed by the analysis unit. The notification unit can, for example, notify the local government of the information analyzed by the generation AI. For example, the notification unit notifies the local government of the information "My house is flooded." The integration unit reflects the information notified by the notification unit into the local government system, integrating the information in real time. The integration unit can, for example, integrate damage reports from multiple users to grasp the overall picture of the damage. For example, the integration unit can organize damage reports from multiple users and respond efficiently. The guidance unit presents the user with the optimal evacuation route based on the information integrated by the integration unit. For example, the guidance unit can use a generating AI to analyze damage information and send a message to the user such as, "The nearest evacuation center is XX. Please evacuate using this route." As a result, the evacuation support system according to the embodiment can quickly process the user's rescue request and provide information to the local government in an appropriate format.

[0070] The reception desk receives rescue requests from users. For example, the reception desk can receive rescue requests sent by users through messaging apps. Specifically, users can send rescue requests using a dedicated application on their smartphones or tablets. This application has an easy-to-use interface and has a function to send rescue requests with a single tap in emergencies. The reception desk can also receive users' location information and damage reports. For example, if a user sends a message such as "My house is flooded," the reception desk will receive the message. Location information is automatically obtained using the smartphone's GPS function, and damage reports are sent in the form of text messages, photos, videos, etc. This allows the reception desk to quickly collect detailed information from users and pass it on to the next processing step. Furthermore, the reception desk can also receive voice rescue requests using voice recognition technology. For example, if a user says "Help" aloud, the system can recognize the voice and receive it as a rescue request. This makes it easy for users with their hands full or those with visual impairments to request rescue.

[0071] The analysis unit uses a generation AI to analyze information received by the reception unit. For example, the analysis unit inputs user location information and damage reports into the generation AI, which then analyzes the information. Specifically, the generation AI uses natural language processing technology to analyze text messages from users and determine the nature and urgency of the damage. For example, it analyzes a message such as "My house is flooded" to estimate the extent of the flooding and the affected area. It also identifies the specific location where the damage occurred based on the location information. Furthermore, the generation AI uses image recognition technology to analyze photos and videos sent by users to confirm visual evidence of the damage. For example, it can determine the depth and extent of the flooding from images and grasp the detailed situation of the damage. This allows the analysis unit to analyze the collected information from multiple angles and grasp the accurate extent of the damage. In addition, the analysis unit can also utilize past data and statistical information to analyze damage patterns and trends. For example, based on past flood data, it can predict fluctuations in risk in specific areas and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0072] The notification unit notifies local governments of the appropriate locations and situations based on the information analyzed by the analysis unit. For example, the notification unit can notify local governments of information analyzed by a generation AI. Specifically, the notification unit automatically transmits the analyzed damage information to the local government's disaster response headquarters. The transmitted information includes detailed information about the damage, the location of the incident, and the urgency level, enabling local governments to respond quickly. The notification unit can also link with local government systems and update information in real time. For example, if the damage situation changes or a new rescue request is made, the notification unit immediately notifies the local government of this information, reflecting the latest situation. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, it can use email, SMS, and dedicated applications in combination to ensure that important information is delivered reliably. In this way, the notification unit can provide local governments with rapid and accurate information and support effective disaster response.

[0073] The Integration Department integrates information received by the Notification Department into the local government system, providing real-time information consolidation. For example, the Integration Department can consolidate damage reports from multiple users to grasp the overall picture of the damage. Specifically, the Integration Department works in conjunction with the local government's disaster response system to centrally manage the received damage information. This allows local governments to quickly grasp the scale and scope of the damage and take appropriate countermeasures. The Integration Department can also visualize damage information on a map, allowing for visual confirmation of the distribution of damage and evacuation routes. For example, it can map the locations of damage on a map to identify areas where damage is concentrated and areas requiring evacuation. Furthermore, the Integration Department can utilize historical data and statistical information to analyze damage patterns and trends. This allows local governments to obtain valuable information for formulating long-term disaster countermeasures and preventive measures. Because the Integration Department continuously updates information in real time, it can always grasp the latest situation. This enables the Integration Department to improve the disaster response capabilities of local governments and support a rapid and effective response.

[0074] The guidance unit presents the user with the optimal evacuation route based on information integrated by the integration unit. For example, the guidance unit can use a generating AI to analyze damage information and send a message to the user such as, "The nearest evacuation center is XX. Please evacuate using this route." Specifically, the guidance unit calculates the optimal evacuation route based on the user's current location and evacuation destination. The calculation considers road conditions, traffic information, and the extent of damage to select the safest and fastest route. Furthermore, the guidance unit can dynamically modify the evacuation route based on information updated in real time. For example, if new damage occurs or roads are blocked during evacuation, the guidance unit immediately calculates a new route and notifies the user. The guidance unit also provides means to ensure that users receive information reliably during evacuation, such as voice guidance and vibration notifications. This allows users to act with peace of mind even during evacuation. In addition, the guidance unit can select an appropriate evacuation destination by considering the congestion level and capacity of evacuation centers. This prevents overcrowding at evacuation centers and supports efficient evacuation. The guidance unit provides users with quick and accurate evacuation instructions, minimizing the risk of disaster.

[0075] The reception desk can receive user location information and damage reports. For example, the reception desk can receive location information and damage reports sent by users through messaging apps. For instance, if a user sends a message such as "My house is flooded," the reception desk will receive that message. The reception desk can also obtain user location information using GPS data or Wi-Fi location information. For example, the reception desk can obtain GPS data from the user's smartphone to determine their location. The reception desk can also obtain location information using the user's Wi-Fi location information. As a result, the reception desk can obtain accurate disaster information by receiving user location information and damage reports.

[0076] The analysis unit can analyze user location information and damage reports using a generative AI. For example, the analysis unit inputs user location information and damage reports into the generative AI, which then analyzes the information. For instance, the generative AI analyzes a message such as "My house is flooded" to determine the precise location and extent of the damage. The generative AI can analyze user location information and damage reports using deep learning models and natural language processing technologies. For example, the generative AI uses a deep learning model to analyze user location information and determine the precise location. The generative AI can also use natural language processing technologies to analyze the user's damage report and understand the extent of the damage. As a result, the analysis unit can accurately and quickly analyze user location information and damage reports using the generative AI.

[0077] The notification unit can notify local governments based on information provided by the analysis unit. For example, the notification unit notifies local governments of information analyzed by the generation AI. For instance, the notification unit notifies local governments that "a home is flooded." The notification unit can also directly transmit information to local government systems. For example, the notification unit transmits information analyzed by the generation AI to the local government's disaster response system, enabling local governments to respond quickly. In this way, the notification unit can facilitate rapid rescue responses by notifying local governments based on information provided by the analysis unit.

[0078] The Integration Department can reflect information provided by the Notification Department into the local government system and integrate information in real time. For example, the Integration Department can integrate damage reports from multiple users to grasp the overall picture of the damage. For example, the Integration Department can organize damage reports from multiple users and respond efficiently. In addition, the Integration Department can update information in real time and provide the latest disaster information. For example, the Integration Department can update information on a second- or minute-by-minute basis, enabling local governments to respond based on the latest information. As a result, by integrating information provided by the Notification Department in real time, the Integration Department can organize disaster information received from multiple sources and respond efficiently.

[0079] The guidance unit can present the user with the optimal evacuation route based on information provided by the integration unit. For example, the guidance unit can use a generating AI to analyze damage information and send a message to the user such as, "The nearest evacuation center is XX. Please evacuate using this route." The guidance unit can also update the optimal evacuation route in real time based on the user's location and the extent of the damage. For example, the guidance unit can present the safest evacuation route based on the user's location. Furthermore, the guidance unit can change the evacuation route according to the extent of the damage to ensure the user's safe evacuation. In this way, the guidance unit can support the user's safe evacuation by presenting the user with the optimal evacuation route based on information provided by the integration unit.

[0080] The reception unit can estimate the user's emotions and adjust the method of receiving rescue requests based on the estimated emotions. For example, if the user is in a state of panic, the reception unit can provide a simple and intuitive interface to enable them to make a rescue request quickly. If the user is calm, the reception unit can also provide an interface that allows them to input detailed information to make a more accurate rescue request. Furthermore, if the user is feeling anxious, the reception unit can display reassuring messages to support the rescue request process. In this way, the reception unit can make more appropriate rescue requests by adjusting the method of receiving rescue requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The reception unit can analyze the user's past rescue request history and select the optimal reception method. For example, the reception unit can analyze patterns of rescue requests frequently made by the user in the past and propose the optimal reception method. The reception unit can also select the optimal reception method for a specific situation based on the user's past rescue request history. Furthermore, the reception unit can provide a quick and efficient reception method based on the user's past rescue request history. Thus, the reception unit can provide the optimal reception method by analyzing the user's past rescue request history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past rescue request history into a generating AI and have the generating AI select the optimal reception method.

[0082] The reception unit can filter rescue requests based on the user's current situation and environment. For example, the reception unit can provide the optimal method for receiving rescue requests based on the user's current location information. The reception unit can also analyze the user's surrounding environment information and select an appropriate method for receiving rescue requests. Furthermore, the reception unit can provide the optimal method for receiving rescue requests based on the user's current situation (e.g., weather, time of day). This allows the reception unit to receive more appropriate rescue requests by filtering based on the user's current situation and environment. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation and environment data into a generating AI and have the generating AI perform the filtering.

[0083] The reception desk can estimate the user's emotions and determine the priority of rescue requests based on the estimated emotions. For example, if the user is in a state of panic, the reception desk will process the rescue request with the highest priority. If the user is calm, the reception desk can also prioritize other requests of higher urgency. Furthermore, if the user is feeling anxious, the reception desk can set priorities for a quicker response. This allows the reception desk to prioritize requests of higher urgency by determining the priority of rescue requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The reception unit can prioritize receiving rescue requests based on their relevance, taking into account the user's geographical location. For example, if the user's current location is close to the center of a disaster, the reception unit will prioritize receiving the rescue request. The reception unit can also prioritize requests from the most severely affected areas based on the user's location. Furthermore, the reception unit can analyze the user's geographical location and set the optimal priority for rescue requests. This allows the reception unit to prioritize receiving requests based on their relevance by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into a generating AI and have the generating AI prioritize the most relevant requests.

[0085] The reception department can analyze a user's social media activity when receiving a rescue request and accept relevant requests. For example, the reception department can analyze a user's social media posts and prioritize requests with high urgency. The reception department can also understand the current situation from the user's social media activity and provide an appropriate reception method. Furthermore, the reception department can accept relevant rescue requests based on the user's social media data. In this way, the reception department can accept relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the user's social media data into a generating AI and have the generating AI perform the reception of relevant requests.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is in a state of panic, the analysis unit can provide a concise and intuitive analysis result. If the user is calm, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is feeling anxious, the analysis unit can provide a reassuring analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the damage information during the analysis. For example, if the damage information is serious, the analysis unit will perform a detailed analysis to provide accurate information. Conversely, if the damage information is minor, the analysis unit can perform a concise analysis to provide information quickly. Furthermore, the analysis unit can set an appropriate level of detail of the analysis according to the importance of the damage information. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the damage information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input damage information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of damage information during the analysis. For example, in the case of flood damage, the analysis unit can apply an algorithm to analyze the depth and extent of the flooding. In the case of fire damage, the analysis unit can also apply an algorithm to analyze the spread and extent of the fire. Furthermore, in the case of earthquake damage, the analysis unit can apply an algorithm to analyze the degree of building collapse and the extent of the damage. In this way, the analysis unit can provide more accurate analysis results by applying different analysis algorithms depending on the category of damage information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input damage information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a state of panic, the analysis unit can provide a short, concise analysis result. If the user is calm, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is feeling anxious, the analysis unit can provide a reassuring analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The analysis unit can determine the priority of analysis based on the timing of damage information submission during the analysis. For example, if damage information is submitted early, the analysis unit will prioritize the analysis. Furthermore, if damage information is submitted late, the analysis unit can prioritize other information of higher urgency. In addition, the analysis unit can set appropriate analysis priorities based on the timing of damage information submission. This allows the analysis unit to prioritize the analysis of information of higher urgency by determining the analysis priority based on the timing of damage information submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the timing of damage information submission into a generating AI and have the generating AI determine the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the damage information during the analysis process. For example, the analysis unit can prioritize the analysis of damage information that is highly relevant to other information. It can also postpone the analysis of damage information that is less relevant to other information. Furthermore, the analysis unit can set an appropriate order of analysis based on the relevance of the damage information. In this way, the analysis unit can prioritize the analysis of highly relevant information by adjusting the order of analysis based on the relevance of the damage information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance data of the damage information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is in a state of panic, the notification unit can provide a concise and intuitive notification. If the user is calm, the notification unit can also provide a detailed notification. Furthermore, if the user is feeling anxious, the notification unit can provide a reassuring notification. In this way, the notification unit can provide more appropriate notifications by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The notification unit can adjust the level of detail of a notification based on the importance of the damage information. For example, if the damage information is serious, the notification unit will provide a detailed notification. Conversely, if the damage information is minor, the notification unit can provide a concise notification. Furthermore, the notification unit can set an appropriate level of detail for the notification according to the importance of the damage information. This allows the notification unit to provide appropriate notifications by adjusting the level of detail based on the importance of the damage information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input damage information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail for the notification.

[0094] The notification unit can apply different notification algorithms depending on the category of damage information when issuing a notification. For example, in the case of flood damage, the notification unit can apply an algorithm to notify the depth and extent of the flooding. In the case of fire damage, the notification unit can also apply an algorithm to notify the spread of the fire and the extent of the damage. Furthermore, in the case of earthquake damage, the notification unit can apply an algorithm to notify the extent of building collapse and the extent of the damage. In this way, the notification unit can provide more accurate notifications by applying different notification algorithms depending on the category of damage information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI. For example, the notification unit can input damage information category data into a generating AI and have the generating AI execute the application of different notification algorithms.

[0095] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is in a state of panic, the notification unit will give the highest priority to the notification. If the user is calm, the notification unit can also prioritize other notifications of higher urgency. Furthermore, if the user is feeling anxious, the notification unit can set priorities for quicker responses. In this way, the notification unit can prioritize notifications of higher urgency by determining notification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The notification unit can adjust the order of notifications based on when the damage information was submitted. For example, if the damage information is submitted early, the notification unit will prioritize the notification. Also, if the damage information is submitted late, the notification unit can prioritize other information of higher urgency. Furthermore, the notification unit can set an appropriate order of notifications based on when the damage information was submitted. In this way, the notification unit can send notifications in the appropriate order by adjusting the order of notifications based on when the damage information was submitted. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input data on when the damage information was submitted into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0097] The notification unit can adjust the order of notifications based on the relevance of the damage information when sending notifications. For example, the notification unit will prioritize notifications if the damage information is highly relevant to other information. It can also postpone notifications if the damage information is less relevant to other information. Furthermore, the notification unit can set an appropriate order of notifications based on the relevance of the damage information. In this way, the notification unit can prioritize notifications for highly relevant information by adjusting the order of notifications based on the relevance of the damage information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the relevance of the damage information into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0098] The integration unit can estimate the user's emotions and adjust the method of information integration based on the estimated user emotions. For example, if the user is in a state of panic, the integration unit can perform concise and intuitive information integration. If the user is calm, the integration unit can also perform detailed information integration. Furthermore, if the user is feeling anxious, the integration unit can perform reassuring information integration. In this way, the integration unit can perform more appropriate information integration by adjusting the method of information integration according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The integration unit can adjust the level of detail of the integration based on the importance of the damage information during the integration process. For example, if the damage information is severe, the integration unit will perform detailed information integration. Conversely, if the damage information is minor, the integration unit can perform concise information integration. Furthermore, the integration unit can set an appropriate level of detail for information integration according to the importance of the damage information. This allows the integration unit to perform appropriate information integration by adjusting the level of detail of the integration based on the importance of the damage information. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input damage information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the integration.

[0100] The integration unit can apply different integration algorithms depending on the category of damage information during the integration process. For example, in the case of flood damage, the integration unit can apply an algorithm to integrate the depth and extent of the flooding. It can also apply an algorithm to integrate the spread and extent of the fire in the case of fire damage. Furthermore, in the case of earthquake damage, the integration unit can apply an algorithm to integrate the building collapse status and extent of the damage. This allows the integration unit to perform more accurate information integration by applying different integration algorithms depending on the category of damage information. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input damage information category data into a generating AI and have the generating AI execute the application of different integration algorithms.

[0101] The integration unit can estimate the user's emotions and determine the priority of integration based on the estimated emotions. For example, if the user is in a state of panic, the integration unit will prioritize integration. If the user is calm, the integration unit can also prioritize other high-urgency integrations. Furthermore, if the user is feeling anxious, the integration unit can set priorities for rapid response. In this way, by determining the priority of integration according to the user's emotions, the integration unit can prioritize the integration of high-urgency information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, or not using AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The integration unit can adjust the order of integration based on the timing of the submission of damage information. For example, if damage information is submitted early, the integration unit will prioritize its integration. If damage information is submitted late, the integration unit can also prioritize other information of higher urgency. Furthermore, the integration unit can set an appropriate order of integration based on the timing of the submission of damage information. This allows the integration unit to integrate information in an appropriate order by adjusting the order of integration based on the timing of the submission of damage information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data on the timing of damage information submission into a generating AI and have the generating AI perform the adjustment of the integration order.

[0103] The integration unit can adjust the integration order based on the relevance of the damage information during the integration process. For example, the integration unit prioritizes the integration of damage information that is highly relevant to other information. It can also postpone the integration of damage information that is less relevant to other information. Furthermore, the integration unit can set an appropriate integration order based on the relevance of the damage information. This allows the integration unit to prioritize the integration of highly relevant information by adjusting the integration order based on the relevance of the damage information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input relevance data of the damage information into a generating AI and have the generating AI perform the adjustment of the integration order.

[0104] The guidance unit can estimate the user's emotions and adjust the method of presenting evacuation routes based on the estimated emotions. For example, if the user is in a state of panic, the guidance unit can present a simple and intuitive evacuation route. If the user is calm, the guidance unit can also present a detailed evacuation route. Furthermore, if the user is feeling anxious, the guidance unit can present an evacuation route that provides a sense of security. In this way, the guidance unit can provide a more appropriate evacuation route by adjusting the method of presenting evacuation routes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The guidance unit can adjust the level of detail of the evacuation route based on the importance of the damage information when presenting an evacuation route. For example, if the damage information is severe, the guidance unit will present a detailed evacuation route. Conversely, if the damage information is minor, the guidance unit can also present a simplified evacuation route. Furthermore, the guidance unit can set an appropriate level of detail for the evacuation route according to the importance of the damage information. In this way, the guidance unit can provide an appropriate evacuation route by adjusting the level of detail of the route based on the importance of the damage information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI. For example, the guidance unit can input damage information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the route.

[0106] The guidance unit can apply different route suggestion algorithms depending on the category of damage information when presenting evacuation routes. For example, in the case of flooding, the guidance unit can apply an algorithm that suggests a route that avoids flooding. In the case of fire damage, the guidance unit can also apply an algorithm that suggests a route that avoids fire. Furthermore, in the case of earthquake damage, the guidance unit can apply an algorithm that suggests a route with a low risk of collapse. In this way, the guidance unit can provide more appropriate evacuation routes by applying different route suggestion algorithms depending on the category of damage information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input damage information category data into a generating AI and cause the generating AI to execute the application of different route suggestion algorithms.

[0107] The guidance unit can estimate the user's emotions and determine the priority of evacuation routes based on the estimated emotions. For example, if the user is in a state of panic, the guidance unit will present the evacuation route with the highest priority. If the user is calm, the guidance unit can also prioritize other evacuation routes of higher urgency. Furthermore, if the user is feeling anxious, the guidance unit can set priorities for quick response. In this way, by determining the priority of evacuation routes according to the user's emotions, the guidance unit can prioritize providing high-urgency evacuation routes. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The guidance unit can adjust the order of evacuation routes based on the timing of damage information submission when presenting evacuation routes. For example, if damage information is submitted early, the guidance unit will prioritize presenting evacuation routes. If damage information is submitted late, the guidance unit can also prioritize other information of higher urgency. Furthermore, the guidance unit can set an appropriate order for evacuation routes based on the timing of damage information submission. In this way, the guidance unit can provide evacuation routes in the appropriate order by adjusting the order of routes based on the timing of damage information submission. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the timing of damage information submission into a generating AI and have the generating AI perform the adjustment of the route order.

[0109] The guidance unit can adjust the order of evacuation routes based on the relevance of damage information when presenting evacuation routes. For example, if damage information is highly relevant to other information, the guidance unit will prioritize presenting that evacuation route. Conversely, if damage information is less relevant to other information, the guidance unit may postpone presenting that route. Furthermore, the guidance unit can set an appropriate order for evacuation routes based on the relevance of the damage information. In this way, the guidance unit can prioritize providing highly relevant evacuation routes by adjusting the order of routes based on the relevance of the damage information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input relevance data of damage information into a generating AI and have the generating AI perform the adjustment of the route order.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can estimate the user's emotions and adjust the rescue request processing method based on those emotions. For example, if the user is in a state of panic, it can provide a simple and intuitive interface to enable them to quickly request rescue. If the user is calm, it can provide an interface that allows them to enter detailed information to enable them to make a more accurate rescue request. Furthermore, if the user is feeling anxious, it can display reassuring messages to support the rescue request process. In this way, the reception desk can make more appropriate rescue requests by adjusting the rescue request processing method according to the user's emotions.

[0112] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is in a state of panic, it can provide concise and intuitive analysis results. If the user is calm, it can provide detailed analysis results. Furthermore, if the user is feeling anxious, it can provide reassuring analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions.

[0113] The notification unit can estimate the user's emotions and adjust the notification method based on those emotions. For example, if the user is panicking, it can provide a concise and intuitive notification. If the user is calm, it can provide a detailed notification. Furthermore, if the user is feeling anxious, it can provide a reassuring notification. In this way, the notification unit can provide more appropriate notifications by adjusting the notification method according to the user's emotions.

[0114] The integration unit can estimate the user's emotions and adjust the information integration method based on those emotions. For example, if the user is in a state of panic, it can perform concise and intuitive information integration. If the user is calm, it can perform detailed information integration. Furthermore, if the user is feeling anxious, it can perform information integration that provides a sense of security. In this way, the integration unit can perform more appropriate information integration by adjusting the information integration method according to the user's emotions.

[0115] The guidance unit can estimate the user's emotions and adjust the method of presenting evacuation routes based on those emotions. For example, if the user is in a state of panic, it can present a simple and intuitive evacuation route. If the user is calm, it can present a detailed evacuation route. Furthermore, if the user is feeling anxious, it can present an evacuation route that provides a sense of security. In this way, the guidance unit can provide a more appropriate evacuation route by adjusting the method of presenting evacuation routes according to the user's emotions.

[0116] The reception department can analyze a user's past rescue request history and select the optimal reception method. For example, it can analyze patterns of rescue requests frequently made by the user in the past and suggest the most suitable reception method. It can also select the optimal reception method for a specific situation based on the user's past rescue request history. Furthermore, it can provide a quick and efficient reception method based on the user's past rescue request history. In this way, the reception department can provide the optimal reception method by analyzing the user's past rescue request history.

[0117] The analysis unit can adjust the level of detail of the analysis based on the importance of the damage information during the analysis. For example, if the damage information is severe, a detailed analysis is performed to provide accurate information. Conversely, if the damage information is minor, a concise analysis can be performed to provide information quickly. Furthermore, the analysis unit can set an appropriate level of detail according to the importance of the damage information. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the damage information.

[0118] The notification unit can apply different notification algorithms depending on the category of damage information when issuing a notification. For example, in the case of flooding, it can apply an algorithm that notifies the depth and extent of the flooding. In the case of fire damage, it can also apply an algorithm that notifies the spread of the fire and the extent of the damage. Furthermore, in the case of earthquake damage, it can also apply an algorithm that notifies the extent of building collapse and the extent of the damage. As a result, the notification unit can provide more accurate notifications by applying different notification algorithms depending on the category of damage information.

[0119] The integration unit can adjust the order of integration based on when the damage information was submitted. For example, if damage information is submitted early, it will be prioritized for integration. Conversely, if damage information is submitted late, other information of higher urgency can be prioritized. Furthermore, the integration unit can set an appropriate order of integration based on when the damage information was submitted. As a result, the integration unit can integrate information in the appropriate order by adjusting the order of integration based on when the damage information was submitted.

[0120] The guidance unit can apply different route suggestion algorithms depending on the category of damage information when presenting evacuation routes. For example, in the case of flooding, it can apply an algorithm that suggests a route that avoids flooding. In the case of fire damage, it can also apply an algorithm that suggests a route that avoids fire. Furthermore, in the case of earthquake damage, it can apply an algorithm that suggests a route with a low risk of collapse. In this way, the guidance unit can provide more appropriate evacuation routes by applying different route suggestion algorithms depending on the category of damage information.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives rescue requests from users. For example, it can receive rescue requests sent by users via messaging apps, as well as user location information and damage reports. When a user sends a message such as "My house is flooded," the reception desk receives that message. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit. For example, the user's location information and damage report are input into the generation AI, which then analyzes the information. The generation AI analyzes the message "My house is flooded" to determine the exact location and extent of the damage. Step 3: The notification unit notifies the local government of the appropriate location and situation based on the information analyzed by the analysis unit. For example, it notifies the local government of the information analyzed by the generation AI, such as "Your home is flooded." Step 4: The Integration Department reflects the information notified by the Notification Department into the local government system, integrating the information in real time. For example, it can integrate damage reports from multiple users to grasp the overall picture of the damage. Damage reports from multiple users can be organized and responded to efficiently. Step 5: The guidance unit presents the user with the optimal evacuation route based on the information integrated by the integration unit. For example, the generation AI can analyze damage information and send a message to the user such as, "The nearest evacuation center is XX. Please evacuate using this route."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, notification unit, integration unit, and guidance unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives rescue requests from users. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information using a generating AI. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and notifies the local government of the analyzed information. The integration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reflects the notified information in the local government system and integrates the information in real time. The guidance unit is implemented by, for example, the output device 40 of the smart device 14 and presents the user with the optimal evacuation route. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, notification unit, integration unit, and guidance unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives rescue requests from the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information using a generating AI. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and notifies the local government of the analyzed information. The integration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reflects the notified information in the local government system and integrates the information in real time. The guidance unit is implemented by, for example, the speaker 240 of the smart glasses 214 and presents the user with the optimal evacuation route. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, notification unit, integration unit, and guidance unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives rescue requests from users. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information using a generating AI. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and notifies the local government of the analyzed information. The integration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reflects the notified information in the local government system and integrates the information in real time. The guidance unit is implemented by, for example, the display 343 of the headset terminal 314 and presents the user with the optimal evacuation route. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, analysis unit, notification unit, integration unit, and guidance unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives rescue requests from users. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information using a generating AI. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and notifies the local government of the analyzed information. The integration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reflects the notified information in the local government system and integrates the information in real time. The guidance unit is implemented by, for example, the speaker 240 of the robot 414 and presents the user with the optimal evacuation route. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception desk that receives rescue requests from users, An analysis unit that analyzes the information received by the reception unit, A notification unit that notifies the local government of the appropriate location and situation based on the information analyzed by the aforementioned analysis unit, The integration unit reflects the information notified by the aforementioned notification unit into the local government system and integrates the information in real time. The system includes a guidance unit that presents the user with the optimal evacuation route based on the information integrated by the aforementioned integration unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept user location information and damage reports. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The generated AI analyzes user location information and damage reports. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Based on the information provided by the analysis department, we will notify local governments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned integration unit is The information provided by the notification department will be reflected in the local government system, integrating the information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned induction unit is Based on information provided by the integration department, the system will present the user with the most suitable evacuation route. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of accepting rescue requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past rescue request history and selects the most suitable method of receiving the request. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a rescue request, filtering is performed based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of rescue requests to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving rescue requests, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a rescue request, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the damage information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of damage information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the damage information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the damage information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When sending a notification, adjust the level of detail in the notification based on the importance of the damage information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the category of the damage information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When notifying, the order of notifications will be adjusted based on when the damage information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, the order of notifications will be adjusted based on the relevance of the damage information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned integration unit is It estimates the user's emotions and adjusts the information integration method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned integration unit is During integration, adjust the level of detail based on the importance of the damage information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned integration unit is During integration, different integration algorithms are applied depending on the category of damage information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned integration unit is It estimates user sentiment and determines integration priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned integration unit is During the integration process, the order of integration will be adjusted based on when the damage information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned integration unit is During integration, the order of integration is adjusted based on the relevance of the damage information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned induction unit is The system estimates the user's emotions and adjusts the method of presenting evacuation routes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned induction unit is When presenting evacuation routes, adjust the level of detail in the route based on the importance of the damage information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned induction unit is When presenting evacuation routes, different route suggestion algorithms are applied depending on the category of damage information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned induction unit is The system estimates the user's emotions and prioritizes evacuation routes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned induction unit is When presenting evacuation routes, the order of the routes will be adjusted based on when damage information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned induction unit is When presenting evacuation routes, the order of the routes will be adjusted based on the relevance of the damage information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives rescue requests from users, An analysis unit that analyzes the information received by the reception unit, A notification unit that notifies the local government of the appropriate location and situation based on the information analyzed by the aforementioned analysis unit, The integration unit reflects the information notified by the aforementioned notification unit into the local government system and integrates the information in real time. The system includes a guidance unit that presents the user with the optimal evacuation route based on the information integrated by the aforementioned integration unit. A system characterized by the following features.

2. The aforementioned reception unit is We accept user location information and damage reports. The system according to feature 1.

3. The aforementioned analysis unit, The generated AI analyzes user location information and damage reports. The system according to feature 1.

4. The aforementioned notification unit, Based on the information provided by the aforementioned analysis unit, the local government will be notified. The system according to feature 1.

5. The aforementioned integration unit is The information provided by the aforementioned notification unit is reflected in the local government system, and the information is integrated in real time. The system according to feature 1.

6. The aforementioned induction unit is Based on the information provided by the aforementioned integration unit, the system presents the user with the most suitable evacuation route. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of accepting rescue requests based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past rescue request history and selects the most suitable method of receiving the request. The system according to feature 1.

9. The aforementioned reception unit is When receiving a rescue request, filtering is performed based on the user's current situation and environment. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of rescue requests to be accepted based on those estimated emotions. The system according to feature 1.

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