system

Abstract

The system according to the embodiment aims to promptly and appropriately request rescue, provide psychological support, and give instructions on first aid when an earthquake occurs. [Solution] A system according to an embodiment includes a detection unit, a launch unit, a recognition unit, a request unit, a support unit, and an instruction unit. The detection unit detects the occurrence of an earthquake. The launch unit automatically launches an app based on the earthquake detected by the detection unit. The recognition unit recognizes the user's voice. The request unit makes a rescue request based on the voice recognized by the recognition unit. The support unit provides psychological support. The instruction unit gives first aid instructions.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to quickly and appropriately request rescue, provide psychological support, and give first aid instructions when an earthquake occurs.

[0005] The system according to the embodiment aims to promptly and appropriately request rescue, provide psychological support, and give instructions on first aid when an earthquake occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, a launch unit, a recognition unit, a request unit, a support unit, and an instruction unit. The detection unit detects the occurrence of an earthquake. The launch unit automatically launches an app based on the earthquake detected by the detection unit. The recognition unit recognizes the user's voice. The request unit makes a rescue request based on the voice recognized by the recognition unit. The support unit provides psychological support. The instruction unit gives first aid instructions. [Effects of the Invention]

[0007] The system according to the embodiment can promptly and appropriately request rescue, provide psychological support, and give instructions on first aid when an earthquake occurs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A disaster support system according to an embodiment of the present invention detects an earthquake, responds to a user's voice to request rescue, and provides psychological support and first aid instructions. The disaster support system detects an earthquake, automatically launches an app, and responds to the user's voice to request rescue. The disaster support system also realizes mutual assistance through cooperation with the local community and utilizes edge AI to provide psychological support and first aid instructions. For example, the disaster support system automatically activates when an earthquake with a seismic intensity of 7 or higher occurs. For example, the occurrence of an earthquake can be detected using a seismometer or a smartphone sensor. Next, the disaster support system responds to the user's voice to request rescue. For example, when a user yells "help," the disaster support system recognizes the voice and requests rescue. At this time, the disaster support system can acquire the user's location information and notify the nearest rescue team. This enables rapid rescue operations. Furthermore, the disaster support system realizes mutual assistance through cooperation with the local community. For example, a user can request help from neighboring residents through the disaster support system. This promotes mutual assistance throughout the community and ensures efficient disaster relief. In addition, the disaster support system can utilize edge AI to provide psychological support and first aid instructions. For example, if a user panics, the disaster support system can provide appropriate psychological support. Furthermore, if first aid is required, the disaster support system can provide specific instructions. This allows the user to respond calmly. In this way, the disaster support system automatically activates when an earthquake occurs, responds to the user's voice to request rescue, and realizes mutual assistance through cooperation with the local community. Furthermore, because it can utilize edge AI to provide psychological support and first aid instructions, it is an essential application in the event of a disaster. This allows the disaster support system to quickly and accurately request rescue when an earthquake occurs and provide psychological support and first aid instructions. For example, even if a user panics, they can receive appropriate support and respond calmly. Furthermore, cooperation with the local community enables efficient relief activities.

[0029] A disaster support system according to an embodiment includes a detection unit, a launch unit, a recognition unit, a request unit, a support unit, and an instruction unit. The detection unit detects the occurrence of an earthquake. The earthquake can be detected, for example, using a seismometer or a smartphone sensor. For example, the seismometer detects seismic waves and measures seismic intensity. The smartphone sensor can detect earthquake tremors using an acceleration sensor or a gyro sensor. The launch unit automatically launches an app based on the earthquake detected by the detection unit. For example, the app can be automatically launched when an earthquake with a seismic intensity of 7 or higher is detected. The recognition unit recognizes a user's voice. Voice recognition technology can be used to recognize the user's voice. For example, voice recognition technology can recognize a user's voice yelling "help" and issue a rescue request. The request unit issues a rescue request based on the voice recognized by the recognition unit. For example, the request unit can obtain the user's location information and notify the nearest rescue team. The support unit provides psychological support. For example, if the user is in a panic, the support unit can provide appropriate psychological support. For example, the support unit can calm the user using relaxation techniques. The instruction unit provides first aid instructions. For example, if first aid is needed, the instruction unit can provide specific instructions on procedures. For example, the instruction unit can provide instructions on first aid procedures such as how to stop bleeding and cardiopulmonary resuscitation. As a result, the disaster support system according to the embodiment can automatically start up when an earthquake occurs, request rescue in response to the user's voice, and provide psychological support and first aid instructions.

[0030] The request unit can acquire the user's location information and notify the nearest rescue team. The request unit can acquire the user's location information using, for example, GPS. For example, the request unit can identify the user's current location using the GPS function of a smartphone. The request unit can also acquire the user's location information using Wi-Fi location information. For example, the request unit can identify the user's location based on information about surrounding Wi-Fi access points. The request unit can also acquire the user's location information using Bluetooth (registered trademark) location information. For example, the request unit can identify the user's location based on information about surrounding Bluetooth devices. This enables rapid rescue operations by acquiring the user's location information and quickly notifying the nearest rescue team. Some or all of the above-described processing in the request unit can be performed using, for example, AI, or without AI. For example, the request unit can input the user's location information into AI and cause the AI ​​to execute a process of selecting the most appropriate rescue team.

[0031] The support unit can provide appropriate psychological support when a user falls into a panic state. For example, the support unit can monitor the user's heart rate and irregular breathing to determine whether the user is in a panic state. For example, the support unit can measure the user's heart rate using a smartphone sensor and detect an increase in the heart rate. The support unit can also use the smartphone's microphone to detect irregular breathing. For example, the support unit can analyze the user's breathing sounds and detect irregular breathing. The support unit can provide appropriate psychological support to a user who falls into a panic state. For example, the support unit can calm the user using relaxation techniques. For example, the support unit can instruct the user to take deep breaths to relieve tension. The support unit can also play relaxing music to calm the user. Furthermore, the support unit can provide an encouraging message to the user. For example, the support unit can display a message such as "It's okay, please stay calm." This allows the user to remain calm by providing appropriate psychological support when the user falls into a panic state. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input the user's heart rate data into AI and have the AI ​​determine whether the user is in a panic state.

[0032] The instruction unit can instruct specific procedures when first aid is needed. The instruction unit instructs first aid procedures, for example, when a user is injured. For example, the instruction unit can instruct how to stop bleeding. For example, the instruction unit can display specific procedures such as "apply pressure to the wound with a clean cloth." The instruction unit can also instruct cardiopulmonary resuscitation procedures. For example, the instruction unit can display specific procedures such as "perform chest compressions." The instruction unit can also instruct first aid procedures for fractures. For example, the instruction unit can display specific procedures such as "immobilize the fractured area." This allows the user to perform appropriate first aid by instructing specific procedures when first aid is needed. Some or all of the above-described processing in the instruction unit may be performed using, or without, AI. For example, the instruction unit can input the state of the user's injury into AI and cause the AI ​​to generate optimal first aid procedures.

[0033] The disaster support system further includes a cooperation unit that promotes cooperation with the local area and community. The cooperation unit promotes cooperation with the local area and community. For example, the cooperation unit can request help from nearby residents during a disaster. For example, the cooperation unit can send a notification to nearby residents via an app to request help. The cooperation unit can also work with local volunteers to carry out support activities. For example, the cooperation unit can request support from local volunteers and ask for their cooperation. The cooperation unit can also carry out support activities in cooperation with local community centers. For example, the cooperation unit can request support from community centers and ask for their cooperation. This promotes cooperation with the local area and community, thereby efficiently providing support during a disaster. Some or all of the above-mentioned processing in the cooperation unit may be performed using AI, or may be performed without AI. For example, the cooperation unit can input local resident data into AI and have the AI ​​generate an optimal cooperation method.

[0034] The disaster support system further includes a location information acquisition unit that acquires user location information. The location information acquisition unit acquires the user location information. For example, the location information acquisition unit can acquire the user location information using GPS. For example, the location information acquisition unit can identify the user's current location using the GPS function of a smartphone. The location information acquisition unit can also acquire the user location information using Wi-Fi location information. For example, the location information acquisition unit can identify the user's location based on information about surrounding Wi-Fi access points. The location information acquisition unit can also acquire the user location information using Bluetooth location information. For example, the location information acquisition unit can identify the user's location based on information about surrounding Bluetooth devices. By acquiring the user's location information, accurate location information can be provided when requesting rescue. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the location information acquisition unit can input the user's location information into AI and cause the AI ​​to execute a process of selecting the most appropriate rescue team.

[0035] The detection unit can detect precursory phenomena of an earthquake and issue a warning in advance. For example, the detection unit can analyze seismometer data in real time to detect precursory phenomena. For example, the detection unit can analyze seismometer data and detect changes in seismic waves. The detection unit can also use a smartphone's sensors to detect abnormal vibrations or sounds and issue a warning as a precursor to an earthquake. For example, the detection unit can detect abnormal vibrations or sounds using the smartphone's acceleration sensor or microphone. The detection unit can also analyze weather data to detect abnormal weather that is a precursor to an earthquake. For example, the detection unit can analyze weather data and detect abnormal changes in temperature or air pressure. This allows the detection of precursory phenomena of an earthquake and issue a warning in advance, allowing the user to take preventive measures. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input seismometer data into a generation AI and cause the generation AI to detect precursory phenomena.

[0036] The detection unit can improve detection accuracy by integrating data from multiple sensors. For example, the detection unit can integrate data from a smartphone's acceleration sensor and seismometer to detect the occurrence of an earthquake with high accuracy. For example, the detection unit can integrate data from a smartphone's acceleration sensor and seismometer to detect the occurrence of an earthquake. The detection unit can also integrate sensor data from multiple smartphones in the cloud to detect the occurrence of an earthquake. For example, the detection unit can transmit sensor data from multiple smartphones to the cloud and integrate the data in the cloud to detect the occurrence of an earthquake. Furthermore, the detection unit can integrate data from a seismometer, weather data, and a smartphone sensor to detect the occurrence of an earthquake with high accuracy. For example, the detection unit can integrate data from a seismometer, weather data, and smartphone sensor data to detect the occurrence of an earthquake. In this way, by integrating data from multiple sensors, the accuracy of earthquake detection is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input multiple sensor data to a generation AI and cause the generation AI to integrate the data and detect earthquakes.

[0037] The detection unit can optimize the detection algorithm by referring to past earthquake data. The detection unit, for example, analyzes past earthquake data, learns earthquake occurrence patterns, and optimizes the detection algorithm. For example, the detection unit can analyze past earthquake data and learn earthquake occurrence patterns. The detection unit can also evaluate the earthquake occurrence risk of a specific area based on the past earthquake data and adjust the detection algorithm. For example, the detection unit can evaluate the earthquake occurrence risk of a specific area based on the past earthquake data and adjust the detection algorithm. Furthermore, the detection unit can share past earthquake data on the cloud and integrate it with other users' data to optimize the detection algorithm. For example, the detection unit can upload past earthquake data to the cloud and integrate it with other users' data to optimize the detection algorithm. This optimizes the detection algorithm by referring to past earthquake data, improving the accuracy of earthquake detection. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input past earthquake data to a generation AI and cause the generation AI to optimize the detection algorithm.

[0038] The launch unit can automatically notify emergency contacts when an earthquake occurs, simultaneously with the launch of the app. The launch unit, for example, can send an SMS notification to emergency contacts when an earthquake occurs, simultaneously with the launch of the app. For example, the launch unit can send an SMS notification to emergency contacts when an earthquake occurs, simultaneously with the launch of the app. The launch unit can also send an email notification to emergency contacts when an earthquake occurs, simultaneously with the launch of the app. For example, the launch unit can send an email notification to emergency contacts when an earthquake occurs, simultaneously with the launch of the app. The launch unit can also notify emergency contacts by phone when an earthquake occurs, simultaneously with the launch of the app. For example, the launch unit can notify emergency contacts by phone when an earthquake occurs, simultaneously with the launch of the app. This enables a rapid response by automatically notifying emergency contacts when an earthquake occurs, simultaneously with the launch of the app. Some or all of the above-described processing in the launch unit may be performed using, for example, AI, or may be performed without AI. For example, the launch unit can input notification content when an earthquake occurs into a generation AI and cause the generation AI to execute the optimal notification method.

[0039] The launch unit can select an optimal launch mode depending on the user's current activity status. For example, when the user is walking, the launch unit launches the app in lightweight mode to quickly provide information. For example, when the user is walking, the launch unit can launch the app in lightweight mode to quickly provide information. Furthermore, when the user is in a car, the launch unit can launch the app in driving mode to provide information suitable for driving. For example, when the user is in a car, the launch unit can launch the app in driving mode to provide information suitable for driving. Furthermore, when the user is at home, the launch unit can launch the app in home mode to provide information on measures to take at home. For example, when the user is at home, the launch unit can launch the app in home mode to provide information on measures to take at home. In this way, by selecting an optimal launch mode depending on the user's current activity status, it is possible to provide information suitable for the user. Some or all of the above-described processing in the launch unit may be performed using, for example, AI, or may be performed without using AI. For example, the launch unit can input the user's activity status data to the generation AI and cause the generation AI to select the optimal launch mode.

[0040] The launch unit can optimize the launch algorithm by referring to past launch history. The launch unit, for example, analyzes app launch history from past earthquakes to learn the optimal launch timing. For example, the launch unit can analyze app launch history from past earthquakes to learn the optimal launch timing. The launch unit can also analyze a user's behavioral patterns based on the past app launch history and select the optimal launch mode. For example, the launch unit can analyze a user's behavioral patterns based on the past app launch history and select the optimal launch mode. Furthermore, the launch unit can share the past app launch history on the cloud and integrate it with other users' data to optimize the launch algorithm. For example, the launch unit can upload the past app launch history to the cloud and integrate it with other users' data to optimize the launch algorithm. By doing so, the launch algorithm can be optimized by referring to the past launch history, improving the accuracy of app launch. Some or all of the above-described processing in the launch unit may be performed using, or without, AI. For example, the launch unit can input past launch history data to a generation AI and cause the generation AI to optimize the launch algorithm.

[0041] The recognition unit can improve recognition accuracy by adding a function to remove background noise during voice recognition. For example, the recognition unit can filter ambient noise during voice recognition to clearly recognize the user's voice. For example, the recognition unit can filter ambient noise during voice recognition to clearly recognize the user's voice. The recognition unit can also improve recognition accuracy by removing environmental noise, such as wind noise and traffic noise, during voice recognition. For example, the recognition unit can improve recognition accuracy by removing environmental noise, such as wind noise and traffic noise, during voice recognition. Furthermore, the recognition unit can remove other voices during voice recognition to recognize only the user's voice. For example, the recognition unit can remove other voices during voice recognition to recognize only the user's voice. This removes background noise, improving the accuracy of voice recognition. Some or all of the above-described processing in the recognition unit may be performed using AI, or may be performed without AI. For example, the recognition unit can input voice data to a generation AI and cause the generation AI to remove background noise.

[0042] The recognition unit can improve recognition accuracy by integrating multiple voice samples during voice recognition. For example, the recognition unit can improve recognition accuracy by integrating a user's past voice samples. For example, the recognition unit can improve recognition accuracy by integrating a user's past voice samples. The recognition unit can also improve recognition accuracy by integrating multiple users' voice samples. For example, the recognition unit can improve recognition accuracy by integrating multiple users' voice samples. Furthermore, the recognition unit can improve recognition accuracy by integrating voice samples collected in different environments. For example, the recognition unit can improve recognition accuracy by integrating voice samples collected in different environments. In this way, the accuracy of voice recognition improves by integrating multiple voice samples. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input multiple voice samples to a generation AI and cause the generation AI to improve the accuracy of voice recognition.

[0043] During voice recognition, the recognition unit can optimize the recognition algorithm by referring to past voice data. For example, the recognition unit can analyze past voice data and learn specific patterns to optimize the recognition algorithm. For example, the recognition unit can analyze past voice data and learn specific patterns to optimize the recognition algorithm. The recognition unit can also build an algorithm that prioritizes recognition of a specific user's voice based on the past voice data. For example, the recognition unit can build an algorithm that prioritizes recognition of a specific user's voice based on the past voice data. Furthermore, the recognition unit can share past voice data on the cloud and integrate it with other users' data to optimize the recognition algorithm. For example, the recognition unit can upload past voice data to the cloud and integrate it with other users' data to optimize the recognition algorithm. By doing so, the recognition algorithm is optimized by referring to the past voice data, thereby improving the accuracy of voice recognition. Some or all of the above-described processing in the recognition unit may be performed using, or without, AI. For example, the recognition unit can input past voice data to a generation AI and cause the generation AI to optimize the recognition algorithm.

[0044] When requesting rescue, the request unit can monitor the user's health condition and select the optimal rescue method. The request unit can, for example, monitor the user's heart rate and blood pressure and select the rescue method according to the level of urgency. For example, the request unit can monitor the user's heart rate and blood pressure and select the rescue method according to the level of urgency. The request unit can also monitor the user's body temperature and oxygen saturation and select an appropriate rescue method. For example, the request unit can monitor the user's body temperature and oxygen saturation and select an appropriate rescue method. Furthermore, the request unit can comprehensively evaluate the user's health condition and select the optimal rescue method. For example, the request unit can comprehensively evaluate the user's health condition and select the optimal rescue method. In this way, the optimal rescue method can be selected by monitoring the user's health condition. Some or all of the above-mentioned processing in the request unit may be performed using, for example, AI, or may be performed without using AI. For example, the request unit can input the user's health data into the generation AI and cause the generation AI to select the optimal rescue method.

[0045] The request unit can simultaneously notify multiple rescue teams when a rescue request is made. For example, the request unit can simultaneously send notifications to the nearest rescue team and local volunteers when a rescue request is made. For example, the request unit can simultaneously send notifications to the nearest rescue team and local volunteers when a rescue request is made. The request unit can also simultaneously send notifications to the fire department and police when a rescue request is made. For example, the request unit can simultaneously send notifications to the fire department and police when a rescue request is made. Furthermore, the request unit can also simultaneously send notifications to a medical institution and a rescue team when a rescue request is made. For example, the request unit can simultaneously send notifications to a medical institution and a rescue team when a rescue request is made. This enables rapid rescue operations by notifying multiple rescue teams simultaneously. Some or all of the above-mentioned processing in the request unit may be performed using, for example, AI, or may be performed without using AI. For example, the request unit can input the content of the rescue request notification into a generation AI and have the generation AI execute the optimal notification method.

[0046] When requesting rescue, the request unit can optimize the request algorithm by referring to past rescue data. The request unit, for example, analyzes past rescue data to learn the optimal timing for requesting rescue. For example, the request unit can analyze past rescue data to learn the optimal timing for requesting rescue. The request unit can also construct an algorithm for optimally allocating rescue resources in a specific area based on the past rescue data. For example, the request unit can construct an algorithm for optimally allocating rescue resources in a specific area based on the past rescue data. Furthermore, the request unit can share the past rescue data on the cloud and integrate it with other users' data to optimize the request algorithm. For example, the request unit can upload the past rescue data to the cloud and integrate it with other users' data to optimize the request algorithm. By doing so, the request algorithm can be optimized by referring to the past rescue data, thereby improving the accuracy of rescue requests. Some or all of the above-described processing in the request unit may be performed using, or without, AI. For example, the request unit can input past rescue data into a generation AI and cause the generation AI to optimize the request algorithm.

[0047] When providing psychological support, the support unit can select the optimal support method by referring to the user's past psychological state. The support unit, for example, analyzes the user's past psychological state and selects the optimal support method. For example, the support unit can analyze the user's past psychological state and select the optimal support method. The support unit can also prioritize and provide a specific support method based on the user's past psychological state. For example, the support unit can prioritize and provide a specific support method based on the user's past psychological state. Furthermore, the support unit can share the user's past psychological state on the cloud and integrate it with data from other users to select the optimal support method. For example, the support unit can upload the user's past psychological state to the cloud and integrate it with data from other users to select the optimal support method. This allows the optimal support method to be selected by referring to the user's past psychological state. Some or all of the above-described processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past psychological data into a generation AI and cause the generation AI to select the optimal support method.

[0048] The support unit can integrate multiple support means to improve the accuracy of psychological support when providing psychological support. For example, the support unit can integrate audio guidance and text messages to provide psychological support. For example, the support unit can integrate audio guidance and text messages to provide psychological support. The support unit can also integrate music and video to enhance the relaxation effect. For example, the support unit can integrate music and video to enhance the relaxation effect. Furthermore, the support unit can also integrate audio guidance and vibration to provide psychological support. For example, the support unit can integrate audio guidance and vibration to provide psychological support. In this way, the integration of multiple support means improves the accuracy of support. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input multiple support means into the generation AI and cause the generation AI to improve the accuracy of support.

[0049] When providing psychological support, the support unit can optimize the support algorithm by referring to past support data. For example, the support unit can analyze past support data to learn the optimal support method. Furthermore, the support unit can build an algorithm that provides the optimal support method for a specific user based on the past support data. For example, the support unit can build an algorithm that provides the optimal support method for a specific user based on the past support data. Furthermore, the support unit can share past support data on the cloud and integrate it with data from other users to optimize the support algorithm. For example, the support unit can upload past support data to the cloud and integrate it with data from other users to optimize the support algorithm. By referencing the past support data, the support algorithm is optimized and the support accuracy is improved. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input past support data into a generation AI and cause the generation AI to optimize the support algorithm.

[0050] When issuing first aid instructions, the instruction unit can monitor the user's health condition and select the optimal instruction method. The instruction unit can, for example, monitor the user's heart rate and blood pressure and provide first aid instructions according to the level of urgency. For example, the instruction unit can monitor the user's heart rate and blood pressure and provide first aid instructions according to the level of urgency. The instruction unit can also monitor the user's body temperature and oxygen saturation and provide appropriate first aid instructions. For example, the instruction unit can monitor the user's body temperature and oxygen saturation and provide appropriate first aid instructions. Furthermore, the instruction unit can comprehensively evaluate the user's health condition and provide optimal first aid instructions. For example, the instruction unit can comprehensively evaluate the user's health condition and provide optimal first aid instructions. In this way, optimal first aid instructions can be provided by monitoring the user's health condition. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's health data into the generation AI and cause the generation AI to select optimal first aid instructions.

[0051] The instruction unit can improve the accuracy of instructions by integrating multiple instruction means when providing first aid instructions. The instruction unit, for example, can provide first aid instructions by integrating audio guidance and text messages. For example, the instruction unit can provide first aid instructions by integrating audio guidance and text messages. The instruction unit can also provide first aid instructions by integrating video and text. For example, the instruction unit can provide first aid instructions by integrating video and text. Furthermore, the instruction unit can provide first aid instructions by integrating audio guidance and vibration. For example, the instruction unit can provide first aid instructions by integrating audio guidance and vibration. In this way, the accuracy of instructions is improved by integrating multiple instruction means. Some or all of the above-described processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input multiple instruction means to the generation AI and cause the generation AI to improve the accuracy of instructions.

[0052] When issuing first aid instructions, the instruction unit can optimize the instruction algorithm by referring to past first aid data. The instruction unit, for example, analyzes past first aid data to learn an optimal first aid instruction method. For example, the instruction unit can analyze past first aid data to learn an optimal first aid instruction method. The instruction unit can also build an algorithm that provides an optimal first aid instruction method for a specific user based on the past first aid data. For example, the instruction unit can build an algorithm that provides an optimal first aid instruction method for a specific user based on the past first aid data. Furthermore, the instruction unit can share past first aid data on the cloud and integrate it with other users' data to optimize the instruction algorithm. For example, the instruction unit can upload past first aid data to the cloud and integrate it with other users' data to optimize the instruction algorithm. By referencing the past first aid data, the instruction algorithm is optimized, and the accuracy of first aid instructions is improved. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input past first aid data into the generation AI and cause the generation AI to optimize the instruction algorithm.

[0053] When cooperating, the cooperation unit can select the optimal cooperation method by referring to the user's past cooperation history. The cooperation unit, for example, analyzes the user's past cooperation history and selects the optimal cooperation method. For example, the cooperation unit can analyze the user's past cooperation history and select the optimal cooperation method. The cooperation unit can also prioritize and provide a specific cooperation method based on the user's past cooperation history. For example, the cooperation unit can prioritize and provide a specific cooperation method based on the user's past cooperation history. Furthermore, the cooperation unit can share the user's past cooperation history on the cloud and integrate it with data from other users to select the optimal cooperation method. For example, the cooperation unit can upload the user's past cooperation history to the cloud and integrate it with data from other users to select the optimal cooperation method. In this way, the optimal cooperation method can be selected by referring to the user's past cooperation history. Some or all of the above-described processing in the cooperation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooperation unit can input the user's past cooperation data into a generation AI and cause the generation AI to select the optimal cooperation method.

[0054] The cooperation unit can improve the accuracy of cooperation by integrating multiple cooperation means during cooperation. For example, the cooperation unit can provide a cooperation method by integrating audio guidance and text messages. For example, the cooperation unit can provide a cooperation method by integrating audio guidance and text messages. The cooperation unit can also provide a cooperation method by integrating video and text. For example, the cooperation unit can provide a cooperation method by integrating video and text. Furthermore, the cooperation unit can provide a cooperation method by integrating audio guidance and vibration. For example, the cooperation unit can provide a cooperation method by integrating audio guidance and vibration. In this way, the accuracy of cooperation is improved by integrating multiple cooperation means. Some or all of the above-described processing in the cooperation unit may be performed using AI, for example, or without AI. For example, the cooperation unit can input multiple cooperation means into the generation AI and cause the generation AI to improve the accuracy of cooperation.

[0055] During cooperation, the cooperation unit can optimize the cooperation algorithm by referring to past cooperation data. The cooperation unit, for example, analyzes past cooperation data to learn the optimal cooperation method. For example, the cooperation unit can analyze past cooperation data to learn the optimal cooperation method. The cooperation unit can also build an algorithm that provides an optimal cooperation method for a specific user based on the past cooperation data. For example, the cooperation unit can build an algorithm that provides an optimal cooperation method for a specific user based on the past cooperation data. Furthermore, the cooperation unit can share the past cooperation data on the cloud and integrate it with data from other users to optimize the cooperation algorithm. For example, the cooperation unit can upload the past cooperation data to the cloud and integrate it with data from other users to optimize the cooperation algorithm. This optimizes the cooperation algorithm by referring to the past cooperation data, thereby improving the cooperation accuracy. Some or all of the above-described processing in the cooperation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooperation unit can input the past cooperation data to a generation AI and cause the generation AI to optimize the cooperation algorithm.

[0056] When acquiring location information, the location information acquisition unit can select an optimal acquisition method by taking into account the user's current activity status. For example, when the user is walking, the location information acquisition unit can acquire location information using GPS. For example, when the user is walking, the location information acquisition unit can acquire location information using GPS. Furthermore, when the user is in a car, the location information acquisition unit can acquire location information in cooperation with an in-car navigation system. For example, when the user is in a car, the location information acquisition unit can acquire location information in cooperation with an in-car navigation system. Furthermore, when the user is at home, the location information acquisition unit can acquire location information using Wi-Fi. For example, when the user is at home, the location information acquisition unit can acquire location information using Wi-Fi. This allows the optimal location information acquisition method to be selected by taking into account the user's current activity status. Some or all of the above-described processing in the location information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the location information acquisition unit can input the user's activity status data to a generation AI and cause the generation AI to select an optimal location information acquisition method.

[0057] The location information acquisition unit can improve the accuracy of location information acquisition by integrating multiple location information sources. The location information acquisition unit can acquire highly accurate location information by integrating location information from, for example, GPS, Wi-Fi, and Bluetooth. For example, the location information acquisition unit can acquire highly accurate location information by integrating location information from GPS, Wi-Fi, and Bluetooth. The location information acquisition unit can also improve the accuracy of acquisition by integrating location information from a smartphone sensor and an external device. For example, the location information acquisition unit can improve the accuracy of acquisition by integrating location information from a smartphone sensor and an external device. Furthermore, the location information acquisition unit can improve the accuracy of acquisition by integrating location information from multiple users in the cloud. For example, the location information acquisition unit can upload location information from multiple users to the cloud and integrate the data on the cloud to improve the accuracy of acquisition. In this way, the accuracy of location information acquisition is improved by integrating multiple location information sources. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can input multiple location information sources into the generation AI and have the generation AI integrate the data and acquire location information.

[0058] When acquiring location information, the location information acquisition unit can optimize the acquisition algorithm by referring to past location information data. For example, the location information acquisition unit can analyze past location information data, learn specific patterns, and optimize the acquisition algorithm. For example, the location information acquisition unit can analyze past location information data, learn specific patterns, and optimize the acquisition algorithm. The location information acquisition unit can also build an algorithm that improves the accuracy of acquiring location information in a specific area based on past location information data. For example, the location information acquisition unit can build an algorithm that improves the accuracy of acquiring location information in a specific area based on past location information data. Furthermore, the location information acquisition unit can share past location information data on the cloud and integrate it with other users' data to optimize the acquisition algorithm. For example, the location information acquisition unit can upload past location information data to the cloud and integrate it with other users' data to optimize the acquisition algorithm. By referencing past location information data, the acquisition algorithm is optimized, and the accuracy of location information acquisition is improved. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can input past location information data into the generation AI and cause the generation AI to optimize the acquisition algorithm.

[0059] The location information acquisition unit can improve the accuracy of location information acquisition by taking into account the geographical characteristics of the user. For example, if the user's current location is in a high-rise building, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the influence of the building. For example, if the user's current location is in a high-rise building, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the influence of the building. Furthermore, if the user's current location is in a subway, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the underground environment. For example, if the user's current location is in a subway, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the underground environment. Furthermore, if the user's current location is in a mountainous area, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the influence of the terrain. For example, if the user's current location is in a mountainous area, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the influence of the terrain. This improves the accuracy of location information acquisition by taking into account the user's geographical characteristics. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can input the user's geographical characteristic data into the generation AI and cause the generation AI to improve acquisition accuracy.

[0060] When acquiring location information, the location information acquisition unit can acquire location information in cooperation with the user's smart home device. For example, when the user is at home, the location information acquisition unit can acquire location information using the location information of the smart home device. For example, when the user is at home, the location information acquisition unit can acquire location information using the location information of the smart home device. Furthermore, when the user is linked with a smart home device, the location information acquisition unit can improve acquisition accuracy by integrating the location information of the devices. For example, when the user is linked with a smart home device, the location information acquisition unit can improve acquisition accuracy by integrating the location information of the devices. Furthermore, when the user is carrying a smart home device, the location information acquisition unit can acquire location information using the location information of the device. For example, when the user is carrying a smart home device, the location information acquisition unit can acquire location information using the location information of the device. As a result, by linking with the smart home device, the accuracy of location information acquisition is improved. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can input location information data of a smart home device into the generation AI and cause the generation AI to acquire location information.

[0061] The location information acquisition unit can customize the acquisition algorithm by reflecting the user's past feedback when acquiring location information. For example, if the user has previously expressed dissatisfaction with the accuracy of location information acquisition, the location information acquisition unit can adjust the algorithm based on that feedback. For example, if the user has previously expressed dissatisfaction with the accuracy of location information acquisition, the location information acquisition unit can adjust the algorithm based on that feedback. Furthermore, if the user has previously expressed dissatisfaction with the notification method of location information acquisition, the location information acquisition unit can adjust the notification method based on that feedback. For example, if the user has previously expressed dissatisfaction with the notification method of location information acquisition, the location information acquisition unit can adjust the notification method based on that feedback. Furthermore, if the user has previously expressed dissatisfaction with the timing of location information acquisition, the location information acquisition unit can adjust the acquisition timing based on that feedback. For example, if the user has previously expressed dissatisfaction with the timing of location information acquisition, the location information acquisition unit can adjust the acquisition timing based on that feedback. In this way, by reflecting past feedback, the acquisition algorithm can be customized, and the accuracy of location information acquisition can be improved. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can input user feedback data into the generation AI and cause the generation AI to customize the acquisition algorithm.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The disaster support system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit can monitor the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time and issue an alert if an abnormality is detected. For example, if the user's heart rate suddenly increases, the health monitoring unit can automatically request rescue. If the user's body temperature is abnormally high, the health monitoring unit can warn of the risk of heatstroke. Furthermore, if the user's blood pressure is abnormally low, the health monitoring unit can notify the user of the risk of hypotension. This allows the user's health condition to be constantly monitored and a prompt response can be made.

[0064] The disaster support system can further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit can analyze the user's past behavioral patterns and predict behavior during a disaster. For example, if the user has a history of evacuating to an evacuation shelter in the past, the behavior analysis unit can suggest the optimal evacuation route based on that information. Also, if the user has a history of staying at a specific location for a long time in the past, the behavior analysis unit can suggest that location as a preferred evacuation destination. Furthermore, the behavior analysis unit can predict behavior during a disaster based on the user's past behavioral data and provide appropriate support. This makes it possible to utilize the user's behavioral history to respond to disasters more effectively.

[0065] The disaster support system can further include an environmental monitoring unit that monitors the environment around the user. The environmental monitoring unit monitors environmental data such as ambient temperature, humidity, and air pressure in real time, and can issue an alert if it detects an abnormality. For example, if the ambient temperature rises suddenly, the environmental monitoring unit can warn of the risk of heatstroke. If the ambient humidity is abnormally high, the environmental monitoring unit can notify of the risk of mold growth. Furthermore, if the ambient air pressure drops suddenly, the environmental monitoring unit can warn of the risk of weather fluctuations. This allows the user's surrounding environment to be constantly monitored, enabling rapid response.

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

[0067] Step 1: The detection unit detects the occurrence of an earthquake. Earthquakes can be detected using a seismometer or a smartphone's sensors. For example, a seismometer detects seismic waves and measures the seismic intensity. Smartphone sensors can detect earthquake shaking using an acceleration sensor or gyro sensor. Step 2: The launching unit automatically launches the app based on the earthquake detected by the detection unit. For example, the app can be automatically launched when an earthquake with a seismic intensity of 7 or higher is detected. Step 3: The recognition unit recognizes the user's voice. Speech recognition technology can be used to recognize the user's voice. For example, the speech recognition technology can recognize the user's voice crying out "help" and issue a rescue request. Step 4: The request unit issues a rescue request based on the voice recognized by the recognition unit. For example, the request unit can obtain location information of the user and notify the nearest rescue team. Step 5: The support unit provides psychological support. For example, if the user is in a panic state, the support unit can provide appropriate psychological support. For example, the support unit can use relaxation techniques to calm the user. Step 6: The instruction unit issues first aid instructions. For example, if first aid is required, the instruction unit can instruct specific procedures. For example, the instruction unit can instruct first aid procedures such as how to stop bleeding and cardiopulmonary resuscitation.

[0068] (Example 2) A disaster support system according to an embodiment of the present invention detects an earthquake, responds to a user's voice to request rescue, and provides psychological support and first aid instructions. The disaster support system detects an earthquake, automatically launches an app, and responds to the user's voice to request rescue. The disaster support system also realizes mutual assistance through cooperation with the local community and utilizes edge AI to provide psychological support and first aid instructions. For example, the disaster support system automatically activates when an earthquake with a seismic intensity of 7 or higher occurs. For example, the occurrence of an earthquake can be detected using a seismometer or a smartphone sensor. Next, the disaster support system responds to the user's voice to request rescue. For example, when a user yells "help," the disaster support system recognizes the voice and requests rescue. At this time, the disaster support system can acquire the user's location information and notify the nearest rescue team. This enables rapid rescue operations. Furthermore, the disaster support system realizes mutual assistance through cooperation with the local community. For example, a user can request help from neighboring residents through the disaster support system. This promotes mutual assistance throughout the community and ensures efficient disaster relief. In addition, the disaster support system can utilize edge AI to provide psychological support and first aid instructions. For example, if a user panics, the disaster support system can provide appropriate psychological support. Furthermore, if first aid is required, the disaster support system can provide specific instructions. This allows the user to respond calmly. In this way, the disaster support system automatically activates when an earthquake occurs, responds to the user's voice to request rescue, and realizes mutual assistance through cooperation with the local community. Furthermore, because it can utilize edge AI to provide psychological support and first aid instructions, it is an essential application in the event of a disaster. This allows the disaster support system to quickly and accurately request rescue when an earthquake occurs and provide psychological support and first aid instructions. For example, even if a user panics, they can receive appropriate support and respond calmly. Furthermore, cooperation with the local community enables efficient relief activities.

[0069] A disaster support system according to an embodiment includes a detection unit, a launch unit, a recognition unit, a request unit, a support unit, and an instruction unit. The detection unit detects the occurrence of an earthquake. The earthquake can be detected, for example, using a seismometer or a smartphone sensor. For example, the seismometer detects seismic waves and measures seismic intensity. The smartphone sensor can detect earthquake tremors using an acceleration sensor or a gyro sensor. The launch unit automatically launches an app based on the earthquake detected by the detection unit. For example, the app can be automatically launched when an earthquake with a seismic intensity of 7 or higher is detected. The recognition unit recognizes a user's voice. Voice recognition technology can be used to recognize the user's voice. For example, voice recognition technology can recognize a user's voice yelling "help" and issue a rescue request. The request unit issues a rescue request based on the voice recognized by the recognition unit. For example, the request unit can obtain the user's location information and notify the nearest rescue team. The support unit provides psychological support. For example, if the user is in a panic, the support unit can provide appropriate psychological support. For example, the support unit can calm the user using relaxation techniques. The instruction unit provides first aid instructions. For example, if first aid is needed, the instruction unit can provide specific instructions on procedures. For example, the instruction unit can provide instructions on first aid procedures such as how to stop bleeding and cardiopulmonary resuscitation. As a result, the disaster support system according to the embodiment can automatically start up when an earthquake occurs, request rescue in response to the user's voice, and provide psychological support and first aid instructions.

[0070] The request unit can acquire the user's location information and notify the nearest rescue team. The request unit can acquire the user's location information using, for example, GPS. For example, the request unit can identify the user's current location using the GPS function of a smartphone. The request unit can also acquire the user's location information using Wi-Fi location information. For example, the request unit can identify the user's location based on information about surrounding Wi-Fi access points. The request unit can also acquire the user's location information using Bluetooth location information. For example, the request unit can identify the user's location based on information about surrounding Bluetooth devices. This enables prompt rescue operations by acquiring the user's location information and quickly notifying the nearest rescue team. Some or all of the above-described processing in the request unit can be performed using, for example, AI, or without AI. For example, the request unit can input the user's location information into AI and cause the AI ​​to execute a process of selecting the most appropriate rescue team.

[0071] The support unit can provide appropriate psychological support when a user falls into a panic state. For example, the support unit can monitor the user's heart rate and irregular breathing to determine whether the user is in a panic state. For example, the support unit can measure the user's heart rate using a smartphone sensor and detect an increase in the heart rate. The support unit can also use the smartphone's microphone to detect irregular breathing. For example, the support unit can analyze the user's breathing sounds and detect irregular breathing. The support unit can provide appropriate psychological support to a user who falls into a panic state. For example, the support unit can calm the user using relaxation techniques. For example, the support unit can instruct the user to take deep breaths to relieve tension. The support unit can also play relaxing music to calm the user. Furthermore, the support unit can provide an encouraging message to the user. For example, the support unit can display a message such as "It's okay, please stay calm." This allows the user to remain calm by providing appropriate psychological support when the user falls into a panic state. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input the user's heart rate data into AI and have the AI ​​determine whether the user is in a panic state.

[0072] The instruction unit can instruct specific procedures when first aid is needed. The instruction unit instructs first aid procedures, for example, when a user is injured. For example, the instruction unit can instruct how to stop bleeding. For example, the instruction unit can display specific procedures such as "apply pressure to the wound with a clean cloth." The instruction unit can also instruct cardiopulmonary resuscitation procedures. For example, the instruction unit can display specific procedures such as "perform chest compressions." The instruction unit can also instruct first aid procedures for fractures. For example, the instruction unit can display specific procedures such as "immobilize the fractured area." This allows the user to perform appropriate first aid by instructing specific procedures when first aid is needed. Some or all of the above-described processing in the instruction unit may be performed using, or without, AI. For example, the instruction unit can input the state of the user's injury into AI and cause the AI ​​to generate optimal first aid procedures.

[0073] The disaster support system further includes a cooperation unit that promotes cooperation with the local area and community. The cooperation unit promotes cooperation with the local area and community. For example, the cooperation unit can request help from nearby residents during a disaster. For example, the cooperation unit can send a notification to nearby residents via an app to request help. The cooperation unit can also work with local volunteers to carry out support activities. For example, the cooperation unit can request support from local volunteers and ask for their cooperation. The cooperation unit can also carry out support activities in cooperation with local community centers. For example, the cooperation unit can request support from community centers and ask for their cooperation. This promotes cooperation with the local area and community, thereby efficiently providing support during a disaster. Some or all of the above-mentioned processing in the cooperation unit may be performed using AI, or may be performed without AI. For example, the cooperation unit can input local resident data into AI and have the AI ​​generate an optimal cooperation method.

[0074] The disaster support system further includes a location information acquisition unit that acquires user location information. The location information acquisition unit acquires the user location information. For example, the location information acquisition unit can acquire the user location information using GPS. For example, the location information acquisition unit can identify the user's current location using the GPS function of a smartphone. The location information acquisition unit can also acquire the user location information using Wi-Fi location information. For example, the location information acquisition unit can identify the user's location based on information about surrounding Wi-Fi access points. The location information acquisition unit can also acquire the user location information using Bluetooth location information. For example, the location information acquisition unit can identify the user's location based on information about surrounding Bluetooth devices. By acquiring the user's location information, accurate location information can be provided when requesting rescue. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the location information acquisition unit can input the user's location information into AI and cause the AI ​​to execute a process of selecting the most appropriate rescue team.

[0075] The detection unit can estimate the user's emotions and adjust the earthquake detection sensitivity based on the estimated user's emotions. The detection unit can, for example, analyze the user's voice and facial expressions to estimate emotions. For example, the detection unit can use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. The detection unit can also use facial expression recognition technology to analyze the user's facial expressions to estimate emotions. For example, the detection unit can capture the user's facial expressions with a camera and estimate emotions using a facial expression recognition algorithm. The detection unit adjusts the earthquake detection sensitivity based on the estimated user's emotions. For example, if the user is nervous, the detection unit can increase the earthquake detection sensitivity to detect even the slightest tremors. If the user is relaxed, the detection unit can set the earthquake detection sensitivity to normal to avoid excessive alerts. Furthermore, if the user is in a panic, the detection unit can maximize the earthquake detection sensitivity and immediately issue an alert. This allows for more appropriate earthquake detection by adjusting the earthquake detection sensitivity according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit may input user voice data to the generation AI and cause the generation AI to estimate emotions.

[0076] The detection unit can detect precursory phenomena of an earthquake and issue a warning in advance. For example, the detection unit can analyze seismometer data in real time to detect precursory phenomena. For example, the detection unit can analyze seismometer data and detect changes in seismic waves. The detection unit can also use a smartphone's sensors to detect abnormal vibrations or sounds and issue a warning as a precursor to an earthquake. For example, the detection unit can detect abnormal vibrations or sounds using the smartphone's acceleration sensor or microphone. The detection unit can also analyze weather data to detect abnormal weather that is a precursor to an earthquake. For example, the detection unit can analyze weather data and detect abnormal changes in temperature or air pressure. This allows the detection of precursory phenomena of an earthquake and issue a warning in advance, allowing the user to take preventive measures. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input seismometer data into a generation AI and cause the generation AI to detect precursory phenomena.

[0077] The detection unit can improve detection accuracy by integrating data from multiple sensors. For example, the detection unit can integrate data from a smartphone's acceleration sensor and seismometer to detect the occurrence of an earthquake with high accuracy. For example, the detection unit can integrate data from a smartphone's acceleration sensor and seismometer to detect the occurrence of an earthquake. The detection unit can also integrate sensor data from multiple smartphones in the cloud to detect the occurrence of an earthquake. For example, the detection unit can transmit sensor data from multiple smartphones to the cloud and integrate the data in the cloud to detect the occurrence of an earthquake. Furthermore, the detection unit can integrate data from a seismometer, weather data, and a smartphone sensor to detect the occurrence of an earthquake with high accuracy. For example, the detection unit can integrate data from a seismometer, weather data, and smartphone sensor data to detect the occurrence of an earthquake. In this way, by integrating data from multiple sensors, the accuracy of earthquake detection is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input multiple sensor data to a generation AI and cause the generation AI to integrate the data and detect earthquakes.

[0078] The detection unit can optimize the detection algorithm by referring to past earthquake data. The detection unit, for example, analyzes past earthquake data, learns earthquake occurrence patterns, and optimizes the detection algorithm. For example, the detection unit can analyze past earthquake data and learn earthquake occurrence patterns. The detection unit can also evaluate the earthquake occurrence risk of a specific area based on the past earthquake data and adjust the detection algorithm. For example, the detection unit can evaluate the earthquake occurrence risk of a specific area based on the past earthquake data and adjust the detection algorithm. Furthermore, the detection unit can share past earthquake data on the cloud and integrate it with other users' data to optimize the detection algorithm. For example, the detection unit can upload past earthquake data to the cloud and integrate it with other users' data to optimize the detection algorithm. This optimizes the detection algorithm by referring to past earthquake data, improving the accuracy of earthquake detection. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input past earthquake data to a generation AI and cause the generation AI to optimize the detection algorithm.

[0079] The launch unit can estimate the user's emotions and adjust the timing of app launch based on the estimated user emotions. The launch unit, for example, analyzes the user's voice and facial expressions to estimate emotions. For example, the launch unit can use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. The launch unit can also use facial expression recognition technology to analyze the user's facial expressions to estimate emotions. For example, the launch unit can capture the user's facial expressions with a camera and estimate emotions using a facial expression recognition algorithm. The launch unit adjusts the timing of app launch based on the estimated user emotions. For example, if the user is nervous, the launch unit can immediately launch the app immediately after an earthquake occurs. Alternatively, if the user is relaxed, the launch unit can launch the app a short time after the earthquake occurs. Furthermore, if the user is in a panic, the launch unit can launch the app immediately after the earthquake occurs, allowing for a quick response. This allows the app to be launched at a more appropriate time by adjusting the timing of app launch based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the launch unit may be performed using AI, or may be performed without using AI. For example, the launch unit may input user voice data to the generation AI and cause the generation AI to estimate emotions.

[0080] The launch unit can automatically notify emergency contacts when an earthquake occurs, simultaneously with the launch of the app. The launch unit, for example, can send an SMS notification to emergency contacts when an earthquake occurs, simultaneously with the launch of the app. For example, the launch unit can send an SMS notification to emergency contacts when an earthquake occurs, simultaneously with the launch of the app. The launch unit can also send an email notification to emergency contacts when an earthquake occurs, simultaneously with the launch of the app. For example, the launch unit can send an email notification to emergency contacts when an earthquake occurs, simultaneously with the launch of the app. The launch unit can also notify emergency contacts by phone when an earthquake occurs, simultaneously with the launch of the app. For example, the launch unit can notify emergency contacts by phone when an earthquake occurs, simultaneously with the launch of the app. This enables a rapid response by automatically notifying emergency contacts when an earthquake occurs, simultaneously with the launch of the app. Some or all of the above-described processing in the launch unit may be performed using, for example, AI, or may be performed without AI. For example, the launch unit can input notification content when an earthquake occurs into a generation AI and cause the generation AI to execute the optimal notification method.

[0081] The launch unit can select an optimal launch mode depending on the user's current activity status. For example, when the user is walking, the launch unit launches the app in lightweight mode to quickly provide information. For example, when the user is walking, the launch unit can launch the app in lightweight mode to quickly provide information. Furthermore, when the user is in a car, the launch unit can launch the app in driving mode to provide information suitable for driving. For example, when the user is in a car, the launch unit can launch the app in driving mode to provide information suitable for driving. Furthermore, when the user is at home, the launch unit can launch the app in home mode to provide information on measures to take at home. For example, when the user is at home, the launch unit can launch the app in home mode to provide information on measures to take at home. In this way, by selecting an optimal launch mode depending on the user's current activity status, it is possible to provide information suitable for the user. Some or all of the above-described processing in the launch unit may be performed using, for example, AI, or may be performed without using AI. For example, the launch unit can input the user's activity status data to the generation AI and cause the generation AI to select the optimal launch mode.

[0082] The launch unit can optimize the launch algorithm by referring to past launch history. The launch unit, for example, analyzes app launch history from past earthquakes to learn the optimal launch timing. For example, the launch unit can analyze app launch history from past earthquakes to learn the optimal launch timing. The launch unit can also analyze a user's behavioral patterns based on the past app launch history and select the optimal launch mode. For example, the launch unit can analyze a user's behavioral patterns based on the past app launch history and select the optimal launch mode. Furthermore, the launch unit can share the past app launch history on the cloud and integrate it with other users' data to optimize the launch algorithm. For example, the launch unit can upload the past app launch history to the cloud and integrate it with other users' data to optimize the launch algorithm. By doing so, the launch algorithm can be optimized by referring to the past launch history, improving the accuracy of app launch. Some or all of the above-described processing in the launch unit may be performed using, or without, AI. For example, the launch unit can input past launch history data to a generation AI and cause the generation AI to optimize the launch algorithm.

[0083] The recognition unit can estimate the user's emotions and adjust the sensitivity of the voice recognition based on the estimated user's emotions. The recognition unit can, for example, analyze the user's voice and facial expressions to estimate the emotions. For example, the recognition unit can use voice analysis technology to analyze the tone and speed of the user's voice to estimate the emotions. The recognition unit can also use facial expression recognition technology to analyze the user's facial expressions to estimate the emotions. For example, the recognition unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The recognition unit adjusts the sensitivity of the voice recognition based on the estimated user's emotions. For example, if the user is nervous, the recognition unit can increase the sensitivity of the voice recognition to recognize even faint voices. If the user is relaxed, the recognition unit can set the sensitivity of the voice recognition to normal to avoid overreaction. Furthermore, if the user is in a panic, the recognition unit can maximize the sensitivity of the voice recognition to respond immediately. This allows for more appropriate voice recognition by adjusting the sensitivity of the voice recognition according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit may be performed using, or without, an AI. For example, the recognition unit may input user voice data to the generation AI and cause the generation AI to estimate emotions.

[0084] The recognition unit can improve recognition accuracy by adding a function to remove background noise during voice recognition. For example, the recognition unit can filter ambient noise during voice recognition to clearly recognize the user's voice. For example, the recognition unit can filter ambient noise during voice recognition to clearly recognize the user's voice. The recognition unit can also improve recognition accuracy by removing environmental noise, such as wind noise and traffic noise, during voice recognition. For example, the recognition unit can improve recognition accuracy by removing environmental noise, such as wind noise and traffic noise, during voice recognition. Furthermore, the recognition unit can remove other voices during voice recognition to recognize only the user's voice. For example, the recognition unit can remove other voices during voice recognition to recognize only the user's voice. This removes background noise, improving the accuracy of voice recognition. Some or all of the above-described processing in the recognition unit may be performed using AI, or may be performed without AI. For example, the recognition unit can input voice data to a generation AI and cause the generation AI to remove background noise.

[0085] The recognition unit can improve recognition accuracy by integrating multiple voice samples during voice recognition. For example, the recognition unit can improve recognition accuracy by integrating a user's past voice samples. For example, the recognition unit can improve recognition accuracy by integrating a user's past voice samples. The recognition unit can also improve recognition accuracy by integrating multiple users' voice samples. For example, the recognition unit can improve recognition accuracy by integrating multiple users' voice samples. Furthermore, the recognition unit can improve recognition accuracy by integrating voice samples collected in different environments. For example, the recognition unit can improve recognition accuracy by integrating voice samples collected in different environments. In this way, the accuracy of voice recognition improves by integrating multiple voice samples. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input multiple voice samples to a generation AI and cause the generation AI to improve the accuracy of voice recognition.

[0086] During voice recognition, the recognition unit can optimize the recognition algorithm by referring to past voice data. For example, the recognition unit can analyze past voice data and learn specific patterns to optimize the recognition algorithm. For example, the recognition unit can analyze past voice data and learn specific patterns to optimize the recognition algorithm. The recognition unit can also build an algorithm that prioritizes recognition of a specific user's voice based on the past voice data. For example, the recognition unit can build an algorithm that prioritizes recognition of a specific user's voice based on the past voice data. Furthermore, the recognition unit can share past voice data on the cloud and integrate it with other users' data to optimize the recognition algorithm. For example, the recognition unit can upload past voice data to the cloud and integrate it with other users' data to optimize the recognition algorithm. By doing so, the recognition algorithm is optimized by referring to the past voice data, thereby improving the accuracy of voice recognition. Some or all of the above-described processing in the recognition unit may be performed using, or without, AI. For example, the recognition unit can input past voice data to a generation AI and cause the generation AI to optimize the recognition algorithm.

[0087] The request unit can estimate the user's emotions and determine the priority of rescue requests based on the estimated user emotions. The request unit can, for example, analyze the user's voice and facial expressions to estimate emotions. For example, the request unit can use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. The request unit can also use facial expression recognition technology to analyze the user's facial expressions to estimate emotions. For example, the request unit can capture the user's facial expressions with a camera and estimate emotions using a facial expression recognition algorithm. The request unit determines the priority of rescue requests based on the estimated user emotions. For example, if the user is in a panicked state, the request unit can set the priority of the rescue request to high. If the user is relaxed, the request unit can set the priority of the rescue request to normal. Furthermore, if the user is nervous, the request unit can set the priority of the rescue request to medium. This enables more appropriate rescue operations by determining the priority of rescue requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the request unit may be performed using AI, or may be performed without using AI. For example, the request unit may input user voice data into the generation AI and cause the generation AI to estimate emotions.

[0088] When requesting rescue, the request unit can monitor the user's health condition and select the optimal rescue method. The request unit can, for example, monitor the user's heart rate and blood pressure and select the rescue method according to the level of urgency. For example, the request unit can monitor the user's heart rate and blood pressure and select the rescue method according to the level of urgency. The request unit can also monitor the user's body temperature and oxygen saturation and select an appropriate rescue method. For example, the request unit can monitor the user's body temperature and oxygen saturation and select an appropriate rescue method. Furthermore, the request unit can comprehensively evaluate the user's health condition and select the optimal rescue method. For example, the request unit can comprehensively evaluate the user's health condition and select the optimal rescue method. In this way, the optimal rescue method can be selected by monitoring the user's health condition. Some or all of the above-mentioned processing in the request unit may be performed using, for example, AI, or may be performed without using AI. For example, the request unit can input the user's health data into the generation AI and cause the generation AI to select the optimal rescue method.

[0089] The request unit can simultaneously notify multiple rescue teams when a rescue request is made. For example, the request unit can simultaneously send notifications to the nearest rescue team and local volunteers when a rescue request is made. For example, the request unit can simultaneously send notifications to the nearest rescue team and local volunteers when a rescue request is made. The request unit can also simultaneously send notifications to the fire department and police when a rescue request is made. For example, the request unit can simultaneously send notifications to the fire department and police when a rescue request is made. Furthermore, the request unit can also simultaneously send notifications to a medical institution and a rescue team when a rescue request is made. For example, the request unit can simultaneously send notifications to a medical institution and a rescue team when a rescue request is made. This enables rapid rescue operations by notifying multiple rescue teams simultaneously. Some or all of the above-mentioned processing in the request unit may be performed using, for example, AI, or may be performed without using AI. For example, the request unit can input the content of the rescue request notification into a generation AI and have the generation AI execute the optimal notification method.

[0090] When requesting rescue, the request unit can optimize the request algorithm by referring to past rescue data. The request unit, for example, analyzes past rescue data to learn the optimal timing for requesting rescue. For example, the request unit can analyze past rescue data to learn the optimal timing for requesting rescue. The request unit can also construct an algorithm for optimally allocating rescue resources in a specific area based on the past rescue data. For example, the request unit can construct an algorithm for optimally allocating rescue resources in a specific area based on the past rescue data. Furthermore, the request unit can share the past rescue data on the cloud and integrate it with other users' data to optimize the request algorithm. For example, the request unit can upload the past rescue data to the cloud and integrate it with other users' data to optimize the request algorithm. By doing so, the request algorithm can be optimized by referring to the past rescue data, thereby improving the accuracy of rescue requests. Some or all of the above-described processing in the request unit may be performed using, or without, AI. For example, the request unit can input past rescue data into a generation AI and cause the generation AI to optimize the request algorithm.

[0091] The support unit can estimate the user's emotions and adjust the content of psychological support based on the estimated user's emotions. The support unit, for example, analyzes the user's voice and facial expressions to estimate emotions. For example, the support unit can use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. The support unit can also use facial expression recognition technology to analyze the user's facial expressions and estimate emotions. For example, the support unit can capture the user's facial expressions with a camera and estimate emotions using a facial expression recognition algorithm. The support unit adjusts the content of psychological support based on the estimated user's emotions. For example, if the user is nervous, the support unit can provide relaxing music or messages. If the user is relaxed, the support unit can provide regular psychological support. Furthermore, if the user is in a panic state, the support unit can provide instructions for deep breathing or relaxation. This allows the content of psychological support to be adjusted according to the user's emotions, thereby providing more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit may input user voice data into the generation AI and cause the generation AI to estimate emotions.

[0092] When providing psychological support, the support unit can select the optimal support method by referring to the user's past psychological state. The support unit, for example, analyzes the user's past psychological state and selects the optimal support method. For example, the support unit can analyze the user's past psychological state and select the optimal support method. The support unit can also prioritize and provide a specific support method based on the user's past psychological state. For example, the support unit can prioritize and provide a specific support method based on the user's past psychological state. Furthermore, the support unit can share the user's past psychological state on the cloud and integrate it with data from other users to select the optimal support method. For example, the support unit can upload the user's past psychological state to the cloud and integrate it with data from other users to select the optimal support method. This allows the optimal support method to be selected by referring to the user's past psychological state. Some or all of the above-described processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past psychological data into a generation AI and cause the generation AI to select the optimal support method.

[0093] The support unit can integrate multiple support means to improve the accuracy of psychological support when providing psychological support. For example, the support unit can integrate audio guidance and text messages to provide psychological support. For example, the support unit can integrate audio guidance and text messages to provide psychological support. The support unit can also integrate music and video to enhance the relaxation effect. For example, the support unit can integrate music and video to enhance the relaxation effect. Furthermore, the support unit can also integrate audio guidance and vibration to provide psychological support. For example, the support unit can integrate audio guidance and vibration to provide psychological support. In this way, the integration of multiple support means improves the accuracy of support. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input multiple support means into the generation AI and cause the generation AI to improve the accuracy of support.

[0094] When providing psychological support, the support unit can optimize the support algorithm by referring to past support data. For example, the support unit can analyze past support data to learn the optimal support method. Furthermore, the support unit can build an algorithm that provides the optimal support method for a specific user based on the past support data. For example, the support unit can build an algorithm that provides the optimal support method for a specific user based on the past support data. Furthermore, the support unit can share past support data on the cloud and integrate it with data from other users to optimize the support algorithm. For example, the support unit can upload past support data to the cloud and integrate it with data from other users to optimize the support algorithm. By referencing the past support data, the support algorithm is optimized and the support accuracy is improved. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input past support data into a generation AI and cause the generation AI to optimize the support algorithm.

[0095] The instruction unit can estimate the user's emotions and adjust the content of first aid instructions based on the estimated user's emotions. The instruction unit can, for example, analyze the user's voice and facial expressions to estimate the emotions. For example, the instruction unit can use voice analysis technology to analyze the tone and speed of the user's voice to estimate the emotions. The instruction unit can also use facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. For example, the instruction unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The instruction unit adjusts the content of the first aid instructions based on the estimated user's emotions. For example, if the user is nervous, the instruction unit can provide simple and easy-to-understand first aid instructions. If the user is relaxed, the instruction unit can provide detailed first aid instructions. Furthermore, if the user is in a panicked state, the instruction unit can provide quick and concise first aid instructions. This allows for more appropriate first aid by adjusting the content of the first aid instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the instruction unit may be performed using AI, or may be performed without using AI. For example, the instruction unit may input user voice data to the generation AI and cause the generation AI to estimate emotions.

[0096] When issuing first aid instructions, the instruction unit can monitor the user's health condition and select the optimal instruction method. The instruction unit can, for example, monitor the user's heart rate and blood pressure and provide first aid instructions according to the level of urgency. For example, the instruction unit can monitor the user's heart rate and blood pressure and provide first aid instructions according to the level of urgency. The instruction unit can also monitor the user's body temperature and oxygen saturation and provide appropriate first aid instructions. For example, the instruction unit can monitor the user's body temperature and oxygen saturation and provide appropriate first aid instructions. Furthermore, the instruction unit can comprehensively evaluate the user's health condition and provide optimal first aid instructions. For example, the instruction unit can comprehensively evaluate the user's health condition and provide optimal first aid instructions. In this way, optimal first aid instructions can be provided by monitoring the user's health condition. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's health data into the generation AI and cause the generation AI to select optimal first aid instructions.

[0097] The instruction unit can improve the accuracy of instructions by integrating multiple instruction means when providing first aid instructions. The instruction unit, for example, can provide first aid instructions by integrating audio guidance and text messages. For example, the instruction unit can provide first aid instructions by integrating audio guidance and text messages. The instruction unit can also provide first aid instructions by integrating video and text. For example, the instruction unit can provide first aid instructions by integrating video and text. Furthermore, the instruction unit can provide first aid instructions by integrating audio guidance and vibration. For example, the instruction unit can provide first aid instructions by integrating audio guidance and vibration. In this way, the accuracy of instructions is improved by integrating multiple instruction means. Some or all of the above-described processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input multiple instruction means to the generation AI and cause the generation AI to improve the accuracy of instructions.

[0098] When issuing first aid instructions, the instruction unit can optimize the instruction algorithm by referring to past first aid data. The instruction unit, for example, analyzes past first aid data to learn an optimal first aid instruction method. For example, the instruction unit can analyze past first aid data to learn an optimal first aid instruction method. The instruction unit can also build an algorithm that provides an optimal first aid instruction method for a specific user based on the past first aid data. For example, the instruction unit can build an algorithm that provides an optimal first aid instruction method for a specific user based on the past first aid data. Furthermore, the instruction unit can share past first aid data on the cloud and integrate it with other users' data to optimize the instruction algorithm. For example, the instruction unit can upload past first aid data to the cloud and integrate it with other users' data to optimize the instruction algorithm. By referencing the past first aid data, the instruction algorithm is optimized, and the accuracy of first aid instructions is improved. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input past first aid data into the generation AI and cause the generation AI to optimize the instruction algorithm.

[0099] The collaboration unit can estimate the user's emotions and adjust the collaboration method based on the estimated user's emotions. The collaboration unit can, for example, analyze the user's voice and facial expressions to estimate emotions. For example, the collaboration unit can use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. The collaboration unit can also use facial expression recognition technology to analyze the user's facial expressions and estimate emotions. For example, the collaboration unit can capture the user's facial expressions with a camera and estimate emotions using a facial expression recognition algorithm. The collaboration unit adjusts the collaboration method based on the estimated user's emotions. For example, if the user is nervous, the collaboration unit can provide a simple and easy-to-understand collaboration method. If the user is relaxed, the collaboration unit can provide a detailed collaboration method. Furthermore, if the user is panicked, the collaboration unit can provide a quick and concise collaboration method. This enables more appropriate collaboration by adjusting the collaboration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit may be performed using AI, or may be performed without using AI. For example, the collaboration unit may input user voice data into the generation AI and cause the generation AI to estimate emotions.

[0100] When cooperating, the cooperation unit can select the optimal cooperation method by referring to the user's past cooperation history. The cooperation unit, for example, analyzes the user's past cooperation history and selects the optimal cooperation method. For example, the cooperation unit can analyze the user's past cooperation history and select the optimal cooperation method. The cooperation unit can also prioritize and provide a specific cooperation method based on the user's past cooperation history. For example, the cooperation unit can prioritize and provide a specific cooperation method based on the user's past cooperation history. Furthermore, the cooperation unit can share the user's past cooperation history on the cloud and integrate it with data from other users to select the optimal cooperation method. For example, the cooperation unit can upload the user's past cooperation history to the cloud and integrate it with data from other users to select the optimal cooperation method. In this way, the optimal cooperation method can be selected by referring to the user's past cooperation history. Some or all of the above-described processing in the cooperation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooperation unit can input the user's past cooperation data into a generation AI and cause the generation AI to select the optimal cooperation method.

[0101] The cooperation unit can improve the accuracy of cooperation by integrating multiple cooperation means during cooperation. For example, the cooperation unit can provide a cooperation method by integrating audio guidance and text messages. For example, the cooperation unit can provide a cooperation method by integrating audio guidance and text messages. The cooperation unit can also provide a cooperation method by integrating video and text. For example, the cooperation unit can provide a cooperation method by integrating video and text. Furthermore, the cooperation unit can provide a cooperation method by integrating audio guidance and vibration. For example, the cooperation unit can provide a cooperation method by integrating audio guidance and vibration. In this way, the accuracy of cooperation is improved by integrating multiple cooperation means. Some or all of the above-described processing in the cooperation unit may be performed using AI, for example, or without AI. For example, the cooperation unit can input multiple cooperation means into the generation AI and cause the generation AI to improve the accuracy of cooperation.

[0102] During cooperation, the cooperation unit can optimize the cooperation algorithm by referring to past cooperation data. The cooperation unit, for example, analyzes past cooperation data to learn the optimal cooperation method. For example, the cooperation unit can analyze past cooperation data to learn the optimal cooperation method. The cooperation unit can also build an algorithm that provides an optimal cooperation method for a specific user based on the past cooperation data. For example, the cooperation unit can build an algorithm that provides an optimal cooperation method for a specific user based on the past cooperation data. Furthermore, the cooperation unit can share the past cooperation data on the cloud and integrate it with data from other users to optimize the cooperation algorithm. For example, the cooperation unit can upload the past cooperation data to the cloud and integrate it with data from other users to optimize the cooperation algorithm. This optimizes the cooperation algorithm by referring to the past cooperation data, thereby improving the cooperation accuracy. Some or all of the above-described processing in the cooperation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooperation unit can input the past cooperation data to a generation AI and cause the generation AI to optimize the cooperation algorithm.

[0103] The location information acquisition unit can estimate the user's emotion and adjust the frequency of location information acquisition based on the estimated user's emotion. The location information acquisition unit can, for example, analyze the user's voice and facial expression to estimate the emotion. For example, the location information acquisition unit can use voice analysis technology to analyze the tone and speed of the user's voice to estimate the emotion. The location information acquisition unit can also use facial expression recognition technology to analyze the user's facial expression and estimate the emotion. For example, the location information acquisition unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The location information acquisition unit adjusts the frequency of location information acquisition based on the estimated user's emotion. For example, if the user is nervous, the location information acquisition unit can increase the frequency of location information acquisition and update the location information in real time. Furthermore, if the user is relaxed, the location information acquisition unit can set the frequency of location information acquisition to normal to reduce battery consumption. Furthermore, if the user is in a panic state, the location information acquisition unit can maximize the frequency of location information acquisition and update the location information immediately. This allows for more appropriate location information acquisition by adjusting the frequency of location information acquisition according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the location information acquisition unit may be performed using AI, or may be performed without using AI. For example, the location information acquisition unit may input the user's voice data into the generation AI and cause the generation AI to estimate the emotion.

[0104] When acquiring location information, the location information acquisition unit can select an optimal acquisition method by taking into account the user's current activity status. For example, when the user is walking, the location information acquisition unit can acquire location information using GPS. For example, when the user is walking, the location information acquisition unit can acquire location information using GPS. Furthermore, when the user is in a car, the location information acquisition unit can acquire location information in cooperation with an in-car navigation system. For example, when the user is in a car, the location information acquisition unit can acquire location information in cooperation with an in-car navigation system. Furthermore, when the user is at home, the location information acquisition unit can acquire location information using Wi-Fi. For example, when the user is at home, the location information acquisition unit can acquire location information using Wi-Fi. This allows the optimal location information acquisition method to be selected by taking into account the user's current activity status. Some or all of the above-described processing in the location information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the location information acquisition unit can input the user's activity status data to a generation AI and cause the generation AI to select an optimal location information acquisition method.

[0105] The location information acquisition unit can improve the accuracy of location information acquisition by integrating multiple location information sources. The location information acquisition unit can acquire highly accurate location information by integrating location information from, for example, GPS, Wi-Fi, and Bluetooth. For example, the location information acquisition unit can acquire highly accurate location information by integrating location information from GPS, Wi-Fi, and Bluetooth. The location information acquisition unit can also improve the accuracy of acquisition by integrating location information from a smartphone sensor and an external device. For example, the location information acquisition unit can improve the accuracy of acquisition by integrating location information from a smartphone sensor and an external device. Furthermore, the location information acquisition unit can improve the accuracy of acquisition by integrating location information from multiple users in the cloud. For example, the location information acquisition unit can upload location information from multiple users to the cloud and integrate the data on the cloud to improve the accuracy of acquisition. In this way, the accuracy of location information acquisition is improved by integrating multiple location information sources. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can input multiple location information sources into the generation AI and have the generation AI integrate the data and acquire location information.

[0106] When acquiring location information, the location information acquisition unit can optimize the acquisition algorithm by referring to past location information data. For example, the location information acquisition unit can analyze past location information data, learn specific patterns, and optimize the acquisition algorithm. For example, the location information acquisition unit can analyze past location information data, learn specific patterns, and optimize the acquisition algorithm. The location information acquisition unit can also build an algorithm that improves the accuracy of acquiring location information in a specific area based on past location information data. For example, the location information acquisition unit can build an algorithm that improves the accuracy of acquiring location information in a specific area based on past location information data. Furthermore, the location information acquisition unit can share past location information data on the cloud and integrate it with other users' data to optimize the acquisition algorithm. For example, the location information acquisition unit can upload past location information data to the cloud and integrate it with other users' data to optimize the acquisition algorithm. By referencing past location information data, the acquisition algorithm is optimized, and the accuracy of location information acquisition is improved. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can input past location information data into the generation AI and cause the generation AI to optimize the acquisition algorithm.

[0107] The location information acquisition unit can estimate the user's emotion and adjust the notification method of location information acquisition based on the estimated user's emotion. The location information acquisition unit, for example, analyzes the user's voice and facial expression to estimate the emotion. For example, the location information acquisition unit can use voice analysis technology to analyze the tone and speed of the user's voice to estimate the emotion. The location information acquisition unit can also use facial expression recognition technology to analyze the user's facial expression and estimate the emotion. For example, the location information acquisition unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The location information acquisition unit adjusts the notification method of location information acquisition based on the estimated user's emotion. For example, if the user is nervous, the location information acquisition unit can tone down the notification of location information acquisition and notify the user by vibration. Furthermore, if the user is relaxed, the location information acquisition unit can notify the user of location information acquisition as usual. Furthermore, if the user is in a panic, the location information acquisition unit can emphasize the notification of location information acquisition to immediately attract the user's attention. This allows for more appropriate notification by adjusting the notification method of location information acquisition according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the location information acquisition unit may be performed using, or without, an AI. For example, the location information acquisition unit may input user voice data to the generation AI and cause the generation AI to estimate emotions.

[0108] The location information acquisition unit can improve the accuracy of location information acquisition by taking into account the geographical characteristics of the user. For example, if the user's current location is in a high-rise building, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the influence of the building. For example, if the user's current location is in a high-rise building, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the influence of the building. Furthermore, if the user's current location is in a subway, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the underground environment. For example, if the user's current location is in a subway, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the underground environment. Furthermore, if the user's current location is in a mountainous area, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the influence of the terrain. For example, if the user's current location is in a mountainous area, the location information acquisition unit can adjust the accuracy of location information acquisition by taking into account the influence of the terrain. This improves the accuracy of location information acquisition by taking into account the user's geographical characteristics. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can input the user's geographical characteristic data into the generation AI and cause the generation AI to improve acquisition accuracy.

[0109] When acquiring location information, the location information acquisition unit can acquire location information in cooperation with the user's smart home device. For example, when the user is at home, the location information acquisition unit can acquire location information using the location information of the smart home device. For example, when the user is at home, the location information acquisition unit can acquire location information using the location information of the smart home device. Furthermore, when the user is linked with a smart home device, the location information acquisition unit can improve acquisition accuracy by integrating the location information of the devices. For example, when the user is linked with a smart home device, the location information acquisition unit can improve acquisition accuracy by integrating the location information of the devices. Furthermore, when the user is carrying a smart home device, the location information acquisition unit can acquire location information using the location information of the device. For example, when the user is carrying a smart home device, the location information acquisition unit can acquire location information using the location information of the device. As a result, by linking with the smart home device, the accuracy of location information acquisition is improved. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can input location information data of a smart home device into the generation AI and cause the generation AI to acquire location information.

[0110] The location information acquisition unit can customize the acquisition algorithm by reflecting the user's past feedback when acquiring location information. For example, if the user has previously expressed dissatisfaction with the accuracy of location information acquisition, the location information acquisition unit can adjust the algorithm based on that feedback. For example, if the user has previously expressed dissatisfaction with the accuracy of location information acquisition, the location information acquisition unit can adjust the algorithm based on that feedback. Furthermore, if the user has previously expressed dissatisfaction with the notification method of location information acquisition, the location information acquisition unit can adjust the notification method based on that feedback. For example, if the user has previously expressed dissatisfaction with the notification method of location information acquisition, the location information acquisition unit can adjust the notification method based on that feedback. Furthermore, if the user has previously expressed dissatisfaction with the timing of location information acquisition, the location information acquisition unit can adjust the acquisition timing based on that feedback. For example, if the user has previously expressed dissatisfaction with the timing of location information acquisition, the location information acquisition unit can adjust the acquisition timing based on that feedback. In this way, by reflecting past feedback, the acquisition algorithm can be customized, and the accuracy of location information acquisition can be improved. Some or all of the above-described processing in the location information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information acquisition unit can input user feedback data into the generation AI and cause the generation AI to customize the acquisition algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the detection unit, activation unit, recognition unit, request unit, support unit, instruction unit, cooperation unit, and location information acquisition unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit can detect the occurrence of an earthquake using a sensor in the smart device 14. The activation unit automatically launches an app in the smart device 14 based on the earthquake detected by the detection unit. The recognition unit recognizes the user's voice using the microphone in the smart device 14. The request unit issues a rescue request based on the voice recognized by the recognition unit, acquires the user's location information using the specific processing unit 290 in the data processing device 12, and notifies the nearest rescue team. The support unit provides psychological support using the specific processing unit 290 in the data processing device 12. The instruction unit issues first aid instructions using the specific processing unit 290 in the data processing device 12. The cooperation unit promotes cooperation with the local area or community through the app in the smart device 14. The location information acquisition unit acquires the user's location information using the GPS function of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the detection unit, activation unit, recognition unit, request unit, support unit, instruction unit, cooperation unit, and location information acquisition unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit can detect the occurrence of an earthquake using a sensor in the smart glasses 214. The activation unit automatically launches an app in the smart glasses 214 based on the earthquake detected by the detection unit. The recognition unit recognizes the user's voice using a microphone in the smart glasses 214. The request unit issues a rescue request based on the voice recognized by the recognition unit, acquires the user's location information via the specific processing unit 290 in the data processing device 12, and notifies the nearest rescue team. The support unit provides psychological support via the specific processing unit 290 in the data processing device 12. The instruction unit issues first aid instructions via the specific processing unit 290 in the data processing device 12. The cooperation unit promotes cooperation with the local area or community through the app in the smart glasses 214. The location information acquisition unit acquires the user's location information using the GPS function of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the detection unit, activation unit, recognition unit, request unit, support unit, instruction unit, cooperation unit, and location information acquisition unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the detection unit can detect the occurrence of an earthquake using a sensor in the headset terminal 314. The activation unit automatically launches an app in the headset terminal 314 based on the earthquake detected by the detection unit. The recognition unit recognizes the user's voice using the microphone in the headset terminal 314. The request unit issues a rescue request based on the voice recognized by the recognition unit, acquires the user's location information using the identification processing unit 290 in the data processing device 12, and notifies the nearest rescue team. The support unit provides psychological support using the identification processing unit 290 in the data processing device 12. The instruction unit issues first aid instructions using the identification processing unit 290 in the data processing device 12. The cooperation unit promotes cooperation with the local area or community through the app in the headset terminal 314. The location information acquisition unit acquires the user's location information using the GPS function of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the detection unit, activation unit, recognition unit, request unit, support unit, instruction unit, cooperation unit, and location information acquisition unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit can detect the occurrence of an earthquake using a sensor in the robot 414. The activation unit automatically launches an app for the robot 414 based on the earthquake detected by the detection unit. The recognition unit recognizes the user's voice using the microphone in the robot 414. The request unit issues a rescue request based on the voice recognized by the recognition unit, acquires the user's location information using the specific processing unit 290 in the data processing device 12, and notifies the nearest rescue team. The support unit provides psychological support using the specific processing unit 290 in the data processing device 12. The instruction unit issues first aid instructions using the specific processing unit 290 in the data processing device 12. The cooperation unit promotes cooperation with the local area or community through the app for the robot 414. The location information acquisition unit acquires the user's location information using the GPS function of the robot 414.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The disaster support system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit can monitor the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time and issue an alert if an abnormality is detected. For example, if the user's heart rate suddenly increases, the health monitoring unit can automatically request rescue. If the user's body temperature is abnormally high, the health monitoring unit can warn of the risk of heatstroke. Furthermore, if the user's blood pressure is abnormally low, the health monitoring unit can notify the user of the risk of hypotension. This allows the user's health condition to be constantly monitored and a prompt response can be made.

[0113] The disaster support system can further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit can analyze the user's past behavioral patterns and predict behavior during a disaster. For example, if the user has a history of evacuating to an evacuation shelter in the past, the behavior analysis unit can suggest the optimal evacuation route based on that information. Also, if the user has a history of staying at a specific location for a long time in the past, the behavior analysis unit can suggest that location as a preferred evacuation destination. Furthermore, the behavior analysis unit can predict behavior during a disaster based on the user's past behavioral data and provide appropriate support. This makes it possible to utilize the user's behavioral history to respond to disasters more effectively.

[0114] The disaster support system may further include a music providing unit that estimates the user's emotions and provides appropriate music based on the estimated emotions. The music providing unit may analyze the user's emotions and provide relaxing music or encouraging music. For example, if the user is nervous, the music providing unit may play relaxing music. If the user is in a state of panic, the music providing unit may play encouraging music. If the user is relaxed, the music providing unit may play normal music. This may provide music according to the user's emotions, thereby enhancing psychological support.

[0115] The disaster support system can further include an environmental monitoring unit that monitors the environment around the user. The environmental monitoring unit monitors environmental data such as ambient temperature, humidity, and air pressure in real time, and can issue an alert if it detects an abnormality. For example, if the ambient temperature rises suddenly, the environmental monitoring unit can warn of the risk of heatstroke. If the ambient humidity is abnormally high, the environmental monitoring unit can notify of the risk of mold growth. Furthermore, if the ambient air pressure drops suddenly, the environmental monitoring unit can warn of the risk of weather fluctuations. This allows the user's surrounding environment to be constantly monitored, enabling rapid response.

[0116] The disaster support system may further include a message providing unit that estimates the user's emotions and provides an appropriate message based on the estimated emotions. The message providing unit may analyze the user's emotions and provide an encouraging message or a message with a relaxing effect. For example, if the user is nervous, the message providing unit may display a message such as "It's okay, please stay calm." If the user is in a state of panic, the message providing unit may display a message such as "Take a deep breath and stay calm." If the user is relaxed, the message providing unit may display a message such as "Just stay relaxed." In this way, psychological support can be enhanced by providing a message that corresponds to the user's emotions.

[0117] The disaster support system can further include an action suggestion unit that estimates the user's emotions and suggests appropriate actions based on the estimated emotions. The action suggestion unit can analyze the user's emotions and suggest actions that have a relaxing effect or actions that are encouraging. For example, if the user is nervous, the action suggestion unit can suggest an action such as "Try taking a deep breath." If the user is in a state of panic, the action suggestion unit can suggest an action such as "Sit down and calm down." If the user is relaxed, the action suggestion unit can suggest an action such as "Just stay relaxed." In this way, psychological support can be strengthened by suggesting actions according to the user's emotions.

[0118] The disaster support system may further include a relaxation providing unit that estimates the user's emotions and provides an appropriate relaxation method based on the estimated emotions. The relaxation providing unit can analyze the user's emotions and provide methods that have a relaxing effect. For example, if the user is tense, the relaxation providing unit may suggest a method such as "Try taking a deep breath." If the user is in a state of panic, the relaxation providing unit may suggest a method such as "Try sitting down and closing your eyes." If the user is relaxed, the relaxation providing unit may suggest a method such as "Just stay relaxed." This allows for enhanced psychological support by providing relaxation methods that correspond to the user's emotions.

[0119] The disaster support system may further include an exercise suggestion unit that estimates the user's emotions and suggests appropriate exercises based on the estimated emotions. The exercise suggestion unit can analyze the user's emotions and suggest exercises that have a relaxing effect or exercises to relieve stress. For example, if the user is tense, the exercise suggestion unit may suggest an exercise such as "try some light stretching." If the user is in a state of panic, the exercise suggestion unit may suggest an exercise such as "try walking slowly while taking deep breaths." If the user is relaxed, the exercise suggestion unit may suggest an exercise such as "just stay relaxed." In this way, psychological support can be enhanced by suggesting exercises that correspond to the user's emotions.

[0120] The disaster support system may further include a reminder providing unit that estimates the user's emotions and provides appropriate reminders based on the estimated emotions. The reminder providing unit may analyze the user's emotions and provide relaxing reminders or encouraging reminders. For example, if the user is nervous, the reminder providing unit may display a reminder such as "Don't forget to take a deep breath." If the user is in a panic, the reminder providing unit may display a reminder such as "Remain calm and collected." If the user is relaxed, the reminder providing unit may display a reminder such as "Stay relaxed." This allows for enhanced psychological support by providing reminders according to the user's emotions.

[0121] The disaster support system may further include an advice providing unit that estimates the user's emotions and provides appropriate advice based on the estimated emotions. The advice providing unit may analyze the user's emotions and provide relaxing advice or encouraging advice. For example, if the user is nervous, the advice providing unit may display advice such as "Take a deep breath and relax." If the user is in a panic, the advice providing unit may display advice such as "Calm down and deal with the situation calmly." If the user is relaxed, the advice providing unit may display advice such as "Just stay relaxed." This allows for enhanced psychological support by providing advice according to the user's emotions.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The detection unit detects the occurrence of an earthquake. Earthquakes can be detected using a seismometer or a smartphone's sensors. For example, a seismometer detects seismic waves and measures the seismic intensity. Smartphone sensors can detect earthquake shaking using an acceleration sensor or gyro sensor. Step 2: The launching unit automatically launches the app based on the earthquake detected by the detection unit. For example, the app can be automatically launched when an earthquake with a seismic intensity of 7 or higher is detected. Step 3: The recognition unit recognizes the user's voice. Speech recognition technology can be used to recognize the user's voice. For example, the speech recognition technology can recognize the user's voice crying out "help" and issue a rescue request. Step 4: The request unit issues a rescue request based on the voice recognized by the recognition unit. For example, the request unit can obtain location information of the user and notify the nearest rescue team. Step 5: The support unit provides psychological support. For example, if the user is in a panic state, the support unit can provide appropriate psychological support. For example, the support unit can use relaxation techniques to calm the user. Step 6: The instruction unit issues first aid instructions. For example, if first aid is required, the instruction unit can instruct specific procedures. For example, the instruction unit can instruct first aid procedures such as how to stop bleeding and cardiopulmonary resuscitation.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] 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.

[0187] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0189] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0193] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a detection unit that detects the occurrence of an earthquake; a launching unit that automatically launches an application based on the earthquake detected by the detection unit; a recognition unit that recognizes the user's voice; a request unit that makes a rescue request based on the voice recognized by the recognition unit; Support department that provides psychological support; an instruction unit that gives first aid instructions; Equipped with A system characterized by:

2. The request unit: Obtain the user's location and notify the nearest rescue team 2. The system of claim 1.

3. The support portion is Providing appropriate psychological support when users are in a panic state 2. The system of claim 1.

4. The instruction unit Provide specific instructions if first aid is required 2. The system of claim 1.

5. Establish a cooperation department to promote cooperation with local and community organizations 2. The system of claim 1.

6. A location information acquisition unit is provided to acquire user location information.

2. The system of claim 1.

7. The detection unit Estimate the user's emotions and adjust the sensitivity of earthquake detection based on the estimated user emotions.

2. The system of claim 1.

8. The detection unit Detecting earthquake precursors and issuing advance warnings 2. The system of claim 1.

9. The detection unit Integrating data from multiple sensors to improve detection accuracy 2. The system of claim 1.

10. The detection unit Optimizing detection algorithms by referencing historical earthquake data 2. The system of claim 1.

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