System

Abstract

An object of a system according to an embodiment is to take quick and appropriate measures when an accident or a sudden illness occurs.SOLUTION: A system according to an embodiment includes an accident detection unit, an emergency response instruction unit, and an ambulance dispatch unit. The accident detection unit detects the occurrence of an accident or a sudden illness. The emergency response instruction unit instructs an appropriate response to the accident or the sudden illness detected by the accident detection unit. The ambulance dispatcher dispatches an ambulance for the accident or the sudden illness detected by the accident detector.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to respond quickly and appropriately in the event of an accident or sudden illness.

[0005] The system according to the embodiment aims to take prompt and appropriate action in the event of an accident or sudden illness. [Means for solving the problem]

[0006] The system according to the embodiment includes an accident detection unit, an emergency response instruction unit, and an ambulance dispatch unit. The accident detection unit detects the occurrence of an accident or sudden illness. The emergency response instruction unit instructs an appropriate response to the accident or sudden illness detected by the accident detection unit. The ambulance dispatch unit dispatches an ambulance in response to the accident or sudden illness detected by the accident detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can take prompt and appropriate action in the event of an accident or sudden illness. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The emergency response support system according to an embodiment of the present invention detects the occurrence of an accident or sudden illness, and the generation AI instructs appropriate responses and dispatches an ambulance. As a result, the emergency response support system can respond quickly and appropriately when an accident or sudden illness occurs, thereby improving the survival rate of victims.

[0029] The emergency response support system according to the embodiment includes an accident detection unit, an emergency response instruction unit, and an ambulance dispatch unit. The accident detection unit detects the occurrence of an accident or sudden illness. For example, it uses a camera or sensors to detect abnormal movements such as falls, traffic accidents, or collapses due to sudden illness. The accident detection unit can also analyze audio data to detect abnormal sounds such as screams or collisions. For example, it can use voice recognition technology to detect changes in specific frequencies or volume and identify abnormalities. Furthermore, the accident detection unit can integrate data from multiple sensors to detect abnormal movements or environmental changes with high accuracy. For example, it can combine and analyze data from an acceleration sensor and a temperature sensor. The emergency response instruction unit instructs appropriate responses to accidents or sudden illnesses detected by the accident detection unit. For example, it can provide instructions on cardiopulmonary resuscitation (CPR) procedures via voice or on a screen, or guide the victim on how to use an AED (Automated External Defibrillator). The emergency response instruction unit can also refer to the victim's past medical data to instruct individually optimized emergency responses. For example, it can take into account allergy information and pre-existing conditions. Furthermore, the emergency response instruction unit can analyze the surrounding situation in real time and indicate the optimal evacuation route or safe location. For example, based on data from cameras and sensors, it can suggest a route that avoids obstacles and dangerous areas. The ambulance dispatch unit dispatches an ambulance in response to an accident or sudden illness detected by the accident detection unit. For example, the generation AI automatically makes an emergency call and prompts the arrival of an ambulance. The ambulance dispatch unit can also analyze traffic conditions in real time and dispatch an ambulance along the shortest route. For example, it can calculate the optimal route based on information about traffic congestion and road construction. Furthermore, the ambulance dispatch unit can identify the victim's location information with high accuracy and optimize the ambulance's arrival time. For example, it can analyze GPS data and smartphone location information. As a result, the ambulance activity support system according to the embodiment can respond quickly and appropriately in the event of an accident or sudden illness, thereby improving the victim's survival rate. For example, by instructing cardiopulmonary resuscitation, appropriate first aid can be provided before the ambulance arrives, thereby increasing the victim's survival rate. Furthermore, providing information to emergency teams enables them to respond quickly and accurately after their arrival.

[0030] The accident detection unit analyzes surrounding audio data and can identify the occurrence of an accident or sudden illness by detecting at least one abnormal sound, such as a shout or a collision sound. For example, the accident detection unit uses a generative AI to analyze surrounding audio data in real time and detect abnormal sounds, such as a shout or a collision sound. For example, it uses voice recognition technology to detect changes in specific frequencies or volume and identify the abnormality. This allows for the rapid identification of accidents or sudden illnesses by analyzing audio data and detecting abnormal sounds.

[0031] The incident detection unit integrates data from multiple sensors and can detect at least one of abnormal behavior or environmental changes with high accuracy. For example, the incident detection unit uses a generative AI to integrate data from multiple sensors in real time and detect abnormal behavior or environmental changes. For example, it combines and analyzes data from acceleration sensors and temperature sensors. By integrating data from multiple sensors, abnormal behavior and environmental changes can be detected with high accuracy.

[0032] The emergency response instruction unit can instruct an individually optimized emergency response based on the victim's past medical data. For example, the generation AI references the victim's past medical data and instructs an individually optimized emergency response. For example, it takes into account allergy information and medical history. In this way, by referencing the victim's past medical data, it is possible to instruct an individually optimized emergency response.

[0033] The emergency response instruction unit can analyze the surrounding situation in real time and indicate the optimal evacuation route or safe location. For example, the emergency response instruction unit uses a generation AI to analyze the surrounding situation in real time and indicate the optimal evacuation route. For example, it can present a route that avoids obstacles and dangerous areas based on data from cameras and sensors. This allows the optimal evacuation route and safe location to be indicated by analyzing the surrounding situation in real time.

[0034] The ambulance dispatch unit can analyze traffic conditions in real time and dispatch an ambulance along the optimal route. For example, the generation AI in the ambulance dispatch unit analyzes traffic conditions in real time and dispatches an ambulance along the shortest route. For example, it calculates the optimal route based on information on traffic congestion and road construction. This allows for the dispatch of an ambulance along the shortest route by analyzing traffic conditions in real time.

[0035] The ambulance dispatch unit can identify the victim's location information with high accuracy and optimize the ambulance's arrival time. For example, the ambulance dispatch unit uses generative AI to identify the victim's location information with high accuracy and optimize the ambulance's arrival time. For example, it analyzes GPS data and smartphone location information. This allows the victim's location information to be identified with high accuracy, thereby optimizing the ambulance's arrival time.

[0036] The ambulance dispatching department can use a drone to deliver a first aid kit to the scene before the ambulance arrives. For example, the generative AI controls the drone to deliver a first aid kit to the scene before the ambulance arrives. For example, the location of the victim is identified using a GPS installed in the drone. This allows the drone to deliver a first aid kit to the scene before the ambulance arrives.

[0037] The ambulance dispatch unit can work in conjunction with smart city infrastructure to automatically ensure priority passage for emergency vehicles. For example, the generative AI can work in conjunction with smart city infrastructure to automatically ensure priority passage for emergency vehicles. For example, it can control traffic signals to give priority to emergency vehicles. In this way, by working in conjunction with smart city infrastructure, priority passage for emergency vehicles can be automatically ensured.

[0038] The accident detection unit controls the drone and monitors a wide area, enabling it to quickly detect the occurrence of accidents or sudden illnesses. For example, the generation AI controls the drone and monitors a wide area. For example, cameras and sensors mounted on the drone are used to detect abnormal movements and environmental changes. This allows the drone to be controlled to monitor a wide area, enabling it to quickly detect the occurrence of accidents or sudden illnesses.

[0039] The incident detection unit can collect data from smartphones or wearable devices and monitor the health condition of individuals in real time. For example, the generative AI collects data from smartphones or wearable devices in real time and monitors the health condition of individuals. For example, it analyzes heart rate and step count data to detect abnormalities. This makes it possible to monitor the health condition of individuals in real time by collecting data from smartphones and wearable devices.

[0040] The emergency response instruction unit supports multiple languages ​​and can instruct appropriate emergency responses even for people who speak different languages. For example, the generation AI supports multiple languages ​​and can instruct emergency responses even for people who speak different languages. For example, instructions can be provided in multiple languages ​​such as English, Spanish, and Chinese. This multilingual support makes it possible to instruct appropriate emergency responses even for people who speak different languages.

[0041] The emergency response instruction unit can visually show emergency response procedures using AR technology. For example, the generation AI in the emergency response instruction unit visually shows emergency response procedures using AR technology. For example, cardiopulmonary resuscitation procedures are visually displayed through a smartphone or AR glasses. In this way, emergency response procedures can be visually shown using AR technology.

[0042] The victim's condition monitoring unit analyzes the victim's vital signs in real time and can immediately instruct a response if an abnormality occurs. For example, the generation AI analyzes the victim's vital signs in real time and can immediately instruct a response if an abnormality occurs. For example, it detects sudden changes in heart rate or respiratory rate. This allows the victim's vital signs to be analyzed in real time, making it possible to immediately instruct a response if an abnormality occurs.

[0043] The victim's condition monitoring unit can apply an individually optimized monitoring protocol based on the victim's past health data. For example, the generating AI references the victim's past health data and applies an individually optimized monitoring protocol. For example, monitoring is performed taking into account allergy information and medical history. This makes it possible to apply an individually optimized monitoring protocol by referencing the victim's past health data.

[0044] The victim's condition monitoring unit can remotely monitor the victim's vital signs using a wearable device. For example, the generating AI can remotely monitor the victim's vital signs using a wearable device. For example, a smartwatch or heart rate monitor can be used to monitor the heart rate and respiratory rate in real time. This makes it possible to remotely monitor the victim's vital signs using the wearable device.

[0045] The victim's condition monitoring unit visualizes the victim's condition in a 3D model, allowing the rescuer to intuitively understand it. For example, the generative AI visualizes the victim's condition in a 3D model, allowing the rescuer to intuitively understand it. For example, the victim's vital signs and health condition are displayed in a 3D model. This allows the rescuer to intuitively understand the victim's condition by visualizing it in a 3D model.

[0046] The information provision unit for emergency responders can update the victim's condition in real time and provide the latest information to emergency responders. For example, the generation AI can update the victim's condition in real time and provide the latest information to emergency responders. For example, it can notify changes in heart rate and respiratory rate in real time. This allows the latest information to be provided to emergency responders by updating the victim's condition in real time.

[0047] The unit for providing information to emergency responders can provide the necessary information to emergency responders based on the victim's past medical data. For example, the generation AI references the victim's past medical data and provides the necessary information to emergency responders. For example, it provides information that takes into account allergy information and medical history. In this way, by referencing the victim's past medical data, the necessary information can be provided to emergency responders.

[0048] The unit for providing information to the ambulance team can visually show the victim's condition to the ambulance team using AR technology. For example, the generation AI can visually show the victim's condition to the ambulance team using AR technology. For example, the victim's vital signs and health condition can be visually displayed through a smartphone or AR glasses. In this way, the use of AR technology makes it possible to visually show the victim's condition to the ambulance team.

[0049] The information provision unit for emergency responders supports multiple languages ​​and can provide appropriate information to emergency responders who speak different languages. For example, the generation AI supports multiple languages ​​and provides information to emergency responders who speak different languages. For example, it provides information in multiple languages ​​such as English, Spanish, and Chinese. This multilingual support makes it possible to provide appropriate information to emergency responders who speak different languages.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The emergency response support system can also be equipped with a visualization section that visualizes the victim's condition in 3D, allowing rescuers to intuitively understand it. For example, the victim's vital signs and health status can be displayed in 3D, allowing rescuers to respond quickly and accurately. This allows rescuers to visually understand the victim's condition, allowing them to make quick and accurate decisions.

[0052] The emergency response support system can also be equipped with a victim condition monitoring unit that monitors the victim's condition in real time and immediately issues instructions if an abnormality occurs. For example, it can detect sudden changes in heart rate or respiratory rate and issue instructions for appropriate responses. This allows the system to constantly monitor the victim's condition and respond quickly if an abnormality occurs.

[0053] The emergency response support system can also be equipped with a drone control unit that uses drones to deliver first aid kits to the scene before an ambulance arrives. For example, the drone's onboard GPS can be used to identify the victim's location and quickly deliver the first aid kit. This allows first aid to be administered before the ambulance arrives, improving the victim's chances of survival.

[0054] The emergency response support system can also be equipped with a traffic control unit that works in conjunction with smart city infrastructure to automatically ensure priority passage for emergency vehicles, for example by controlling traffic signals to give priority to emergency vehicles. This allows emergency vehicles to arrive at the scene more quickly, improving the chances of saving the lives of victims.

[0055] The emergency response support system can also be equipped with a multilingual support unit that provides instructions in multiple languages, such as English, Spanish, and Chinese, to provide appropriate emergency response instructions to people who speak different languages. This allows for quick and appropriate emergency response to be provided to people who speak different languages.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The accident detection unit detects the occurrence of accidents or sudden illnesses. For example, cameras and sensors are used to detect abnormal movements such as falls, traffic accidents, or collapses due to sudden illness. Audio data can also be analyzed to detect abnormal sounds such as screams or collisions. Furthermore, data from multiple sensors is integrated to detect abnormal movements and environmental changes with high accuracy. Step 2: The emergency response instruction unit issues instructions for appropriate responses to accidents or sudden illnesses detected by the accident detection unit. For example, it provides instructions on cardiopulmonary resuscitation (CPR) procedures via voice or screen, or provides instructions on how to use an AED (Automated External Defibrillator). It can also refer to the victim's past medical data to provide individually optimized emergency responses. It can also analyze the surrounding situation in real time and provide instructions on the best evacuation route or safe location. Step 3: The ambulance dispatch unit dispatches an ambulance in response to an accident or sudden illness detected by the accident detection unit. For example, the generation AI automatically makes an emergency call and prompts the arrival of an ambulance. It can also analyze traffic conditions in real time and dispatch an ambulance via the shortest route. It can also pinpoint the victim's location with high accuracy to optimize the ambulance's arrival time.

[0058] (Example 2) The emergency response support system according to an embodiment of the present invention detects the occurrence of an accident or sudden illness, and the generation AI instructs appropriate responses and dispatches an ambulance. As a result, the emergency response support system can respond quickly and appropriately when an accident or sudden illness occurs, thereby improving the survival rate of victims.

[0059] The emergency response support system according to the embodiment includes an accident detection unit, an emergency response instruction unit, and an ambulance dispatch unit. The accident detection unit detects the occurrence of an accident or sudden illness. For example, it uses a camera or sensors to detect abnormal movements such as falls, traffic accidents, or collapses due to sudden illness. The accident detection unit can also analyze audio data to detect abnormal sounds such as screams or collisions. For example, it can use voice recognition technology to detect changes in specific frequencies or volume and identify abnormalities. Furthermore, the accident detection unit can integrate data from multiple sensors to detect abnormal movements or environmental changes with high accuracy. For example, it can combine and analyze data from an acceleration sensor and a temperature sensor. The emergency response instruction unit instructs appropriate responses to accidents or sudden illnesses detected by the accident detection unit. For example, it can provide instructions on cardiopulmonary resuscitation (CPR) procedures via voice or on a screen, or guide the victim on how to use an AED (Automated External Defibrillator). The emergency response instruction unit can also refer to the victim's past medical data to instruct individually optimized emergency responses. For example, it can take into account allergy information and pre-existing conditions. Furthermore, the emergency response instruction unit can analyze the surrounding situation in real time and indicate the optimal evacuation route or safe location. For example, based on data from cameras and sensors, it can suggest a route that avoids obstacles and dangerous areas. The ambulance dispatch unit dispatches an ambulance in response to an accident or sudden illness detected by the accident detection unit. For example, the generation AI automatically makes an emergency call and prompts the arrival of an ambulance. The ambulance dispatch unit can also analyze traffic conditions in real time and dispatch an ambulance along the shortest route. For example, it can calculate the optimal route based on information about traffic congestion and road construction. Furthermore, the ambulance dispatch unit can identify the victim's location information with high accuracy and optimize the ambulance's arrival time. For example, it can analyze GPS data and smartphone location information. As a result, the ambulance activity support system according to the embodiment can respond quickly and appropriately in the event of an accident or sudden illness, thereby improving the victim's survival rate. For example, by instructing cardiopulmonary resuscitation, appropriate first aid can be provided before the ambulance arrives, thereby increasing the victim's survival rate. Furthermore, providing information to emergency teams enables them to respond quickly and accurately after their arrival.

[0060] The accident detection unit analyzes surrounding audio data and can identify the occurrence of an accident or sudden illness by detecting at least one abnormal sound, such as a shout or a collision sound. For example, the accident detection unit uses a generative AI to analyze surrounding audio data in real time and detect abnormal sounds, such as a shout or a collision sound. For example, it uses voice recognition technology to detect changes in specific frequencies or volume and identify the abnormality. This allows for the rapid identification of accidents or sudden illnesses by analyzing audio data and detecting abnormal sounds.

[0061] The incident detection unit integrates data from multiple sensors and can detect at least one of abnormal behavior or environmental changes with high accuracy. For example, the incident detection unit uses a generative AI to integrate data from multiple sensors in real time and detect abnormal behavior or environmental changes. For example, it combines and analyzes data from acceleration sensors and temperature sensors. By integrating data from multiple sensors, abnormal behavior and environmental changes can be detected with high accuracy.

[0062] The incident detection unit can use the emotion estimation function to analyze changes in the emotions of people in the vicinity and estimate the occurrence of an emergency. For example, the incident detection unit uses a generative AI to analyze changes in the emotions of people in the vicinity in real time and estimate the occurrence of an emergency. For example, it analyzes facial expressions and voice tones to detect changes in emotions. As a result, by using the emotion estimation function, it is possible to analyze changes in the emotions of people in the vicinity and estimate the occurrence of an emergency.

[0063] The emergency response instruction unit can instruct an individually optimized emergency response based on the victim's past medical data. For example, the generation AI references the victim's past medical data and instructs an individually optimized emergency response. For example, it takes into account allergy information and medical history. In this way, by referencing the victim's past medical data, it is possible to instruct an individually optimized emergency response.

[0064] The emergency response instruction unit can analyze the surrounding situation in real time and indicate the optimal evacuation route or safe location. For example, the emergency response instruction unit uses a generation AI to analyze the surrounding situation in real time and indicate the optimal evacuation route. For example, it can present a route that avoids obstacles and dangerous areas based on data from cameras and sensors. This allows the optimal evacuation route and safe location to be indicated by analyzing the surrounding situation in real time.

[0065] The emergency response instruction unit can use the emotion estimation function to analyze the rescuer's stress level and provide encouragement or instructions at an appropriate time. For example, the generation AI in the emergency response instruction unit analyzes the rescuer's emotional state in real time and evaluates the stress level. For example, it analyzes facial expressions and voice tone to detect signs of stress. This allows the emotion estimation function to analyze the rescuer's stress level and provide encouragement or instructions at an appropriate time.

[0066] The ambulance dispatch unit can analyze traffic conditions in real time and dispatch an ambulance along the optimal route. For example, the generation AI in the ambulance dispatch unit analyzes traffic conditions in real time and dispatches an ambulance along the shortest route. For example, it calculates the optimal route based on information on traffic congestion and road construction. This allows for the dispatch of an ambulance along the shortest route by analyzing traffic conditions in real time.

[0067] The ambulance dispatch unit can identify the victim's location information with high accuracy and optimize the ambulance's arrival time. For example, the ambulance dispatch unit uses generative AI to identify the victim's location information with high accuracy and optimize the ambulance's arrival time. For example, it analyzes GPS data and smartphone location information. This allows the victim's location information to be identified with high accuracy, thereby optimizing the ambulance's arrival time.

[0068] The ambulance dispatch unit can use the emotion estimation function to analyze the caller's state of tension and provide guidance to encourage them to make a calm call. For example, the ambulance dispatch unit uses a generative AI to analyze the caller's emotional state in real time and evaluate the state of tension. For example, it analyzes facial expressions and tone of voice to detect signs of tension. This allows the emotion estimation function to analyze the caller's state of tension and provide guidance to encourage them to make a calm call.

[0069] The ambulance dispatching department can use a drone to deliver a first aid kit to the scene before the ambulance arrives. For example, the generative AI controls the drone to deliver a first aid kit to the scene before the ambulance arrives. For example, the location of the victim is identified using a GPS installed in the drone. This allows the drone to deliver a first aid kit to the scene before the ambulance arrives.

[0070] The ambulance dispatch unit can work in conjunction with smart city infrastructure to automatically ensure priority passage for emergency vehicles. For example, the generative AI can work in conjunction with smart city infrastructure to automatically ensure priority passage for emergency vehicles. For example, it can control traffic signals to give priority to emergency vehicles. In this way, by working in conjunction with smart city infrastructure, priority passage for emergency vehicles can be automatically ensured.

[0071] The ambulance dispatch unit can use the emotion estimation function to analyze the emotional state of the caller and provide a reporting procedure that gives a sense of security. For example, the ambulance dispatch unit uses a generation AI to analyze the emotional state of the caller in real time and provide a reporting procedure that gives a sense of security. For example, facial expressions and tone of voice can be analyzed to promote a sense of security. In this way, by using the emotion estimation function, the emotional state of the caller can be analyzed and a reporting procedure that gives a sense of security can be provided.

[0072] The accident detection unit controls the drone and monitors a wide area, enabling it to quickly detect the occurrence of accidents or sudden illnesses. For example, the generation AI controls the drone and monitors a wide area. For example, cameras and sensors mounted on the drone are used to detect abnormal movements and environmental changes. This allows the drone to be controlled to monitor a wide area, enabling it to quickly detect the occurrence of accidents or sudden illnesses.

[0073] The incident detection unit can collect data from smartphones or wearable devices and monitor the health condition of individuals in real time. For example, the generative AI collects data from smartphones or wearable devices in real time and monitors the health condition of individuals. For example, it analyzes heart rate and step count data to detect abnormalities. This makes it possible to monitor the health condition of individuals in real time by collecting data from smartphones and wearable devices.

[0074] The incident detection unit uses the emotion estimation function to monitor people's emotions in public places and detect abnormal emotional changes to identify emergency situations. For example, the incident detection unit uses a generative AI to monitor people's emotions in public places in real time and detect abnormal emotional changes. For example, it uses a camera or microphone to analyze facial expressions and voice tone. This allows the emotion estimation function to monitor people's emotions in public places and detect abnormal emotional changes to identify emergency situations.

[0075] The emergency response instruction unit supports multiple languages ​​and can instruct appropriate emergency responses even for people who speak different languages. For example, the generation AI supports multiple languages ​​and can instruct emergency responses even for people who speak different languages. For example, instructions can be provided in multiple languages ​​such as English, Spanish, and Chinese. This multilingual support makes it possible to instruct appropriate emergency responses even for people who speak different languages.

[0076] The emergency response instruction unit can visually show emergency response procedures using AR technology. For example, the generation AI in the emergency response instruction unit visually shows emergency response procedures using AR technology. For example, cardiopulmonary resuscitation procedures are visually displayed through a smartphone or AR glasses. In this way, emergency response procedures can be visually shown using AR technology.

[0077] The emergency response instruction unit can use the emotion estimation function to analyze the emotional state of the rescuer and provide instructions to elicit positive emotions. For example, the emergency response instruction unit uses a generation AI to analyze the emotional state of the rescuer in real time and provide instructions to elicit positive emotions. For example, the generation AI analyzes facial expressions and tone of voice to encourage positive emotions. In this way, the emotion estimation function can be used to analyze the emotional state of the rescuer and provide instructions to elicit positive emotions.

[0078] The victim's condition monitoring unit analyzes the victim's vital signs in real time and can immediately instruct a response if an abnormality occurs. For example, the generation AI analyzes the victim's vital signs in real time and can immediately instruct a response if an abnormality occurs. For example, it detects sudden changes in heart rate or respiratory rate. This allows the victim's vital signs to be analyzed in real time, making it possible to immediately instruct a response if an abnormality occurs.

[0079] The victim's condition monitoring unit can apply an individually optimized monitoring protocol based on the victim's past health data. For example, the generating AI references the victim's past health data and applies an individually optimized monitoring protocol. For example, monitoring is performed taking into account allergy information and medical history. This makes it possible to apply an individually optimized monitoring protocol by referencing the victim's past health data.

[0080] The victim's state monitoring unit can use the emotion estimation function to analyze the victim's emotional state and instruct responses to reduce stress or anxiety. The victim's state monitoring unit, for example, uses a generative AI to analyze the victim's emotional state in real time and instruct responses to reduce stress and anxiety. For example, it analyzes facial expressions and voice tone to detect signs of stress. This allows the emotion estimation function to analyze the victim's emotional state and instruct responses to reduce stress and anxiety.

[0081] The victim's condition monitoring unit can remotely monitor the victim's vital signs using a wearable device. For example, the generating AI can remotely monitor the victim's vital signs using a wearable device. For example, a smartwatch or heart rate monitor can be used to monitor the heart rate and respiratory rate in real time. This makes it possible to remotely monitor the victim's vital signs using the wearable device.

[0082] The victim's condition monitoring unit visualizes the victim's condition in a 3D model, allowing the rescuer to intuitively understand it. For example, the generative AI visualizes the victim's condition in a 3D model, allowing the rescuer to intuitively understand it. For example, the victim's vital signs and health condition are displayed in a 3D model. This allows the rescuer to intuitively understand the victim's condition by visualizing it in a 3D model.

[0083] The victim's condition monitoring unit can use the emotion estimation function to analyze the victim's emotional state and provide audio guidance to give a sense of security. The victim's condition monitoring unit, for example, uses a generation AI to analyze the victim's emotional state in real time and provide audio guidance to give a sense of security. For example, it analyzes facial expressions and tone of voice to promote a sense of security. In this way, by using the emotion estimation function, it is possible to analyze the victim's emotional state and provide audio guidance to give a sense of security.

[0084] The information provision unit for emergency responders can update the victim's condition in real time and provide the latest information to emergency responders. For example, the generation AI can update the victim's condition in real time and provide the latest information to emergency responders. For example, it can notify changes in heart rate and respiratory rate in real time. This allows the latest information to be provided to emergency responders by updating the victim's condition in real time.

[0085] The unit for providing information to emergency responders can provide the necessary information to emergency responders based on the victim's past medical data. For example, the generation AI references the victim's past medical data and provides the necessary information to emergency responders. For example, it provides information that takes into account allergy information and medical history. In this way, by referencing the victim's past medical data, the necessary information can be provided to emergency responders.

[0086] The unit that provides information to emergency responders can use the emotion estimation function to analyze the stress level of emergency responders and provide information at the appropriate time. For example, the unit that provides information to emergency responders uses a generative AI to analyze the emotional state of emergency responders in real time and evaluate their stress level. For example, it analyzes facial expressions and tone of voice to detect signs of stress. This allows the emotion estimation function to analyze the stress level of emergency responders and provide information at the appropriate time.

[0087] The unit for providing information to the ambulance team can visually show the victim's condition to the ambulance team using AR technology. For example, the generation AI can visually show the victim's condition to the ambulance team using AR technology. For example, the victim's vital signs and health condition can be visually displayed through a smartphone or AR glasses. In this way, the use of AR technology makes it possible to visually show the victim's condition to the ambulance team.

[0088] The information provision unit for emergency responders supports multiple languages ​​and can provide appropriate information to emergency responders who speak different languages. For example, the generation AI supports multiple languages ​​and provides information to emergency responders who speak different languages. For example, it provides information in multiple languages ​​such as English, Spanish, and Chinese. This multilingual support makes it possible to provide appropriate information to emergency responders who speak different languages.

[0089] The information provision unit for emergency responders can use the emotion estimation function to analyze the emotional state of emergency responders and provide information that elicits positive emotions. For example, the information provision unit for emergency responders uses a generative AI to analyze the emotional state of emergency responders in real time and provide information that elicits positive emotions. For example, facial expressions and tone of voice can be analyzed to encourage positive emotions. In this way, the emotion estimation function can be used to analyze the emotional state of emergency responders and provide information that elicits positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The emergency response support system can also be equipped with a visualization section that visualizes the victim's condition in 3D, allowing rescuers to intuitively understand it. For example, the victim's vital signs and health status can be displayed in 3D, allowing rescuers to respond quickly and accurately. This allows rescuers to visually understand the victim's condition, allowing them to make quick and accurate decisions.

[0092] The emergency response support system can also be equipped with a victim condition monitoring unit that monitors the victim's condition in real time and immediately issues instructions if an abnormality occurs. For example, it can detect sudden changes in heart rate or respiratory rate and issue instructions for appropriate responses. This allows the system to constantly monitor the victim's condition and respond quickly if an abnormality occurs.

[0093] The emergency response support system can also be equipped with a drone control unit that uses drones to deliver first aid kits to the scene before an ambulance arrives. For example, the drone's onboard GPS can be used to identify the victim's location and quickly deliver the first aid kit. This allows first aid to be administered before the ambulance arrives, improving the victim's chances of survival.

[0094] The emergency response support system can also be equipped with a traffic control unit that works in conjunction with smart city infrastructure to automatically ensure priority passage for emergency vehicles, for example by controlling traffic signals to give priority to emergency vehicles. This allows emergency vehicles to arrive at the scene more quickly, improving the chances of saving the lives of victims.

[0095] The emergency response support system can also be equipped with a multilingual support unit that provides instructions in multiple languages, such as English, Spanish, and Chinese, to provide appropriate emergency response instructions to people who speak different languages. This allows for quick and appropriate emergency response to be provided to people who speak different languages.

[0096] The emergency response instruction unit can use emotion estimation to analyze the emotional state of rescuers and provide instructions to elicit positive emotions. For example, the generative AI can analyze the rescuer's emotional state in real time and encourage positive emotions by analyzing facial expressions and voice tone. This increases the rescuer's motivation and promotes a quick and accurate response.

[0097] The incident detection unit uses emotion estimation to monitor people's emotions in public places, detecting abnormal emotional changes and identifying emergencies. For example, the generative AI uses a camera and microphone to analyze facial expressions and voice tone to detect abnormal emotional changes. This allows for the rapid identification of emergencies in public places and the appropriate response.

[0098] The ambulance dispatch department can use the emotion estimation function to analyze the caller's state of tension and provide guidance to encourage calm reporting. For example, the generative AI can analyze the caller's emotional state in real time, analyzing facial expressions and tone of voice to detect signs of tension, thereby helping the caller report the situation calmly.

[0099] The victim's condition monitoring unit uses the emotion estimation function to analyze the victim's emotional state and provide instructions for responses to reduce stress and anxiety. For example, the generative AI can analyze the victim's emotional state in real time, analyzing facial expressions and tone of voice to detect signs of stress. This allows the system to understand the victim's emotional state and provide appropriate responses, thereby increasing the victim's sense of security.

[0100] The information provision unit for emergency responders can use the emotion estimation function to analyze the emotional state of emergency responders and provide information to elicit positive emotions. For example, the generative AI can analyze the emotional state of emergency responders in real time, analyzing facial expressions and voice tone to encourage positive emotions. This will boost the morale of emergency responders and promote quick and accurate response.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The accident detection unit detects the occurrence of accidents or sudden illnesses. For example, cameras and sensors are used to detect abnormal movements such as falls, traffic accidents, or collapses due to sudden illness. Audio data can also be analyzed to detect abnormal sounds such as screams or collisions. Furthermore, data from multiple sensors is integrated to detect abnormal movements and environmental changes with high accuracy. Step 2: The emergency response instruction unit issues instructions for appropriate responses to accidents or sudden illnesses detected by the accident detection unit. For example, it provides instructions on cardiopulmonary resuscitation (CPR) procedures via voice or screen, or provides instructions on how to use an AED (Automated External Defibrillator). It can also refer to the victim's past medical data to provide individually optimized emergency responses. It can also analyze the surrounding situation in real time and provide instructions on the best evacuation route or safe location. Step 3: The ambulance dispatch unit dispatches an ambulance in response to an accident or sudden illness detected by the accident detection unit. For example, the generation AI automatically makes an emergency call and prompts the arrival of an ambulance. It can also analyze traffic conditions in real time and dispatch an ambulance via the shortest route. It can also pinpoint the victim's location with high accuracy to optimize the ambulance's arrival time.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

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

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. an accident detection unit that detects the occurrence of an accident or sudden illness; an emergency response instruction unit that instructs an appropriate response to an accident or sudden illness detected by the accident detection unit; an ambulance dispatching unit that dispatches an ambulance in response to an accident or sudden illness detected by the accident detection unit; A system characterized by:

2. The accident detection unit Analyzes surrounding audio data and detects at least one abnormal sound, such as a shout or a crash, to identify the occurrence of an accident or sudden illness.

2. The system of claim 1.

3. The emergency response instruction unit Prescribes an individually optimized emergency response based on the victim's past medical data 2. The system of claim 1.

4. The ambulance dispatch unit Analyzing traffic conditions in real time and dispatching ambulances along the optimal route 2. The system of claim 1.

5. The victim's condition monitoring department Analyze the victim's vital signs in real time and immediately instruct how to respond if any abnormalities occur.

2. The system of claim 1.

6. The accident detection unit Using emotion estimation functionality, the system analyzes changes in the emotions of people around it and predicts the occurrence of an emergency.

2. The system of claim 1.

7. The emergency response instruction unit Uses emotion estimation to analyze the rescuer's stress level and provide encouragement or instructions at the right time 2. The system of claim 1.

8. The ambulance dispatch unit Using emotion estimation functionality, the system analyzes the caller's state of tension and provides guidance to encourage calm reporting.

2. The system of claim 1.

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