system

Abstract

The system according to this embodiment aims to provide rapid and appropriate psychological support and first aid instructions in the event of an earthquake of magnitude 7 or higher, and to promote mutual assistance in cooperation with local communities. [Solution] The system according to this embodiment comprises an activation unit, a recognition unit, a provision unit, and a cooperation unit. The activation unit is activated automatically when an earthquake of magnitude 7 or higher occurs. The recognition unit recognizes the user's voice and actions after being activated by the activation unit. The provision unit provides psychological support and first aid instructions in real time based on the information recognized by the recognition unit. The cooperation unit promotes mutual assistance by coordinating with local communities and regions based on the information provided by the provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that when an earthquake with a seismic intensity of magnitude 7 or more occurred, rapid and appropriate psychological support and instructions for emergency treatment were not sufficiently provided, and cooperation with regions and communities to promote mutual assistance was not sufficiently carried out.

[0005] The system according to the embodiment aims to provide rapid and appropriate psychological support and instructions for emergency treatment when an earthquake with a seismic intensity of magnitude 7 or more occurs, and to promote mutual assistance in cooperation with regions and communities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an activation unit, a recognition unit, a provision unit, and a collaboration unit. The activation unit is activated automatically when an earthquake of magnitude 7 or higher occurs. The recognition unit recognizes the user's voice and actions after being activated by the activation unit. The provision unit provides psychological support and first aid instructions in real time based on the information recognized by the recognition unit. The collaboration unit promotes mutual assistance by collaborating with local communities based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide rapid and appropriate psychological support and first aid instructions when an earthquake of magnitude 7 or higher occurs, and can promote mutual assistance in cooperation with local communities. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The disaster support system according to an embodiment of the present invention is a rescue application that utilizes a "small AI" that operates on a smartphone when a disaster occurs. When an earthquake of magnitude 7 or higher occurs, the edge AI automatically activates to request rescue and provide assistance. This rescue application uses AI to recognize the user's voice and actions and provides psychological support and first aid instructions in real time. It also promotes mutual assistance in cooperation with local communities and reduces the burden on the rescue system. For example, when an earthquake of magnitude 7 or higher occurs, the edge AI automatically activates to request rescue and provide assistance, enabling a rapid response. In addition, the AI ​​recognizes the user's voice and actions and provides psychological support and first aid instructions in real time, which can increase the sense of security of disaster victims. Furthermore, by promoting mutual assistance in cooperation with local communities and other organizations, the burden on the rescue system is reduced, enabling efficient rescue operations. As a result, the disaster support system can provide maximum support during a disaster and protect the lives and safety of disaster victims.

[0029] The disaster relief system according to this embodiment comprises an activation unit, a recognition unit, a provision unit, and a coordination unit. The activation unit is activated automatically when an earthquake of magnitude 7 or higher occurs. For example, the activation unit monitors seismometer data in real time and is activated automatically when an earthquake of magnitude 7 or higher is detected. The activation unit can also set the type of sensor and the activation trigger. The recognition unit recognizes the user's voice and actions. For example, the recognition unit recognizes the user's voice using speech recognition technology. The recognition unit can also recognize the user's actions using an action detection algorithm. The provision unit provides psychological support and first aid instructions in real time based on the information recognized by the recognition unit. For example, the provision unit provides psychological support using counseling and relaxation techniques. The provision unit can also provide first aid procedures and instruction formats. The coordination unit promotes mutual assistance by coordinating with local areas and communities based on the information provided by the provision unit. The coordination unit can, for example, set the means of coordination and the method of information sharing. As a result, the disaster relief system according to this embodiment can be automatically activated when an earthquake of magnitude 7 or higher occurs, recognize the user's voice and actions to provide psychological support and first aid instructions in real time, and promote mutual assistance in cooperation with local communities.

[0030] The activation unit automatically starts up when an earthquake of magnitude 7 or higher occurs. For example, the activation unit monitors seismometer data in real time and automatically starts up when an earthquake of magnitude 7 or higher is detected. Specifically, the seismometer detects seismic waves and transmits the data to a central monitoring system. The monitoring system analyzes the received data and calculates the seismic intensity. If the seismic intensity is determined to be 7 or higher, the activation unit immediately starts up the entire system. The activation unit can also configure the types of sensors and the activation triggers. For example, in addition to seismometers, acceleration sensors and vibration sensors can be added, and the system can be activated when these sensors exceed a certain threshold. Furthermore, the activation unit also provides an interface for users to manually start the system. This allows users to manually start the system as needed and respond quickly. The activation unit also has a function to monitor the system's startup status and issue warnings if abnormalities occur. For example, if a sensor fails or a communication failure occurs, the activation unit will notify the user of this information and prompt a quick response. In this way, the activation unit can quickly and reliably start up the system when an earthquake occurs, ensuring the safety of the user.

[0031] The recognition unit recognizes the user's voice and actions. For example, the recognition unit recognizes the user's voice using speech recognition technology. Specifically, speech recognition technology collects the user's speech with a microphone and analyzes the audio data. The audio data undergoes preprocessing such as noise reduction and speech enhancement before being input to the speech recognition engine. The speech recognition engine converts the audio data into text data and analyzes the user's intent. The recognition unit can also recognize the user's actions using an action detection algorithm. The action detection algorithm captures the user's actions using a camera or motion sensor and analyzes the data. For example, it detects the user's posture and movement from camera footage and analyzes acceleration data obtained from motion sensors to identify the user's actions. This allows the recognition unit to accurately recognize the user's voice and actions and reflect them in the operation and instructions of the entire system. Furthermore, the recognition unit can learn patterns of the user's voice and actions and improve recognition accuracy to be optimized for individual users. For example, it can learn the characteristics of the user's speech and movement habits to improve recognition accuracy. In addition, the recognition unit can recognize multiple users simultaneously, enabling efficient support for multiple users during disasters. This allows the recognition unit to recognize the user's voice and actions with high accuracy, improving the overall operability and reliability of the system.

[0032] The service provider provides psychological support and first aid instructions in real time based on information recognized by the recognition unit. For example, the service provider offers psychological support using counseling and relaxation techniques. Specifically, it provides users with advice on relaxation methods and stress reduction through audio guides and video content. The audio guide selects appropriate counseling content according to the user's situation and reassures the user with a gentle voice. It also provides instructions on relaxation techniques such as deep breathing and meditation to help users calm down. Furthermore, the service provider can also provide first aid procedures and instructions. For example, it clearly explains first aid methods for injuries and emergency response procedures through audio and video. If a user is injured, the service provider shows specific procedures such as how to stop bleeding and how to apply bandages, supporting the user in performing appropriate first aid. The service provider can provide the most suitable support in real time according to the user's situation and needs. For example, if a user is in a panic state, it prioritizes counseling to calm them down; if an injured user is injured, it prioritizes first aid instructions. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its support. This allows the service provider to provide users with prompt and appropriate psychological support and first aid instructions, ensuring their safety and peace of mind during disasters.

[0033] The Liaison Department promotes mutual assistance by collaborating with local communities based on information provided by the Provision Department. For example, the Liaison Department can set the means of collaboration and methods of information sharing. Specifically, it collaborates with local disaster prevention organizations and community groups to provide a platform for efficient information sharing and support activities during disasters. The Liaison Department can share information in real time via the internet and mobile networks to grasp the situation throughout the region. For example, it can quickly share the extent of damage and the need for support during a disaster, enabling the entire region to cooperate in a response. The Liaison Department also collaborates with local leaders and volunteers to coordinate support activities and allocate resources. For example, it can manage evacuation centers, distribute supplies, and coordinate rescue operations, enabling efficient support activities throughout the region. Furthermore, the Liaison Department supports not only information sharing during disasters but also disaster prevention training and awareness-raising activities during normal times. For example, it supports the planning and implementation of local disaster prevention training and provides information to raise residents' awareness of disaster prevention. In this way, the Liaison Department can strengthen collaboration with local communities not only during disasters but also during normal times, ensuring thorough preparedness for disasters. The Collaboration Department can collect user feedback and continuously improve collaboration methods and information sharing techniques. This allows the Collaboration Department to strengthen ties with local communities and promote mutual assistance during disasters.

[0034] The activation unit can be automatically activated when an earthquake of magnitude 7 or higher occurs. For example, the activation unit monitors seismometer data in real time and automatically activates when an earthquake of magnitude 7 or higher is detected. The activation unit can also be configured with sensor types and activation triggers. This allows for a rapid response by automatically activating when an earthquake of magnitude 7 or higher occurs.

[0035] The recognition unit can recognize the user's voice and actions. For example, it can recognize the user's voice using speech recognition technology. It can also recognize the user's actions using an action detection algorithm. This allows for appropriate responses by recognizing the user's voice and actions.

[0036] The support unit can provide psychological support and first aid instructions in real time. For example, the support unit can provide psychological support using counseling and relaxation techniques. The support unit can also provide first aid procedures and instructions in a format. This real-time provision of psychological support and first aid instructions can enhance the sense of security for disaster victims.

[0037] The liaison department can promote mutual assistance by collaborating with local communities. For example, the liaison department can set up methods for collaboration and information sharing. This allows for reduced burden on the rescue system and more efficient rescue operations by promoting mutual assistance in collaboration with local communities.

[0038] The system allows users to send rescue requests by voice, even if they don't have a smartphone. For example, the system uses voice recognition technology to recognize the user's voice and send a rescue request. Furthermore, the system can analyze the user's voice in real time to make an appropriate rescue request. This enables users to send rescue requests by voice, even without a smartphone, facilitating rapid rescue efforts.

[0039] The service provider can communicate even offline. For example, it can provide necessary information offline using a local database. Furthermore, it can achieve natural-sounding conversations offline using speech synthesis technology. This allows for communication even offline, ensuring appropriate support is provided even if communication is interrupted.

[0040] The service provider can send and receive safety information between devices in an emergency, synchronize it via the cloud when communication is possible, and synchronize it between devices using Wi-Fi ad-hoc communication when communication is not possible. For example, the service provider can set up a communication protocol to quickly send and receive safety information. In addition, the service provider can synchronize safety information between devices using Wi-Fi ad-hoc communication even when communication is not possible. As a result, in an emergency, the safety of disaster victims can be quickly confirmed by sending and receiving safety information between devices, synchronizing it via the cloud when communication is possible, and synchronizing it between devices using Wi-Fi ad-hoc communication when communication is not possible.

[0041] The activation unit can determine activation priorities based on the time and location of an earthquake. For example, the activation unit monitors seismometer data in real time to obtain the time and location of an earthquake. The activation unit can also set activation priorities based on the time and location of an earthquake. This allows for a rapid response by determining activation priorities based on the time and location of an earthquake.

[0042] The startup unit can select the optimal startup method by referring to the user's past behavior history at startup. For example, the startup unit can retrieve and refer to the user's past behavior history from a database. Furthermore, the startup unit can select the optimal startup method based on the user's past behavior history. This allows for more appropriate responses by selecting the optimal startup method based on the user's past behavior history.

[0043] The activation unit can adjust the activation range based on the magnitude and affected area of ​​the earthquake. For example, the activation unit monitors seismometer data in real time to obtain the earthquake's magnitude and affected area. The activation unit can also set the activation range based on the earthquake's magnitude and affected area. This allows for the provision of necessary support by adjusting the activation range based on the earthquake's magnitude and affected area.

[0044] The startup unit can select the optimal startup method at startup, taking into account the battery level of the user's device. For example, the startup unit can retrieve and refer to the battery level of the user's device from a database. Alternatively, the startup unit can select the optimal startup method based on the battery level of the user's device. This allows for minimizing battery consumption by selecting the optimal startup method based on the battery level of the user's device.

[0045] The recognition unit can improve recognition accuracy by analyzing the tone and speed of the user's voice during recognition. For example, the recognition unit uses speech analysis technology to analyze the tone and speed of the user's voice. Furthermore, the recognition unit can also improve recognition accuracy based on the tone and speed of the user's voice using a speech recognition algorithm. This allows for more accurate recognition by analyzing the tone and speed of the user's voice to improve recognition accuracy.

[0046] The recognition unit can select the optimal recognition method by referring to the user's behavior patterns during recognition. For example, the recognition unit analyzes the user's behavior patterns using a behavior detection algorithm. Furthermore, the recognition unit can select the optimal recognition method based on the user's behavior patterns. This allows for a more appropriate response by selecting the optimal recognition method based on the user's behavior patterns.

[0047] The recognition unit can improve recognition accuracy by taking into account the ambient sounds surrounding the user during recognition. For example, the recognition unit can remove ambient noise using ambient sound filtering technology. Furthermore, the recognition unit can also improve recognition accuracy based on the ambient sounds surrounding the user using a speech recognition algorithm. This allows for more accurate recognition by improving recognition accuracy while considering the ambient sounds surrounding the user.

[0048] The recognition unit can improve the accuracy of recognition by referring to the sensor information of the user's device during recognition. For example, the recognition unit can accurately recognize the user's movements by referring to the device's accelerometer sensor information. The recognition unit can also determine the user's current location by referring to the device's location information. By improving the accuracy of recognition by referring to the sensor information of the user's device, more accurate recognition becomes possible.

[0049] The service provider can provide optimal instructions by referring to the user's past first aid history at the time of service provision. For example, the service provider can retrieve and refer to the user's past first aid history from a database. The service provider can also provide optimal instructions based on the user's past first aid history. This allows for more appropriate first aid by providing optimal instructions by referring to the user's past first aid history.

[0050] The service provider can customize the information provided at the time of delivery, taking into account the user's current health condition. For example, the service provider can retrieve and refer to the user's current health condition from a database. Furthermore, the service provider can customize the information provided based on the user's current health condition. This allows for more appropriate first aid by customizing the information provided based on the user's current health condition.

[0051] The service provider can select the optimal display method at the time of delivery, taking into account the screen size of the user's device. For example, the service provider can retrieve and refer to the screen size of the user's device from a database. Furthermore, the service provider can select the optimal display method based on the screen size of the user's device. This allows for the provision of more easily viewable information by selecting the optimal display method considering the screen size of the user's device.

[0052] The service provider can make the information provided multilingual by referring to the user's language settings at the time of delivery. For example, the service provider can retrieve and refer to the user's device language settings from a database. Furthermore, the service provider can make the information provided multilingual based on the user's language settings. This allows for the provision of more appropriate information by making the information provided multilingual by referring to the user's language settings.

[0053] The collaboration unit can select the optimal collaboration method by referring to local disaster information during collaboration. For example, the collaboration unit can obtain and refer to local disaster information from a database. Furthermore, the collaboration unit can select the optimal collaboration method based on local disaster information. This allows for more appropriate collaboration by selecting the optimal collaboration method based on local disaster information.

[0054] The integration unit can improve the accuracy of integration by referring to the community's past integration history during integration. For example, the integration unit retrieves and refers to the community's past integration history from a database. Furthermore, the integration unit can improve the accuracy of integration based on the community's past integration history. This allows for more appropriate integration by improving the accuracy of integration by referring to the community's past integration history.

[0055] The integration unit can select the optimal integration method when integrating, taking into account the local communication infrastructure situation. For example, the integration unit can retrieve and refer to the local communication infrastructure situation from a database. Furthermore, the integration unit can select the optimal integration method based on the local communication infrastructure situation. This allows for more appropriate integration by selecting the optimal integration method while considering the local communication infrastructure situation.

[0056] The integration unit can improve the accuracy of integration by referring to the attribute information of community members during the integration process. For example, the integration unit retrieves and refers to the attribute information of community members from a database. Furthermore, the integration unit can improve the accuracy of integration based on the attribute information of community members. By improving the accuracy of integration by referring to the attribute information of community members, more appropriate integration becomes possible.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The activation unit can acquire the user's location information when an earthquake occurs and provide information about the nearest evacuation shelter. For example, it can acquire GPS data when an earthquake occurs to determine the user's current location. The activation unit can also refer to a database of evacuation shelters and notify the user of the location of the nearest shelter. This allows the user to quickly head to an evacuation shelter and ensure their safety.

[0059] The service provider can monitor the user's health status and contact medical institutions as needed. For example, it can measure the user's heart rate and body temperature with sensors and notify medical institutions if an abnormality is detected. The service provider can also provide instructions for first aid tailored to the user's health condition. This protects the user's health and enables a rapid medical response.

[0060] The liaison unit can collect and provide local volunteer information to users during disasters. For example, it can refer to a database of local volunteer organizations to obtain information on volunteers who are active during a disaster. The liaison unit can also provide information on the nearest volunteers based on the user's location. This allows users to receive assistance quickly.

[0061] The startup unit can consider the user's device's battery level during a disaster and start in an optimal power-saving mode. For example, if the battery level is low, it will only activate the minimum necessary functions. The startup unit can also automatically configure settings to minimize battery consumption. This allows for extended use and ensures communication during disasters.

[0062] The service provider can refer to a user's past movement history during a disaster to suggest the optimal evacuation route. For example, it can retrieve the user's past movement history from a database and calculate the optimal evacuation route. Furthermore, the service provider can update evacuation route information in real time and notify the user. This allows users to evacuate quickly and safely.

[0063] The service provider can monitor the communication status of users' devices during a disaster and select the optimal communication method. For example, if the communication status is unstable, it will use Wi-Fi ad-hoc communication. The service provider can also adjust the method of sending and receiving information according to the communication status. This ensures stable communication even during a disaster, enabling rapid information sharing.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The activation unit automatically activates when an earthquake of magnitude 7 or higher occurs. The activation unit monitors seismometer data in real time and automatically activates when an earthquake of magnitude 7 or higher is detected. The activation unit can also be configured with the type of sensor and the activation trigger. Step 2: The recognition unit recognizes the user's voice and actions after being activated by the activation unit. The recognition unit can recognize the user's voice using speech recognition technology and recognize the user's actions using an action detection algorithm. Step 3: The service provider provides psychological support and first aid instructions in real time based on the information recognized by the recognition unit. The service provider can provide psychological support using counseling and relaxation techniques, and can also provide first aid procedures and instructions in various formats. Step 4: The Collaboration Department promotes mutual assistance by collaborating with local communities based on the information provided by the Provision Department. The Collaboration Department can set the means of collaboration and the methods of information sharing.

[0066] (Example of form 2) The disaster support system according to an embodiment of the present invention is a rescue application that utilizes a "small AI" that operates on a smartphone when a disaster occurs. When an earthquake of magnitude 7 or higher occurs, the edge AI automatically activates to request rescue and provide assistance. This rescue application uses AI to recognize the user's voice and actions and provides psychological support and first aid instructions in real time. It also promotes mutual assistance in cooperation with local communities and reduces the burden on the rescue system. For example, when an earthquake of magnitude 7 or higher occurs, the edge AI automatically activates to request rescue and provide assistance, enabling a rapid response. In addition, the AI ​​recognizes the user's voice and actions and provides psychological support and first aid instructions in real time, which can increase the sense of security of disaster victims. Furthermore, by promoting mutual assistance in cooperation with local communities and other organizations, the burden on the rescue system is reduced, enabling efficient rescue operations. As a result, the disaster support system can provide maximum support during a disaster and protect the lives and safety of disaster victims.

[0067] The disaster relief system according to this embodiment comprises an activation unit, a recognition unit, a provision unit, and a coordination unit. The activation unit is activated automatically when an earthquake of magnitude 7 or higher occurs. For example, the activation unit monitors seismometer data in real time and is activated automatically when an earthquake of magnitude 7 or higher is detected. The activation unit can also set the type of sensor and the activation trigger. The recognition unit recognizes the user's voice and actions. For example, the recognition unit recognizes the user's voice using speech recognition technology. The recognition unit can also recognize the user's actions using an action detection algorithm. The provision unit provides psychological support and first aid instructions in real time based on the information recognized by the recognition unit. For example, the provision unit provides psychological support using counseling and relaxation techniques. The provision unit can also provide first aid procedures and instruction formats. The coordination unit promotes mutual assistance by coordinating with local areas and communities based on the information provided by the provision unit. The coordination unit can, for example, set the means of coordination and the method of information sharing. As a result, the disaster relief system according to this embodiment can be automatically activated when an earthquake of magnitude 7 or higher occurs, recognize the user's voice and actions to provide psychological support and first aid instructions in real time, and promote mutual assistance in cooperation with local communities.

[0068] The activation unit automatically starts up when an earthquake of magnitude 7 or higher occurs. For example, the activation unit monitors seismometer data in real time and automatically starts up when an earthquake of magnitude 7 or higher is detected. Specifically, the seismometer detects seismic waves and transmits the data to a central monitoring system. The monitoring system analyzes the received data and calculates the seismic intensity. If the seismic intensity is determined to be 7 or higher, the activation unit immediately starts up the entire system. The activation unit can also configure the types of sensors and the activation triggers. For example, in addition to seismometers, acceleration sensors and vibration sensors can be added, and the system can be activated when these sensors exceed a certain threshold. Furthermore, the activation unit also provides an interface for users to manually start the system. This allows users to manually start the system as needed and respond quickly. The activation unit also has a function to monitor the system's startup status and issue warnings if abnormalities occur. For example, if a sensor fails or a communication failure occurs, the activation unit will notify the user of this information and prompt a quick response. In this way, the activation unit can quickly and reliably start up the system when an earthquake occurs, ensuring the safety of the user.

[0069] The recognition unit recognizes the user's voice and actions. For example, the recognition unit recognizes the user's voice using speech recognition technology. Specifically, speech recognition technology collects the user's speech with a microphone and analyzes the audio data. The audio data undergoes preprocessing such as noise reduction and speech enhancement before being input to the speech recognition engine. The speech recognition engine converts the audio data into text data and analyzes the user's intent. The recognition unit can also recognize the user's actions using an action detection algorithm. The action detection algorithm captures the user's actions using a camera or motion sensor and analyzes the data. For example, it detects the user's posture and movement from camera footage and analyzes acceleration data obtained from motion sensors to identify the user's actions. This allows the recognition unit to accurately recognize the user's voice and actions and reflect them in the operation and instructions of the entire system. Furthermore, the recognition unit can learn patterns of the user's voice and actions and improve recognition accuracy to be optimized for individual users. For example, it can learn the characteristics of the user's speech and movement habits to improve recognition accuracy. In addition, the recognition unit can recognize multiple users simultaneously, enabling efficient support for multiple users during disasters. This allows the recognition unit to recognize the user's voice and actions with high accuracy, improving the overall operability and reliability of the system.

[0070] The service provider provides psychological support and first aid instructions in real time based on information recognized by the recognition unit. For example, the service provider offers psychological support using counseling and relaxation techniques. Specifically, it provides users with advice on relaxation methods and stress reduction through audio guides and video content. The audio guide selects appropriate counseling content according to the user's situation and reassures the user with a gentle voice. It also provides instructions on relaxation techniques such as deep breathing and meditation to help users calm down. Furthermore, the service provider can also provide first aid procedures and instructions. For example, it clearly explains first aid methods for injuries and emergency response procedures through audio and video. If a user is injured, the service provider shows specific procedures such as how to stop bleeding and how to apply bandages, supporting the user in performing appropriate first aid. The service provider can provide the most suitable support in real time according to the user's situation and needs. For example, if a user is in a panic state, it prioritizes counseling to calm them down; if an injured user is injured, it prioritizes first aid instructions. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its support. This allows the service provider to provide users with prompt and appropriate psychological support and first aid instructions, ensuring their safety and peace of mind during disasters.

[0071] The Liaison Department promotes mutual assistance by collaborating with local communities based on information provided by the Provision Department. For example, the Liaison Department can set the means of collaboration and methods of information sharing. Specifically, it collaborates with local disaster prevention organizations and community groups to provide a platform for efficient information sharing and support activities during disasters. The Liaison Department can share information in real time via the internet and mobile networks to grasp the situation throughout the region. For example, it can quickly share the extent of damage and the need for support during a disaster, enabling the entire region to cooperate in a response. The Liaison Department also collaborates with local leaders and volunteers to coordinate support activities and allocate resources. For example, it can manage evacuation centers, distribute supplies, and coordinate rescue operations, enabling efficient support activities throughout the region. Furthermore, the Liaison Department supports not only information sharing during disasters but also disaster prevention training and awareness-raising activities during normal times. For example, it supports the planning and implementation of local disaster prevention training and provides information to raise residents' awareness of disaster prevention. In this way, the Liaison Department can strengthen collaboration with local communities not only during disasters but also during normal times, ensuring thorough preparedness for disasters. The Collaboration Department can collect user feedback and continuously improve collaboration methods and information sharing techniques. This allows the Collaboration Department to strengthen ties with local communities and promote mutual assistance during disasters.

[0072] The activation unit can be automatically activated when an earthquake of magnitude 7 or higher occurs. For example, the activation unit monitors seismometer data in real time and automatically activates when an earthquake of magnitude 7 or higher is detected. The activation unit can also be configured with sensor types and activation triggers. This allows for a rapid response by automatically activating when an earthquake of magnitude 7 or higher occurs.

[0073] The recognition unit can recognize the user's voice and actions. For example, it can recognize the user's voice using speech recognition technology. It can also recognize the user's actions using an action detection algorithm. This allows for appropriate responses by recognizing the user's voice and actions.

[0074] The support unit can provide psychological support and first aid instructions in real time. For example, the support unit can provide psychological support using counseling and relaxation techniques. The support unit can also provide first aid procedures and instructions in a format. This real-time provision of psychological support and first aid instructions can enhance the sense of security for disaster victims.

[0075] The liaison department can promote mutual assistance by collaborating with local communities. For example, the liaison department can set up methods for collaboration and information sharing. This allows for reduced burden on the rescue system and more efficient rescue operations by promoting mutual assistance in collaboration with local communities.

[0076] The system allows users to send rescue requests by voice, even if they don't have a smartphone. For example, the system uses voice recognition technology to recognize the user's voice and send a rescue request. Furthermore, the system can analyze the user's voice in real time to make an appropriate rescue request. This enables users to send rescue requests by voice, even without a smartphone, facilitating rapid rescue efforts.

[0077] The service provider can communicate even offline. For example, it can provide necessary information offline using a local database. Furthermore, it can achieve natural-sounding conversations offline using speech synthesis technology. This allows for communication even offline, ensuring appropriate support is provided even if communication is interrupted.

[0078] The service provider can send and receive safety information between devices in an emergency, synchronize it via the cloud when communication is possible, and synchronize it between devices using Wi-Fi ad-hoc communication when communication is not possible. For example, the service provider can set up a communication protocol to quickly send and receive safety information. In addition, the service provider can synchronize safety information between devices using Wi-Fi ad-hoc communication even when communication is not possible. As a result, in an emergency, the safety of disaster victims can be quickly confirmed by sending and receiving safety information between devices, synchronizing it via the cloud when communication is possible, and synchronizing it between devices using Wi-Fi ad-hoc communication when communication is not possible.

[0079] The activation unit can estimate the user's emotions and adjust the activation timing based on the estimated emotions. For example, the activation unit can estimate the user's emotions using an emotion recognition algorithm. Furthermore, the activation unit can set sensor types and emotion criteria and adjust the activation timing based on the user's emotions. This allows for activation at a more appropriate time by adjusting the activation timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The activation unit can determine activation priorities based on the time and location of an earthquake. For example, the activation unit monitors seismometer data in real time to obtain the time and location of an earthquake. The activation unit can also set activation priorities based on the time and location of an earthquake. This allows for a rapid response by determining activation priorities based on the time and location of an earthquake.

[0081] The startup unit can select the optimal startup method by referring to the user's past behavior history at startup. For example, the startup unit can retrieve and refer to the user's past behavior history from a database. Furthermore, the startup unit can select the optimal startup method based on the user's past behavior history. This allows for more appropriate responses by selecting the optimal startup method based on the user's past behavior history.

[0082] The activation unit can estimate the user's emotions and adjust the activation method based on the estimated emotions. For example, the activation unit can estimate the user's emotions using an emotion recognition algorithm. The activation unit can also set sensor types and emotion criteria and adjust the activation method based on the user's emotions. This allows for more appropriate activation by adjusting the activation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The activation unit can adjust the activation range based on the magnitude and affected area of ​​the earthquake. For example, the activation unit monitors seismometer data in real time to obtain the earthquake's magnitude and affected area. The activation unit can also set the activation range based on the earthquake's magnitude and affected area. This allows for the provision of necessary support by adjusting the activation range based on the earthquake's magnitude and affected area.

[0084] The startup unit can select the optimal startup method at startup, taking into account the battery level of the user's device. For example, the startup unit can retrieve and refer to the battery level of the user's device from a database. Alternatively, the startup unit can select the optimal startup method based on the battery level of the user's device. This allows for minimizing battery consumption by selecting the optimal startup method based on the battery level of the user's device.

[0085] The recognition unit can estimate the user's emotions and adjust the accuracy of recognition based on the estimated emotions. For example, the recognition unit estimates the user's emotions using an emotion recognition algorithm. The recognition unit can also set sensor types and emotion criteria and adjust the accuracy of recognition based on the user's emotions. This allows for more accurate recognition by adjusting the accuracy of recognition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The recognition unit can improve recognition accuracy by analyzing the tone and speed of the user's voice during recognition. For example, the recognition unit uses speech analysis technology to analyze the tone and speed of the user's voice. Furthermore, the recognition unit can also improve recognition accuracy based on the tone and speed of the user's voice using a speech recognition algorithm. This allows for more accurate recognition by analyzing the tone and speed of the user's voice to improve recognition accuracy.

[0087] The recognition unit can select the optimal recognition method by referring to the user's behavior patterns during recognition. For example, the recognition unit analyzes the user's behavior patterns using a behavior detection algorithm. Furthermore, the recognition unit can select the optimal recognition method based on the user's behavior patterns. This allows for a more appropriate response by selecting the optimal recognition method based on the user's behavior patterns.

[0088] The recognition unit can estimate the user's emotions and adjust its recognition method based on the estimated emotions. For example, the recognition unit can estimate the user's emotions using an emotion recognition algorithm. The recognition unit can also set sensor types and emotion criteria and adjust its recognition method based on the user's emotions. This allows for more appropriate recognition by adjusting the recognition method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The recognition unit can improve recognition accuracy by taking into account the ambient sounds surrounding the user during recognition. For example, the recognition unit can remove ambient noise using ambient sound filtering technology. Furthermore, the recognition unit can also improve recognition accuracy based on the ambient sounds surrounding the user using a speech recognition algorithm. This allows for more accurate recognition by improving recognition accuracy while considering the ambient sounds surrounding the user.

[0090] The recognition unit can improve the accuracy of recognition by referring to the sensor information of the user's device during recognition. For example, the recognition unit can accurately recognize the user's movements by referring to the device's accelerometer sensor information. The recognition unit can also determine the user's current location by referring to the device's location information. By improving the accuracy of recognition by referring to the sensor information of the user's device, more accurate recognition becomes possible.

[0091] The information provider can estimate the user's emotions and adjust the content of the information provided based on the estimated emotions. For example, the information provider can estimate the user's emotions using an emotion recognition algorithm. Furthermore, the information provider can set sensor types and emotion criteria and adjust the content of the information provided based on the user's emotions. This allows for the provision of more appropriate information by adjusting the content of the information provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The service provider can provide optimal instructions by referring to the user's past first aid history at the time of service provision. For example, the service provider can retrieve and refer to the user's past first aid history from a database. The service provider can also provide optimal instructions based on the user's past first aid history. This allows for more appropriate first aid by providing optimal instructions by referring to the user's past first aid history.

[0093] The service provider can customize the information provided at the time of delivery, taking into account the user's current health condition. For example, the service provider can retrieve and refer to the user's current health condition from a database. Furthermore, the service provider can customize the information provided based on the user's current health condition. This allows for more appropriate first aid by customizing the information provided based on the user's current health condition.

[0094] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, the service provider can estimate the user's emotions using an emotion recognition algorithm. Furthermore, the service provider can set sensor types and emotion criteria to determine the priority of the information to be provided based on the user's emotions. This allows for the provision of more appropriate information by prioritizing the information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The service provider can select the optimal display method at the time of delivery, taking into account the screen size of the user's device. For example, the service provider can retrieve and refer to the screen size of the user's device from a database. Furthermore, the service provider can select the optimal display method based on the screen size of the user's device. This allows for the provision of more easily viewable information by selecting the optimal display method considering the screen size of the user's device.

[0096] The service provider can make the information provided multilingual by referring to the user's language settings at the time of delivery. For example, the service provider can retrieve and refer to the user's device language settings from a database. Furthermore, the service provider can make the information provided multilingual based on the user's language settings. This allows for the provision of more appropriate information by making the information provided multilingual by referring to the user's language settings.

[0097] The collaboration unit can estimate the user's emotions and adjust the collaboration method based on the estimated user emotions. For example, the collaboration unit estimates the user's emotions using an emotion recognition algorithm. Furthermore, the collaboration unit can set sensor types and emotion criteria and adjust the collaboration method based on the user's emotions. This allows for more appropriate collaboration by adjusting the collaboration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The collaboration unit can select the optimal collaboration method by referring to local disaster information during collaboration. For example, the collaboration unit can obtain and refer to local disaster information from a database. Furthermore, the collaboration unit can select the optimal collaboration method based on local disaster information. This allows for more appropriate collaboration by selecting the optimal collaboration method based on local disaster information.

[0099] The integration unit can improve the accuracy of integration by referring to the community's past integration history during integration. For example, the integration unit retrieves and refers to the community's past integration history from a database. Furthermore, the integration unit can improve the accuracy of integration based on the community's past integration history. This allows for more appropriate integration by improving the accuracy of integration by referring to the community's past integration history.

[0100] The collaboration unit can estimate the user's emotions and determine the priority of collaborations based on the estimated user emotions. For example, the collaboration unit estimates the user's emotions using an emotion recognition algorithm. Furthermore, the collaboration unit can set sensor types and emotion criteria and determine the priority of collaborations based on the user's emotions. This allows for more appropriate collaborations by determining the priority of collaborations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The integration unit can select the optimal integration method when integrating, taking into account the local communication infrastructure situation. For example, the integration unit can retrieve and refer to the local communication infrastructure situation from a database. Furthermore, the integration unit can select the optimal integration method based on the local communication infrastructure situation. This allows for more appropriate integration by selecting the optimal integration method while considering the local communication infrastructure situation.

[0102] The integration unit can improve the accuracy of integration by referring to the attribute information of community members during the integration process. For example, the integration unit retrieves and refers to the attribute information of community members from a database. Furthermore, the integration unit can improve the accuracy of integration based on the attribute information of community members. By improving the accuracy of integration by referring to the attribute information of community members, more appropriate integration becomes possible.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The activation unit can acquire the user's location information when an earthquake occurs and provide information about the nearest evacuation shelter. For example, it can acquire GPS data when an earthquake occurs to determine the user's current location. The activation unit can also refer to a database of evacuation shelters and notify the user of the location of the nearest shelter. This allows the user to quickly head to an evacuation shelter and ensure their safety.

[0105] The recognition unit can analyze the tone and speed of the user's voice and estimate the user's stress level. For example, it can use voice analysis technology to analyze the tone and speed of the user's voice and evaluate the stress level. Furthermore, the recognition unit can provide appropriate psychological support according to the stress level. This can reduce the user's stress and provide a sense of security.

[0106] The service provider can monitor the user's health status and contact medical institutions as needed. For example, it can measure the user's heart rate and body temperature with sensors and notify medical institutions if an abnormality is detected. The service provider can also provide instructions for first aid tailored to the user's health condition. This protects the user's health and enables a rapid medical response.

[0107] The liaison unit can collect and provide local volunteer information to users during disasters. For example, it can refer to a database of local volunteer organizations to obtain information on volunteers who are active during a disaster. The liaison unit can also provide information on the nearest volunteers based on the user's location. This allows users to receive assistance quickly.

[0108] The information provider can estimate the user's emotions and adjust the tone of the information provided based on those emotions. For example, it can use an emotion recognition algorithm to estimate the user's emotions and change the tone of the information accordingly. The information provider can also provide audio guides designed to help the user relax. This enables information delivery that is sensitive to the user's emotions.

[0109] The startup unit can consider the user's device's battery level during a disaster and start in an optimal power-saving mode. For example, if the battery level is low, it will only activate the minimum necessary functions. The startup unit can also automatically configure settings to minimize battery consumption. This allows for extended use and ensures communication during disasters.

[0110] The recognition unit can estimate the user's emotions and adjust the accuracy of recognition based on the estimated emotions. For example, it can use an emotion recognition algorithm to estimate the user's emotions and improve the accuracy of recognition according to those emotions. The recognition unit can also provide feedback that corresponds to the user's emotions. This enables recognition that takes the user's emotions into consideration.

[0111] The service provider can refer to a user's past movement history during a disaster to suggest the optimal evacuation route. For example, it can retrieve the user's past movement history from a database and calculate the optimal evacuation route. Furthermore, the service provider can update evacuation route information in real time and notify the user. This allows users to evacuate quickly and safely.

[0112] The collaboration unit can estimate the user's emotions during a disaster and adjust the collaboration method based on the estimated emotions. For example, it can use an emotion recognition algorithm to estimate the user's emotions and change the means of collaboration according to those emotions. The collaboration unit can also provide support that is tailored to the user's emotions. This enables collaboration that takes the user's emotions into consideration.

[0113] The service provider can monitor the communication status of users' devices during a disaster and select the optimal communication method. For example, if the communication status is unstable, it will use Wi-Fi ad-hoc communication. The service provider can also adjust the method of sending and receiving information according to the communication status. This ensures stable communication even during a disaster, enabling rapid information sharing.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The activation unit automatically activates when an earthquake of magnitude 7 or higher occurs. The activation unit monitors seismometer data in real time and automatically activates when an earthquake of magnitude 7 or higher is detected. The activation unit can also be configured with the type of sensor and the activation trigger. Step 2: The recognition unit recognizes the user's voice and actions after being activated by the activation unit. The recognition unit can recognize the user's voice using speech recognition technology and recognize the user's actions using an action detection algorithm. Step 3: The service provider provides psychological support and first aid instructions in real time based on the information recognized by the recognition unit. The service provider can provide psychological support using counseling and relaxation techniques, and can also provide first aid procedures and instructions in various formats. Step 4: The Collaboration Department promotes mutual assistance by collaborating with local communities based on the information provided by the Provision Department. The Collaboration Department can set the means of collaboration and the methods of information sharing.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the activation unit, recognition unit, provision unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the activation unit detects earthquakes of magnitude 7 or higher using the sensors and seismometer data of the smart device 14 and is automatically activated by the control unit 46A of the smart device 14. The recognition unit recognizes the user's voice and actions using the microphone 38B and camera 42 of the smart device 14 and executes speech recognition technology and action detection algorithms using the control unit 46A. The provision unit provides psychological support and first aid instructions in real time using the display 40A and speaker 40B of the smart device 14. The collaboration unit collaborates with local communities via the communication I / F 44 of the smart device 14 to promote mutual assistance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the activation unit, recognition unit, provision unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the activation unit detects earthquakes of magnitude 7 or higher using the sensors and seismometer data of the smart glasses 214 and is automatically activated by the control unit 46A of the smart glasses 214. The recognition unit recognizes the user's voice and actions using the microphone 238 and camera 42 of the smart glasses 214 and executes speech recognition technology and action detection algorithms using the control unit 46A. The provision unit provides psychological support and first aid instructions in real time using the speaker 240 of the smart glasses 214. The collaboration unit collaborates with local communities via the communication I / F 44 of the smart glasses 214 to promote mutual assistance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the activation unit, recognition unit, provision unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the activation unit detects earthquakes of magnitude 7 or higher using the sensors and seismometer data of the headset terminal 314 and is automatically activated by the control unit 46A of the headset terminal 314. The recognition unit recognizes the user's voice and actions using the microphone 238 and camera 42 of the headset terminal 314 and executes speech recognition technology and action detection algorithms using the control unit 46A. The provision unit provides psychological support and first aid instructions in real time using the speaker 240 of the headset terminal 314. The collaboration unit collaborates with local communities via the communication I / F 44 of the headset terminal 314 to promote mutual assistance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0160] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the activation unit, recognition unit, provision unit, and cooperation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the activation unit detects earthquakes of magnitude 7 or higher using the robot 414's sensors and seismometer data, and is automatically activated by the robot 414's control unit 46A. The recognition unit recognizes the user's voice and movements using the robot 414's microphone 238 and camera 42, and the control unit 46A executes speech recognition technology and motion detection algorithms. The provision unit provides psychological support and first aid instructions in real time using the robot 414's speaker 240. The cooperation unit cooperates with local communities via the robot 414's communication I / F 44 to promote mutual assistance. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0169] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0171] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0172] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0173] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0177] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0178] 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.

[0179] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0180] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0181] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0182] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0184] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0185] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0186] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0187] (Note 1) A startup unit that automatically activates when an earthquake of magnitude 7 or higher occurs, After being activated by the aforementioned startup unit, the recognition unit recognizes the user's voice and actions, A provisioning unit provides psychological support and first aid instructions in real time based on the information recognized by the recognition unit. The system includes a cooperation unit that promotes mutual assistance by working with local communities and regions based on the information provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned startup unit is It will automatically activate when an earthquake of magnitude 7 or higher occurs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recognition unit, Recognizing user voice and actions The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Providing real-time psychological support and first aid instructions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, Promote mutual support by collaborating with local communities and regions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Even if a user doesn't have a smartphone, they can still send a voice request for rescue. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Conversation is possible even offline. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, In emergencies, users can send and receive information about each other's safety. If communication is possible, the information is synchronized via the cloud; if communication is not possible, the safety information is synchronized between devices using Wi-Fi ad-hoc communication. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned startup unit is It estimates the user's emotions and adjusts the launch timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned startup unit is Prioritize activation based on the time and location of the earthquake. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned startup unit is Upon startup, the system selects the optimal startup method by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned startup unit is It estimates the user's emotions and adjusts the launch method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned startup unit is Adjust the activation range based on the magnitude and extent of the earthquake's impact. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned startup unit is During startup, the system selects the optimal startup method, taking into account the user's device's battery level. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recognition unit, It estimates the user's emotions and adjusts the accuracy of recognition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recognition unit, During recognition, the system analyzes the user's voice tone and speed to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recognition unit, During recognition, the system selects the optimal recognition method by referring to the user's behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recognition unit, It estimates the user's emotions and adjusts the recognition method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recognition unit, During recognition, the system takes into account ambient noise around the user to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recognition unit, During recognition, the system references the sensor information of the user's device to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the content of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the system will refer to the user's past first aid history to provide the most appropriate instructions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the information provided will be customized to take into account the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the screen size of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, refer to the user's language settings to make the information provided available in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, When coordinating, the most suitable method of cooperation is selected by referring to local disaster information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, When integrating, we improve the accuracy of the integration by referring to the community's past integration history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, When integrating, the optimal integration method will be selected considering the local communication infrastructure situation. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, When integrating, we improve the accuracy of the integration by referencing the attribute information of community members. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A startup unit that automatically activates when an earthquake of magnitude 7 or higher occurs, After being activated by the aforementioned startup unit, the recognition unit recognizes the user's voice and actions, A provisioning unit provides psychological support and first aid instructions in real time based on the information recognized by the recognition unit. The system includes a cooperation unit that promotes mutual assistance by working with local communities and regions based on the information provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned supply unit is, Providing real-time psychological support and first aid instructions. The system according to feature 1.

3. The aforementioned linkage unit is, Promote mutual support by collaborating with local communities and regions. The system according to feature 1.

4. The aforementioned supply unit is, Even if a user doesn't have a smartphone, they can still send a voice request for rescue. The system according to feature 1.

5. The aforementioned supply unit is, Conversation is possible even offline. The system according to feature 1.

6. The aforementioned supply unit is, In emergencies, users can send and receive information about each other's safety. If communication is possible, the information is synchronized via the cloud; if communication is not possible, the safety information is synchronized between devices using Wi-Fi ad-hoc communication. The system according to feature 1.

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