system

Abstract

To respond quickly and safely when a heart attack occurs while driving.SOLUTION: A system according to an embodiment includes a detection unit, an operation unit, a location identification unit, and a reporting unit. The detection unit detects a heart attack. The operation unit remotely operates a car on the basis of the heart attack detected by the detection unit. The location identification unit identifies a current location of the car that has been stopped by the operation unit. The reporting unit calls an ambulance on the basis of the current location identified by the location identification unit.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to respond quickly and safely when a heart attack occurs during driving.

[0005] The system according to an embodiment aims to respond quickly and safely when a heart attack occurs during driving.

Means for Solving the Problems

[0006] The system according to an embodiment includes a detection unit, an operation unit, a location identification unit, and a reporting unit. The detection unit detects a heart attack. The operation unit remotely operates the vehicle based on the heart attack detected by the detection unit. The location identification unit identifies the current location of the vehicle stopped by the operation unit. The reporting unit calls an ambulance based on the current location identified by the location identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can respond quickly and safely when a heart attack occurs while driving. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system in which, when a heart attack occurs while driving, the AI ​​detects the attack by bringing a smartphone close to the heart and, in conjunction with a remote control system, stops the car in a safe location. This system is designed to prevent accidents. Furthermore, because the current location can be detected using GPS, an ambulance can be called immediately. The AI ​​is capable of responding to situations where every second counts. For example, if a heart attack occurs while driving, the user brings the smartphone close to their heart. The AI ​​installed in the smartphone detects the heart attack and, in conjunction with the remote control system, remotely controls the car. The car stops in a safe location, preventing an accident. Next, the current location can be detected using GPS, and an ambulance can be called. The AI ​​analyzes the signs of a heart attack in real time and responds quickly. For example, if an abnormal heart rate or arrhythmia is detected, the car will stop immediately and the procedure for calling an ambulance will be initiated. This system prevents accidents caused by heart attacks while driving and enables a rapid medical response. The user can drive with peace of mind because the AI ​​will respond automatically simply by bringing the smartphone close to their heart. This allows the system to prevent accidents caused by heart attacks while driving and to provide a rapid medical response.

[0029] The system according to this embodiment comprises a detection unit, an operation unit, a location identification unit, and a notification unit. The detection unit detects a heart attack. The detection unit can, for example, use AI to detect abnormal heart rate or arrhythmias. For example, the detection unit can detect a sudden increase in heart rate or arrhythmias, enabling early detection of signs of a heart attack. The detection unit can also monitor fluctuations in heart rate in real time and detect abnormalities. The operation unit remotely controls the vehicle based on the heart attack detected by the detection unit. The operation unit can, for example, stop the vehicle in conjunction with a remote control system. For example, the operation unit can remotely control the vehicle using wireless communication and stop it in a safe location. The operation unit can also remotely control the vehicle via the internet. The location identification unit identifies the current location of the vehicle that has been stopped by the operation unit. The location identification unit can, for example, use GPS functionality to identify the current location. For example, the location identification unit uses GPS data to identify the vehicle's current location. The location identification unit can also use geographic coordinates to identify the current location. The notification unit calls an ambulance based on the current location identified by the location identification unit. The notification unit can call an ambulance using, for example, an emergency call system. For example, the notification unit can call an ambulance using a telephone call. The notification unit can also call an ambulance via the internet. As a result, the system according to this embodiment can detect a heart attack, safely stop the vehicle, and quickly call an ambulance.

[0030] The detection unit detects heart attacks. For example, the detection unit can use AI to detect abnormal heart rates and arrhythmias. Specifically, the detection unit analyzes heart rate data acquired from heart rate sensors and wearable devices in real time. The AI ​​has an algorithm that identifies normal and abnormal heart rate patterns based on past heart rate data and medical standards. For example, to detect a sudden increase in heart rate or the occurrence of arrhythmias, the AI ​​analyzes fluctuations in time-series data to detect abnormal patterns early. The AI ​​can also monitor heart rate fluctuations in real time and detect abnormalities. This allows the detection unit to detect signs of a heart attack early and enable a rapid response. Furthermore, the detection unit has a function to issue alerts when an abnormality is detected. For example, it can send notifications to the user's smartphone or the vehicle's infotainment system to inform the user of the abnormality. The detection unit can also automatically send information to the control unit or notification unit when an abnormality is detected, prompting a rapid response. This allows the detection unit to support the early detection and rapid response to heart attacks, ensuring user safety.

[0031] The control unit remotely operates the vehicle based on a heart attack detected by the detection unit. Specifically, the control unit can stop the vehicle in conjunction with the remote control system. For example, the control unit can remotely operate the vehicle using wireless communication and stop it in a safe location. The control unit can work with the vehicle's control system to stop the engine, apply the brakes, and activate the hazard lights. This ensures that the vehicle can be safely stopped even if the driver suffers a heart attack. The control unit can also remotely operate the vehicle via the internet. For example, it can access the vehicle's control system via a cloud server and operate the vehicle remotely. This allows the control unit to stop the vehicle quickly and safely when the driver suffers a heart attack. Furthermore, the control unit has the function of monitoring the vehicle's current location and surrounding conditions in real time and selecting the optimal stopping position. For example, it can analyze GPS data and camera footage to identify a safe stopping position. This allows the control unit to ensure the driver's safety while minimizing the impact on other road users.

[0032] The location tracking unit identifies the current location of a vehicle parked by the operation unit. Specifically, the location tracking unit can determine the current location using GPS functionality. For example, the location tracking unit analyzes data acquired from a GPS module installed in the vehicle to determine the vehicle's precise location. GPS data includes geographic coordinates such as latitude, longitude, and altitude, allowing for a detailed understanding of the vehicle's current location. The location tracking unit can also determine the current location using geographic coordinates. For example, it can link with a map database to analyze which point corresponds to a specific coordinate. This allows the location tracking unit to quickly and accurately identify the vehicle's current location and provide information to the reporting unit. Furthermore, the location tracking unit can record the vehicle's movement history and refer to past location data. This allows the location tracking unit to understand the vehicle's movement patterns and parking location history, which can be used to assist in emergency responses. For example, based on past parking location data, it can provide reference information for selecting an appropriate parking location in an emergency. This enables the location tracking unit to accurately identify the vehicle's current location and support a rapid response.

[0033] The reporting unit calls an ambulance based on its current location, which is determined by the location identification unit. Specifically, the reporting unit can call an ambulance using the emergency call system. For example, the reporting unit can call an ambulance using a telephone call. The reporting unit can automatically transmit the vehicle's current location information to the emergency call center to facilitate a quick response. The reporting unit can also call an ambulance via the internet. For example, it can access the emergency call system via the internet and transmit the vehicle's current location information. This allows the reporting unit to call an ambulance quickly and reliably. Furthermore, the reporting unit has a function to automatically generate the content of the emergency call. For example, it can use AI to automatically generate the content of the emergency call and send it to the emergency call center. This allows the reporting unit to create emergency call content quickly and accurately, supporting emergency response. The reporting unit also has a follow-up function after the emergency call. For example, it can monitor the estimated arrival time of the ambulance and its current location in real time and notify the user. This allows the reporting unit to reduce anxiety while waiting for the ambulance and support a quick response.

[0034] The heart rate detection unit can detect abnormalities in heart rate. The heart rate detection unit can detect abnormalities in heart rate using, for example, AI. For example, the heart rate detection unit can set upper and lower limits for heart rate and detect abnormalities when these limits are exceeded. The heart rate detection unit can also analyze the rhythm of the heart rate and detect abnormal rhythms. For example, the heart rate detection unit can monitor fluctuations in heart rate in real time and detect abnormalities. This makes it possible to detect heart attacks early by detecting abnormalities in heart rate. Some or all of the above processing in the heart rate detection unit may be performed using, for example, AI, or without AI. For example, the heart rate detection unit can input heart rate data into a generating AI and have the generating AI perform abnormality detection.

[0035] The control unit can stop the vehicle in conjunction with a remote control system. For example, the control unit can stop the vehicle in conjunction with a remote control system. For example, the control unit can remotely control the vehicle using wireless communication and stop it in a safe location. The control unit can also remotely control the vehicle via the internet. For example, the control unit executes a control algorithm for stopping the vehicle using the remote control system. This allows the vehicle to be stopped safely in conjunction with the remote control system. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the control algorithm of the remote control system into a generating AI and have the generating AI execute the vehicle stopping.

[0036] The location-determining unit can determine its current location using GPS functionality. For example, the location-determining unit can determine its current location using GPS functionality. For example, the location-determining unit can determine the car's current location using GPS data. The location-determining unit can also determine its current location using geographic coordinates. For example, the location-determining unit can specify the satellite system to use to improve the accuracy of location determination. This allows the current location to be accurately determined by using GPS functionality. Some or all of the above-described processes in the location-determining unit may be performed using AI, for example, or without AI. For example, the location-determining unit can input GPS data into a generating AI and have the generating AI perform location determination.

[0037] The reporting unit can initiate the process of calling an ambulance based on the current location. For example, the reporting unit can call an ambulance using an emergency call system. The reporting unit can also call an ambulance using a telephone call. For example, the reporting unit can specify a recipient and transmit the message. This allows for the rapid dispatch of an ambulance based on the current location. Some or all of the above-described processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the message into a generating AI and have the generating AI execute the ambulance call.

[0038] The detection unit can detect early signs of a seizure by referring to the user's past health data. For example, the detection unit can refer to the user's past heart rate data and detect abnormal patterns early. The detection unit can also refer to the user's past medical records and issue a warning if the risk of a heart attack is high. Furthermore, the detection unit can refer to the user's past exercise history and detect signs of a seizure by considering fluctuations in heart rate after exercise. In this way, signs of a seizure can be detected early by referring to past health data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past health data into a generating AI and have the generating AI perform early detection of signs of a seizure.

[0039] The detection unit can adjust the detection algorithm based on the user's current activity status. For example, the detection unit can optimize the detection algorithm by considering the user's current activity status. For instance, if the user is exercising, the detection unit can detect signs of an impending seizure by considering a sudden increase in heart rate. The detection unit can also detect an abnormal decrease in heart rate when the user is resting and assess the risk of an impending seizure. Furthermore, if the user is driving, the detection unit can monitor heart rate fluctuations in real time and detect signs of an impending seizure early. This allows the detection algorithm to be optimized by considering the current activity status. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input current activity data into a generating AI and have the generating AI optimize the detection algorithm.

[0040] The detection unit can assess the risk of seizures by considering the user's geographical location. For example, if the user is at high altitude, the detection unit can assess the risk of seizures by considering the decrease in oxygen concentration. The detection unit can also assess the risk of seizures by considering stressors if the user is in an urban area. Furthermore, if the user is at home, the detection unit can assess the risk of seizures by considering the relaxed environment. This allows for an accurate assessment of seizure risk by considering geographical location. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input geographical location information into a generating AI and have the generating AI perform the seizure risk assessment.

[0041] The detection unit can analyze a user's social media activity and improve detection accuracy by considering their stress level. For example, if a user frequently posts stressful content, the detection unit can closely monitor heart rate fluctuations and detect early signs of a heart attack. Furthermore, if a user posts relaxing content, the detection unit can determine that heart rate fluctuations are within the normal range, reducing false positives. Additionally, if a user posts exciting content, the detection unit can assess the risk of a heart attack by considering a rapid increase in heart rate. This allows for improved detection accuracy by analyzing social media activity and considering stress levels. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input social media activity data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0042] The control unit can select a stopping position based on the vehicle's current speed and traffic conditions. For example, the control unit can select the optimal stopping position by considering the vehicle's current speed and traffic conditions. For example, if the vehicle is traveling on a highway, the AI ​​can select the nearest service area as the stopping position. If the vehicle is traveling in an urban area, the AI ​​can also select a safe shoulder as the stopping position. Furthermore, if the vehicle is stuck in traffic, the AI ​​can select the safest stopping position. In this way, the optimal stopping position can be selected by considering speed and traffic conditions. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input speed and traffic condition data into a generating AI and have the generating AI perform the stopping position selection.

[0043] The control unit can ensure safety by referring to other sensor information inside the vehicle. For example, the control unit can ensure safety by referring to other sensor information inside the vehicle. For example, if a seat belt is not fastened, the control unit will have the AI ​​issue a warning before stopping. The control unit can also refer to temperature sensor information inside the vehicle to select an appropriate stopping position. Furthermore, the control unit can refer to camera information inside the vehicle to select a stopping method that ensures the safety of the occupants. In this way, safety can be ensured by referring to other sensor information. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI. For example, the control unit can input other sensor information into a generating AI and have the generating AI perform safety assurance.

[0044] The control unit can select the optimal parking location by considering the vehicle's geographical location. For example, if the vehicle is traveling through a mountainous area, the AI ​​can select the nearest spacious area as the parking location. If the vehicle is traveling through an urban area, the AI ​​can also select a safe parking lot as the parking location. Furthermore, if the vehicle is traveling through a suburban area, the AI ​​can also select a safe roadside as the parking location. In this way, the optimal parking location can be selected by considering geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input geographical location information into a generating AI and have the generating AI perform the selection of the parking location.

[0045] The control unit can select the optimal parking method by referring to the vehicle's past driving history. For example, the control unit can select the optimal parking location based on places where the vehicle has previously parked. The control unit can also select a parking method that avoids congestion based on the vehicle's past driving history. Furthermore, the control unit can analyze the vehicle's past driving history and select the safest parking method. In this way, the optimal parking method can be selected by referring to past driving history. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past driving history data into a generating AI and have the generating AI perform the selection of a parking method.

[0046] The location identification unit can improve the accuracy of determining the current location by referring to past location data. For example, the location identification unit can improve the accuracy of determining the current location by referring to past location data. For example, the location identification unit can improve the accuracy of determining the current location by referring to the user's past location data. The location identification unit can also improve the accuracy of determining the location at a specific location from past location data. Furthermore, the location identification unit can analyze past location data and optimize the accuracy of determining the current location. In this way, the accuracy of determining the current location can be improved by referring to past location data. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input past location data into a generating AI and have the generating AI perform the improvement of the accuracy of determining the current location.

[0047] The location identification unit can improve its accuracy based on surrounding geographic information. For example, the location identification unit can improve its accuracy by considering surrounding geographic information. For example, the location identification unit can improve the accuracy of determining the current location by referring to surrounding building information. The location identification unit can also improve the accuracy of determining the current location by referring to road layout information. Furthermore, the location identification unit can analyze geographic information and optimize the accuracy of determining the current location. In this way, the accuracy of location identification can be improved by considering surrounding geographic information. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input surrounding geographic information into a generating AI and have the generating AI perform the accuracy improvement.

[0048] The location identification unit can improve its identification accuracy by considering the user's geographical location information. For example, if the user is in an urban area, the location identification unit can improve its identification accuracy by referring to surrounding building information. Also, if the user is in a suburban area, the location identification unit can improve its identification accuracy by referring to road layout information. Furthermore, if the user is in a mountainous area, the location identification unit can improve its identification accuracy by referring to terrain information. In this way, identification accuracy can be improved by considering geographical location information. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input geographical location information into a generating AI and have the generating AI perform the improvement of identification accuracy.

[0049] The reporting unit can select the most appropriate reporting method by referring to past reporting history. For example, the reporting unit can select the most appropriate reporting method based on the reporting methods previously used by the user. The reporting unit can also select a rapid reporting method from past reporting history. Furthermore, the reporting unit can analyze the reporting history and select the most effective reporting method. This allows the reporting unit to select the most appropriate reporting method by referring to past reporting history. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past reporting history data into a generating AI and have the generating AI perform the selection of the reporting method.

[0050] The notification unit can optimize the notification method considering the current communication status. For example, if the signal strength is weak, the AI ​​can select the most stable communication method. Also, if the communication status is good, the AI ​​can select a rapid notification method. Furthermore, the notification unit can monitor the communication status in real time and select the optimal notification method. In this way, the optimal notification method can be selected by considering the communication status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input communication status data into a generating AI and have the generating AI perform the optimization of the notification method.

[0051] The notification unit can select the most appropriate notification method by considering the user's geographical location. For example, if the user is in an urban area, the notification unit can initiate the process of calling the nearest ambulance. If the user is in a suburban area, the notification unit can also initiate the process of calling the ambulance that can arrive most quickly. Furthermore, if the user is in a mountainous area, the notification unit can select the most appropriate emergency medical service. In this way, the notification unit can select the most appropriate notification method by considering geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input geographical location information into a generating AI and have the generating AI perform the selection of the notification method.

[0052] The reporting unit can analyze a user's social media activity and optimize the content of the report. For example, the reporting unit can analyze a user's social media activity and optimize the content of the report. For example, the reporting unit can refer to location information posted by the user on social media and optimize the content of the report. The reporting unit can also select the most appropriate content of the report from the user's social media activity. Furthermore, the reporting unit can analyze social media activity and optimize the content of the report. This allows for the selection of the most appropriate content of the report by analyzing social media activity. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input social media activity data into a generating AI and have the generating AI perform the optimization of the report content.

[0053] The heart rate detection unit can detect abnormalities early by referring to past heart rate data. For example, the heart rate detection unit can refer to the user's past heart rate data and detect abnormal patterns early. The heart rate detection unit can also refer to the user's past medical records and evaluate heart rate abnormalities. Furthermore, the heart rate detection unit can refer to the user's past exercise history and detect abnormalities by considering fluctuations in heart rate after exercise. In this way, abnormalities can be detected early by referring to past heart rate data. Some or all of the above processing in the heart rate detection unit may be performed using AI, for example, or without AI. For example, the heart rate detection unit can input past heart rate data into a generating AI and have the generating AI perform early detection of abnormalities.

[0054] The heart rate detection unit can optimize its detection algorithm by considering the user's current activity level. For example, if the user is exercising, the heart rate detection unit can detect abnormalities by considering a sudden increase in heart rate. Furthermore, if the user is resting, the heart rate detection unit can detect abnormal decreases in heart rate and evaluate the abnormality. Additionally, if the user is driving, the heart rate detection unit can monitor heart rate fluctuations in real time and detect abnormalities early. This allows the detection algorithm to be optimized by considering the current activity level. Some or all of the above processing in the heart rate detection unit may be performed using AI, for example, or without AI. For example, the heart rate detection unit can input current activity data into a generating AI and have the generating AI optimize the detection algorithm.

[0055] The heart rate detection unit can evaluate abnormalities by taking into account the user's geographical location information. For example, if the user is at high altitude, the heart rate detection unit can evaluate heart rate abnormalities by taking into account the decrease in oxygen concentration. Also, if the user is in an urban area, the heart rate detection unit can evaluate heart rate abnormalities by taking into account stress factors. Furthermore, if the user is at home, the heart rate detection unit can evaluate heart rate abnormalities by taking into account the relaxed environment. In this way, abnormalities can be accurately evaluated by taking into account geographical location information. Some or all of the above processing in the heart rate detection unit may be performed using AI, for example, or without using AI. For example, the heart rate detection unit can input geographical location information into a generating AI and have the generating AI perform the abnormality evaluation.

[0056] The heart rate detection unit can analyze the user's social media activity and improve detection accuracy by considering the stress level. For example, if the user frequently posts stressful content, the heart rate detection unit can closely monitor heart rate fluctuations and detect abnormalities early. Furthermore, if the user posts relaxed content, the heart rate detection unit can determine that heart rate fluctuations are within the normal range, reducing false positives. Additionally, if the user posts excited content, the heart rate detection unit can evaluate abnormalities by considering the rapid increase in heart rate. This allows for improved detection accuracy by analyzing social media activity and considering stress levels. Some or all of the above processing in the heart rate detection unit may be performed using AI, for example, or without AI. For example, the heart rate detection unit can input social media activity data into a generating AI and have the generating AI perform the detection accuracy improvement.

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

[0058] The detection unit can detect the user's body temperature and identify abnormalities. For example, if the user's body temperature rises rapidly, the detection unit can determine that the risk of a seizure is high and issue an early warning. The detection unit can also monitor temperature fluctuations in real time and detect abnormalities. Furthermore, the detection unit can analyze body temperature data in combination with other health data to detect seizure signs more accurately. This allows for early detection of seizure risk by detecting abnormal body temperature.

[0059] The control unit can analyze the user's driving style and select the optimal stopping method. For example, if the user frequently uses sudden braking, the control unit will select a more cautious stopping method. Furthermore, if the user frequently uses highways, the control unit can select a safe stopping method for highways. Additionally, if the user primarily drives in urban areas, the control unit can select the optimal stopping method for urban areas. This allows the system to select the optimal stopping method based on the user's driving style.

[0060] The location tracking unit can improve the accuracy of determining the current location by considering surrounding weather information. For example, if the GPS signal weakens during rainy weather, the location tracking unit can improve its accuracy by referring to other data sources. It can also improve its accuracy by referring to terrain information when it is snowing. Furthermore, if there are strong winds, the location tracking unit can optimize its accuracy by considering wind speed data. In this way, the accuracy of location tracking can be improved by considering weather information.

[0061] The reporting system can select the most appropriate reporting content by referring to the user's medical history. For example, if the user has previously experienced a heart attack, the reporting system will select reporting content that includes detailed medical information. It can also select reporting content that includes allergy information if the user has allergies. Furthermore, if the reporting system is taking specific medications, it can select reporting content that includes information about those medications. This allows the system to select the most appropriate reporting content by referring to the user's medical history.

[0062] The detection unit can detect signs of seizures early by referring to the user's past exercise data. For example, the detection unit can refer to the user's past exercise data and detect abnormal patterns early. It can also refer to the user's past exercise history and detect signs of seizures by considering fluctuations in heart rate after exercise. Furthermore, the detection unit can analyze the user's past exercise data and issue a warning if the risk of seizures is high. This allows for the early detection of seizure signs by referring to past exercise data.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The detection unit detects a heart attack. The detection unit can, for example, use AI to detect abnormal heart rates or arrhythmias. For example, it can detect a sudden increase in heart rate or arrhythmias, enabling early detection of signs of a heart attack. It can also monitor heart rate fluctuations in real time and detect abnormalities. Step 2: The control unit remotely operates the vehicle based on the heart attack detected by the detection unit. The control unit can, for example, stop the vehicle in conjunction with a remote control system. The vehicle can be remotely operated via wireless communication or the internet and parked in a safe location. Step 3: The location identification unit determines the current location of the parked vehicle controlled by the operation unit. The location identification unit can determine the current location, for example, by using GPS functionality. It determines the vehicle's current location using GPS data or geographic coordinates. Step 4: The reporting unit calls an ambulance based on the current location identified by the location identification unit. The reporting unit can call an ambulance using, for example, an emergency call system. The ambulance can be called via telephone or the internet.

[0065] (Example of form 2) The system according to an embodiment of the present invention is a system in which, when a heart attack occurs while driving, the AI ​​detects the attack by bringing a smartphone close to the heart and, in conjunction with a remote control system, stops the car in a safe location. This system is designed to prevent accidents. Furthermore, because the current location can be detected using GPS, an ambulance can be called immediately. The AI ​​is capable of responding to situations where every second counts. For example, if a heart attack occurs while driving, the user brings the smartphone close to their heart. The AI ​​installed in the smartphone detects the heart attack and, in conjunction with the remote control system, remotely controls the car. The car stops in a safe location, preventing an accident. Next, the current location can be detected using GPS, and an ambulance can be called. The AI ​​analyzes the signs of a heart attack in real time and responds quickly. For example, if an abnormal heart rate or arrhythmia is detected, the car will stop immediately and the procedure for calling an ambulance will be initiated. This system prevents accidents caused by heart attacks while driving and enables a rapid medical response. The user can drive with peace of mind because the AI ​​will respond automatically simply by bringing the smartphone close to their heart. This allows the system to prevent accidents caused by heart attacks while driving and to provide a rapid medical response.

[0066] The system according to this embodiment comprises a detection unit, an operation unit, a location identification unit, and a notification unit. The detection unit detects a heart attack. The detection unit can, for example, use AI to detect abnormal heart rate or arrhythmias. For example, the detection unit can detect a sudden increase in heart rate or arrhythmias, enabling early detection of signs of a heart attack. The detection unit can also monitor fluctuations in heart rate in real time and detect abnormalities. The operation unit remotely controls the vehicle based on the heart attack detected by the detection unit. The operation unit can, for example, stop the vehicle in conjunction with a remote control system. For example, the operation unit can remotely control the vehicle using wireless communication and stop it in a safe location. The operation unit can also remotely control the vehicle via the internet. The location identification unit identifies the current location of the vehicle that has been stopped by the operation unit. The location identification unit can, for example, use GPS functionality to identify the current location. For example, the location identification unit uses GPS data to identify the vehicle's current location. The location identification unit can also use geographic coordinates to identify the current location. The notification unit calls an ambulance based on the current location identified by the location identification unit. The notification unit can call an ambulance using, for example, an emergency call system. For example, the notification unit can call an ambulance using a telephone call. The notification unit can also call an ambulance via the internet. As a result, the system according to this embodiment can detect a heart attack, safely stop the vehicle, and quickly call an ambulance.

[0067] The detection unit detects heart attacks. For example, the detection unit can use AI to detect abnormal heart rates and arrhythmias. Specifically, the detection unit analyzes heart rate data acquired from heart rate sensors and wearable devices in real time. The AI ​​has an algorithm that identifies normal and abnormal heart rate patterns based on past heart rate data and medical standards. For example, to detect a sudden increase in heart rate or the occurrence of arrhythmias, the AI ​​analyzes fluctuations in time-series data to detect abnormal patterns early. The AI ​​can also monitor heart rate fluctuations in real time and detect abnormalities. This allows the detection unit to detect signs of a heart attack early and enable a rapid response. Furthermore, the detection unit has a function to issue alerts when an abnormality is detected. For example, it can send notifications to the user's smartphone or the vehicle's infotainment system to inform the user of the abnormality. The detection unit can also automatically send information to the control unit or notification unit when an abnormality is detected, prompting a rapid response. This allows the detection unit to support the early detection and rapid response to heart attacks, ensuring user safety.

[0068] The control unit remotely operates the vehicle based on a heart attack detected by the detection unit. Specifically, the control unit can stop the vehicle in conjunction with the remote control system. For example, the control unit can remotely operate the vehicle using wireless communication and stop it in a safe location. The control unit can work with the vehicle's control system to stop the engine, apply the brakes, and activate the hazard lights. This ensures that the vehicle can be safely stopped even if the driver suffers a heart attack. The control unit can also remotely operate the vehicle via the internet. For example, it can access the vehicle's control system via a cloud server and operate the vehicle remotely. This allows the control unit to stop the vehicle quickly and safely when the driver suffers a heart attack. Furthermore, the control unit has the function of monitoring the vehicle's current location and surrounding conditions in real time and selecting the optimal stopping position. For example, it can analyze GPS data and camera footage to identify a safe stopping position. This allows the control unit to ensure the driver's safety while minimizing the impact on other road users.

[0069] The location tracking unit identifies the current location of a vehicle parked by the operation unit. Specifically, the location tracking unit can determine the current location using GPS functionality. For example, the location tracking unit analyzes data acquired from a GPS module installed in the vehicle to determine the vehicle's precise location. GPS data includes geographic coordinates such as latitude, longitude, and altitude, allowing for a detailed understanding of the vehicle's current location. The location tracking unit can also determine the current location using geographic coordinates. For example, it can link with a map database to analyze which point corresponds to a specific coordinate. This allows the location tracking unit to quickly and accurately identify the vehicle's current location and provide information to the reporting unit. Furthermore, the location tracking unit can record the vehicle's movement history and refer to past location data. This allows the location tracking unit to understand the vehicle's movement patterns and parking location history, which can be used to assist in emergency responses. For example, based on past parking location data, it can provide reference information for selecting an appropriate parking location in an emergency. This enables the location tracking unit to accurately identify the vehicle's current location and support a rapid response.

[0070] The reporting unit calls an ambulance based on its current location, which is determined by the location identification unit. Specifically, the reporting unit can call an ambulance using the emergency call system. For example, the reporting unit can call an ambulance using a telephone call. The reporting unit can automatically transmit the vehicle's current location information to the emergency call center to facilitate a quick response. The reporting unit can also call an ambulance via the internet. For example, it can access the emergency call system via the internet and transmit the vehicle's current location information. This allows the reporting unit to call an ambulance quickly and reliably. Furthermore, the reporting unit has a function to automatically generate the content of the emergency call. For example, it can use AI to automatically generate the content of the emergency call and send it to the emergency call center. This allows the reporting unit to create emergency call content quickly and accurately, supporting emergency response. The reporting unit also has a follow-up function after the emergency call. For example, it can monitor the estimated arrival time of the ambulance and its current location in real time and notify the user. This allows the reporting unit to reduce anxiety while waiting for the ambulance and support a quick response.

[0071] The heart rate detection unit can detect abnormalities in heart rate. The heart rate detection unit can detect abnormalities in heart rate using, for example, AI. For example, the heart rate detection unit can set upper and lower limits for heart rate and detect abnormalities when these limits are exceeded. The heart rate detection unit can also analyze the rhythm of the heart rate and detect abnormal rhythms. For example, the heart rate detection unit can monitor fluctuations in heart rate in real time and detect abnormalities. This makes it possible to detect heart attacks early by detecting abnormalities in heart rate. Some or all of the above processing in the heart rate detection unit may be performed using, for example, AI, or without AI. For example, the heart rate detection unit can input heart rate data into a generating AI and have the generating AI perform abnormality detection.

[0072] The control unit can stop the vehicle in conjunction with a remote control system. For example, the control unit can stop the vehicle in conjunction with a remote control system. For example, the control unit can remotely control the vehicle using wireless communication and stop it in a safe location. The control unit can also remotely control the vehicle via the internet. For example, the control unit executes a control algorithm for stopping the vehicle using the remote control system. This allows the vehicle to be stopped safely in conjunction with the remote control system. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the control algorithm of the remote control system into a generating AI and have the generating AI execute the vehicle stopping.

[0073] The location-determining unit can determine its current location using GPS functionality. For example, the location-determining unit can determine its current location using GPS functionality. For example, the location-determining unit can determine the car's current location using GPS data. The location-determining unit can also determine its current location using geographic coordinates. For example, the location-determining unit can specify the satellite system to use to improve the accuracy of location determination. This allows the current location to be accurately determined by using GPS functionality. Some or all of the above-described processes in the location-determining unit may be performed using AI, for example, or without AI. For example, the location-determining unit can input GPS data into a generating AI and have the generating AI perform location determination.

[0074] The reporting unit can initiate the process of calling an ambulance based on the current location. For example, the reporting unit can call an ambulance using an emergency call system. The reporting unit can also call an ambulance using a telephone call. For example, the reporting unit can specify a recipient and transmit the message. This allows for the rapid dispatch of an ambulance based on the current location. Some or all of the above-described processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the message into a generating AI and have the generating AI execute the ambulance call.

[0075] The detection unit can estimate the user's emotions and adjust the accuracy of heart attack detection based on the estimated emotions. For example, if the user is stressed, the AI ​​can more closely monitor heart rate fluctuations and detect signs of a heart attack early. Also, if the user is relaxed, the AI ​​can determine that heart rate fluctuations are within the normal range, reducing false positives. Furthermore, if the user is excited, the AI ​​can detect a sudden increase in heart rate and consider it as a factor that increases the risk of a heart attack. This reduces false positives by adjusting the accuracy of heart attack detection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI or not using AI. For example, the detection unit can input user emotion data into a generating AI and have the generating AI adjust the accuracy of heart attack detection.

[0076] The detection unit can detect early signs of a seizure by referring to the user's past health data. For example, the detection unit can refer to the user's past heart rate data and detect abnormal patterns early. The detection unit can also refer to the user's past medical records and issue a warning if the risk of a heart attack is high. Furthermore, the detection unit can refer to the user's past exercise history and detect signs of a seizure by considering fluctuations in heart rate after exercise. In this way, signs of a seizure can be detected early by referring to past health data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past health data into a generating AI and have the generating AI perform early detection of signs of a seizure.

[0077] The detection unit can adjust the detection algorithm based on the user's current activity status. For example, the detection unit can optimize the detection algorithm by considering the user's current activity status. For instance, if the user is exercising, the detection unit can detect signs of an impending seizure by considering a sudden increase in heart rate. The detection unit can also detect an abnormal decrease in heart rate when the user is resting and assess the risk of an impending seizure. Furthermore, if the user is driving, the detection unit can monitor heart rate fluctuations in real time and detect signs of an impending seizure early. This allows the detection algorithm to be optimized by considering the current activity status. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input current activity data into a generating AI and have the generating AI optimize the detection algorithm.

[0078] The detection unit can estimate the user's emotions and adjust the notification method of the detection result based on the estimated user emotions. For example, if the user is tense, the detection unit can notify the user of the detection result in a calm voice. If the user is relaxed, the detection unit can also provide a notification with a detailed explanation. Furthermore, if the user is in a hurry, the detection unit can provide a concise and quick notification. This allows for appropriate notifications by adjusting the notification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generative AI and have the generative AI adjust the notification method.

[0079] The detection unit can assess the risk of seizures by considering the user's geographical location. For example, if the user is at high altitude, the detection unit can assess the risk of seizures by considering the decrease in oxygen concentration. The detection unit can also assess the risk of seizures by considering stressors if the user is in an urban area. Furthermore, if the user is at home, the detection unit can assess the risk of seizures by considering the relaxed environment. This allows for an accurate assessment of seizure risk by considering geographical location. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input geographical location information into a generating AI and have the generating AI perform the seizure risk assessment.

[0080] The detection unit can analyze a user's social media activity and improve detection accuracy by considering their stress level. For example, if a user frequently posts stressful content, the detection unit can closely monitor heart rate fluctuations and detect early signs of a heart attack. Furthermore, if a user posts relaxing content, the detection unit can determine that heart rate fluctuations are within the normal range, reducing false positives. Additionally, if a user posts exciting content, the detection unit can assess the risk of a heart attack by considering a rapid increase in heart rate. This allows for improved detection accuracy by analyzing social media activity and considering stress levels. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input social media activity data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0081] The control unit can estimate the user's emotions and adjust the car's stopping method based on the estimated emotions. For example, if the user is tense, the control unit can have the AI ​​stop the car slowly and safely. If the user is relaxed, the control unit can have the AI ​​quickly select a stopping position. Furthermore, if the user is in a hurry, the control unit can have the AI ​​select the method to stop safely in the shortest time. This ensures safe stopping by adjusting the stopping method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user emotion data into the generative AI and have the generative AI adjust the stopping method.

[0082] The control unit can select a stopping position based on the vehicle's current speed and traffic conditions. For example, the control unit can select the optimal stopping position by considering the vehicle's current speed and traffic conditions. For example, if the vehicle is traveling on a highway, the AI ​​can select the nearest service area as the stopping position. If the vehicle is traveling in an urban area, the AI ​​can also select a safe shoulder as the stopping position. Furthermore, if the vehicle is stuck in traffic, the AI ​​can select the safest stopping position. In this way, the optimal stopping position can be selected by considering speed and traffic conditions. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input speed and traffic condition data into a generating AI and have the generating AI perform the stopping position selection.

[0083] The control unit can ensure safety by referring to other sensor information inside the vehicle. For example, the control unit can ensure safety by referring to other sensor information inside the vehicle. For example, if a seat belt is not fastened, the control unit will have the AI ​​issue a warning before stopping. The control unit can also refer to temperature sensor information inside the vehicle to select an appropriate stopping position. Furthermore, the control unit can refer to camera information inside the vehicle to select a stopping method that ensures the safety of the occupants. In this way, safety can be ensured by referring to other sensor information. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI. For example, the control unit can input other sensor information into a generating AI and have the generating AI perform safety assurance.

[0084] The control unit can estimate the user's emotions and adjust the notification method after stopping based on the estimated emotions. For example, if the user is tense, the control unit can notify the user that stopping is complete in a calm voice. If the user is relaxed, the control unit can also provide a notification with a detailed explanation. Furthermore, if the user is in a hurry, the control unit can provide a concise and quick notification. This allows for appropriate notifications by adjusting the notification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0085] The control unit can select the optimal parking location by considering the vehicle's geographical location. For example, if the vehicle is traveling through a mountainous area, the AI ​​can select the nearest spacious area as the parking location. If the vehicle is traveling through an urban area, the AI ​​can also select a safe parking lot as the parking location. Furthermore, if the vehicle is traveling through a suburban area, the AI ​​can also select a safe roadside as the parking location. In this way, the optimal parking location can be selected by considering geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input geographical location information into a generating AI and have the generating AI perform the selection of the parking location.

[0086] The control unit can select the optimal parking method by referring to the vehicle's past driving history. For example, the control unit can select the optimal parking location based on places where the vehicle has previously parked. The control unit can also select a parking method that avoids congestion based on the vehicle's past driving history. Furthermore, the control unit can analyze the vehicle's past driving history and select the safest parking method. In this way, the optimal parking method can be selected by referring to past driving history. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past driving history data into a generating AI and have the generating AI perform the selection of a parking method.

[0087] The location tracking unit can estimate the user's emotions and adjust the accuracy of location tracking based on the estimated emotions. For example, if the user is stressed, the location tracking unit can use AI to reference multiple data sources to improve the accuracy of location tracking. If the user is relaxed, the location tracking unit can use AI to perform location tracking with normal accuracy. Furthermore, if the user is in a hurry, the location tracking unit can use AI to select the optimal data source for rapid location tracking. This allows for accurate location tracking by adjusting the accuracy of location tracking based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the location tracking unit may be performed using AI or not using AI. For example, the location tracking unit can input user emotion data into a generative AI and have the generative AI adjust the accuracy of location tracking.

[0088] The location identification unit can improve the accuracy of determining the current location by referring to past location data. For example, the location identification unit can improve the accuracy of determining the current location by referring to past location data. For example, the location identification unit can improve the accuracy of determining the current location by referring to the user's past location data. The location identification unit can also improve the accuracy of determining the location at a specific location from past location data. Furthermore, the location identification unit can analyze past location data and optimize the accuracy of determining the current location. In this way, the accuracy of determining the current location can be improved by referring to past location data. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input past location data into a generating AI and have the generating AI perform the improvement of the accuracy of determining the current location.

[0089] The location identification unit can improve its accuracy based on surrounding geographic information. For example, the location identification unit can improve its accuracy by considering surrounding geographic information. For example, the location identification unit can improve the accuracy of determining the current location by referring to surrounding building information. The location identification unit can also improve the accuracy of determining the current location by referring to road layout information. Furthermore, the location identification unit can analyze geographic information and optimize the accuracy of determining the current location. In this way, the accuracy of location identification can be improved by considering surrounding geographic information. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input surrounding geographic information into a generating AI and have the generating AI perform the accuracy improvement.

[0090] The location tracking unit can estimate the user's emotions and adjust the notification method for location tracking results based on the estimated emotions. For example, if the user is tense, the location tracking unit can notify the user of the location tracking results in a calm voice. If the user is relaxed, the location tracking unit can also provide a notification with a detailed explanation. Furthermore, if the user is in a hurry, the location tracking unit can provide a concise and quick notification. This allows for appropriate notifications by adjusting the notification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the location tracking unit may be performed using AI, for example, or without AI. For example, the location tracking unit can input user emotion data into the generative AI and have the generative AI adjust the notification method.

[0091] The location identification unit can improve its identification accuracy by considering the user's geographical location information. For example, if the user is in an urban area, the location identification unit can improve its identification accuracy by referring to surrounding building information. Also, if the user is in a suburban area, the location identification unit can improve its identification accuracy by referring to road layout information. Furthermore, if the user is in a mountainous area, the location identification unit can improve its identification accuracy by referring to terrain information. In this way, identification accuracy can be improved by considering geographical location information. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input geographical location information into a generating AI and have the generating AI perform the improvement of identification accuracy.

[0092] The reporting unit can estimate the user's emotions and adjust the reporting method based on the estimated emotions. For example, if the user is nervous, the reporting unit can perform the reporting procedure in a calm voice. If the user is relaxed, the reporting unit can also perform the reporting procedure with a detailed explanation. Furthermore, if the user is in a hurry, the reporting unit can perform the reporting procedure concisely and quickly. This allows for appropriate reporting by adjusting the reporting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the reporting method.

[0093] The reporting unit can select the most appropriate reporting method by referring to past reporting history. For example, the reporting unit can select the most appropriate reporting method based on the reporting methods previously used by the user. The reporting unit can also select a rapid reporting method from past reporting history. Furthermore, the reporting unit can analyze the reporting history and select the most effective reporting method. This allows the reporting unit to select the most appropriate reporting method by referring to past reporting history. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past reporting history data into a generating AI and have the generating AI perform the selection of the reporting method.

[0094] The notification unit can optimize the notification method considering the current communication status. For example, if the signal strength is weak, the AI ​​can select the most stable communication method. Also, if the communication status is good, the AI ​​can select a rapid notification method. Furthermore, the notification unit can monitor the communication status in real time and select the optimal notification method. In this way, the optimal notification method can be selected by considering the communication status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input communication status data into a generating AI and have the generating AI perform the optimization of the notification method.

[0095] The reporting unit can estimate the user's emotions and adjust the content of the report based on the estimated emotions. For example, if the user is nervous, the reporting unit can deliver the report in a calm voice. If the user is relaxed, the reporting unit can also deliver a report with detailed explanations. Furthermore, if the user is in a hurry, the reporting unit can deliver a concise and quick report. This allows for appropriate reporting by adjusting the content of the report based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the report content.

[0096] The notification unit can select the most appropriate notification method by considering the user's geographical location. For example, if the user is in an urban area, the notification unit can initiate the process of calling the nearest ambulance. If the user is in a suburban area, the notification unit can also initiate the process of calling the ambulance that can arrive most quickly. Furthermore, if the user is in a mountainous area, the notification unit can select the most appropriate emergency medical service. In this way, the notification unit can select the most appropriate notification method by considering geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input geographical location information into a generating AI and have the generating AI perform the selection of the notification method.

[0097] The reporting unit can analyze a user's social media activity and optimize the content of the report. For example, the reporting unit can analyze a user's social media activity and optimize the content of the report. For example, the reporting unit can refer to location information posted by the user on social media and optimize the content of the report. The reporting unit can also select the most appropriate content of the report from the user's social media activity. Furthermore, the reporting unit can analyze social media activity and optimize the content of the report. This allows for the selection of the most appropriate content of the report by analyzing social media activity. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input social media activity data into a generating AI and have the generating AI perform the optimization of the report content.

[0098] The heart rate detection unit can estimate the user's emotions and adjust the accuracy of heart rate detection based on the estimated emotions. For example, if the user is tense, the heart rate detection unit's AI can more closely monitor heart rate fluctuations and detect abnormalities early. Also, if the user is relaxed, the heart rate detection unit's AI can determine that heart rate fluctuations are within the normal range, reducing false detections. Furthermore, if the user is excited, the heart rate detection unit's AI can detect a sudden increase in heart rate and evaluate it as abnormal. In this way, false detections can be reduced by adjusting the accuracy of heart rate detection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the heart rate detection unit may be performed using AI, for example, or without AI. For example, the heart rate detection unit can input user emotion data into a generating AI and have the generating AI adjust the accuracy of heart rate detection.

[0099] The heart rate detection unit can detect abnormalities early by referring to past heart rate data. For example, the heart rate detection unit can refer to the user's past heart rate data and detect abnormal patterns early. The heart rate detection unit can also refer to the user's past medical records and evaluate heart rate abnormalities. Furthermore, the heart rate detection unit can refer to the user's past exercise history and detect abnormalities by considering fluctuations in heart rate after exercise. In this way, abnormalities can be detected early by referring to past heart rate data. Some or all of the above processing in the heart rate detection unit may be performed using AI, for example, or without AI. For example, the heart rate detection unit can input past heart rate data into a generating AI and have the generating AI perform early detection of abnormalities.

[0100] The heart rate detection unit can optimize its detection algorithm by considering the user's current activity level. For example, if the user is exercising, the heart rate detection unit can detect abnormalities by considering a sudden increase in heart rate. Furthermore, if the user is resting, the heart rate detection unit can detect abnormal decreases in heart rate and evaluate the abnormality. Additionally, if the user is driving, the heart rate detection unit can monitor heart rate fluctuations in real time and detect abnormalities early. This allows the detection algorithm to be optimized by considering the current activity level. Some or all of the above processing in the heart rate detection unit may be performed using AI, for example, or without AI. For example, the heart rate detection unit can input current activity data into a generating AI and have the generating AI optimize the detection algorithm.

[0101] The heart rate detection unit can estimate the user's emotions and adjust the method of notifying the user of their heart rate based on the estimated emotions. For example, if the user is tense, the heart rate detection unit can notify the user of an abnormal heart rate in a calm voice. If the user is relaxed, the heart rate detection unit can also provide a notification with a detailed explanation. Furthermore, if the user is in a hurry, the heart rate detection unit can provide a concise and quick notification. This allows for appropriate notifications by adjusting the notification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the heart rate detection unit may be performed using AI, for example, or without AI. For example, the heart rate detection unit can input user emotion data into a generating AI and have the generating AI adjust the notification method.

[0102] The heart rate detection unit can evaluate abnormalities by taking into account the user's geographical location information. For example, if the user is at high altitude, the heart rate detection unit can evaluate heart rate abnormalities by taking into account the decrease in oxygen concentration. Also, if the user is in an urban area, the heart rate detection unit can evaluate heart rate abnormalities by taking into account stress factors. Furthermore, if the user is at home, the heart rate detection unit can evaluate heart rate abnormalities by taking into account the relaxed environment. In this way, abnormalities can be accurately evaluated by taking into account geographical location information. Some or all of the above processing in the heart rate detection unit may be performed using AI, for example, or without using AI. For example, the heart rate detection unit can input geographical location information into a generating AI and have the generating AI perform the abnormality evaluation.

[0103] The heart rate detection unit can analyze the user's social media activity and improve detection accuracy by considering the stress level. For example, if the user frequently posts stressful content, the heart rate detection unit can closely monitor heart rate fluctuations and detect abnormalities early. Furthermore, if the user posts relaxed content, the heart rate detection unit can determine that heart rate fluctuations are within the normal range, reducing false positives. Additionally, if the user posts excited content, the heart rate detection unit can evaluate abnormalities by considering the rapid increase in heart rate. This allows for improved detection accuracy by analyzing social media activity and considering stress levels. Some or all of the above processing in the heart rate detection unit may be performed using AI, for example, or without AI. For example, the heart rate detection unit can input social media activity data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The detection unit can detect the user's body temperature and identify abnormalities. For example, if the user's body temperature rises rapidly, the detection unit can determine that the risk of a seizure is high and issue an early warning. The detection unit can also monitor temperature fluctuations in real time and detect abnormalities. Furthermore, the detection unit can analyze body temperature data in combination with other health data to detect seizure signs more accurately. This allows for early detection of seizure risk by detecting abnormal body temperature.

[0106] The control unit can analyze the user's driving style and select the optimal stopping method. For example, if the user frequently uses sudden braking, the control unit will select a more cautious stopping method. Furthermore, if the user frequently uses highways, the control unit can select a safe stopping method for highways. Additionally, if the user primarily drives in urban areas, the control unit can select the optimal stopping method for urban areas. This allows the system to select the optimal stopping method based on the user's driving style.

[0107] The location tracking unit can improve the accuracy of determining the current location by considering surrounding weather information. For example, if the GPS signal weakens during rainy weather, the location tracking unit can improve its accuracy by referring to other data sources. It can also improve its accuracy by referring to terrain information when it is snowing. Furthermore, if there are strong winds, the location tracking unit can optimize its accuracy by considering wind speed data. In this way, the accuracy of location tracking can be improved by considering weather information.

[0108] The reporting system can select the most appropriate reporting content by referring to the user's medical history. For example, if the user has previously experienced a heart attack, the reporting system will select reporting content that includes detailed medical information. It can also select reporting content that includes allergy information if the user has allergies. Furthermore, if the reporting system is taking specific medications, it can select reporting content that includes information about those medications. This allows the system to select the most appropriate reporting content by referring to the user's medical history.

[0109] The detection unit can estimate the user's emotions and detect abnormalities in heart rate based on those emotions. For example, if the user is stressed, the detection unit will more closely monitor heart rate fluctuations and detect abnormalities early. Furthermore, if the user is relaxed, the detection unit can determine that heart rate fluctuations are within the normal range, reducing false positives. Additionally, if the user is excited, the detection unit can detect a sudden increase in heart rate and evaluate the abnormality. This allows for the detection of heart rate abnormalities based on the user's emotions, thereby reducing false positives.

[0110] The control unit can estimate the user's emotions and adjust the car's stopping method based on those emotions. For example, if the user is tense, the AI ​​will stop the car slowly and safely. If the user is relaxed, the AI ​​can quickly select a stopping position. Furthermore, if the user is in a hurry, the AI ​​can select the method to stop safely in the shortest possible time. This allows for safe stopping by adjusting the stopping method based on the user's emotions.

[0111] The location tracking unit can estimate the user's emotions and adjust the accuracy of location tracking based on those emotions. For example, if the user is stressed, the AI ​​will refer to multiple data sources to improve location tracking accuracy. Conversely, if the user is relaxed, the AI ​​can perform location tracking with normal accuracy. Furthermore, if the user is in a hurry, the AI ​​can select the optimal data source for rapid location tracking. This allows for accurate location tracking by adjusting accuracy based on the user's emotions.

[0112] The reporting system can estimate the user's emotions and adjust the reporting method based on those emotions. For example, if the user is nervous, the reporting system will use a calm voice to initiate the reporting process. If the user is relaxed, the reporting system can also provide a more detailed explanation. Furthermore, if the user is in a hurry, the reporting system can provide a concise and quick reporting process. By adjusting the reporting method based on the user's emotions, appropriate reporting becomes possible.

[0113] The heart rate detection unit can estimate the user's emotions and adjust the notification method based on those emotions. For example, if the user is stressed, the heart rate detection unit will notify the user of an abnormal heart rate in a calm voice. If the user is relaxed, the heart rate detection unit can also provide a notification with a detailed explanation. Furthermore, if the user is in a hurry, the heart rate detection unit can provide a concise and quick notification. By adjusting the notification method based on the user's emotions, appropriate notifications can be provided.

[0114] The detection unit can detect signs of seizures early by referring to the user's past exercise data. For example, the detection unit can refer to the user's past exercise data and detect abnormal patterns early. It can also refer to the user's past exercise history and detect signs of seizures by considering fluctuations in heart rate after exercise. Furthermore, the detection unit can analyze the user's past exercise data and issue a warning if the risk of seizures is high. This allows for the early detection of seizure signs by referring to past exercise data.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The detection unit detects a heart attack. The detection unit can, for example, use AI to detect abnormal heart rates or arrhythmias. For example, it can detect a sudden increase in heart rate or arrhythmias, enabling early detection of signs of a heart attack. It can also monitor heart rate fluctuations in real time and detect abnormalities. Step 2: The control unit remotely operates the vehicle based on the heart attack detected by the detection unit. The control unit can, for example, stop the vehicle in conjunction with a remote control system. The vehicle can be remotely operated via wireless communication or the internet and parked in a safe location. Step 3: The location identification unit determines the current location of the parked vehicle controlled by the operation unit. The location identification unit can determine the current location, for example, by using GPS functionality. It determines the vehicle's current location using GPS data or geographic coordinates. Step 4: The reporting unit calls an ambulance based on the current location identified by the location identification unit. The reporting unit can call an ambulance using, for example, an emergency call system. The ambulance can be called via telephone or the internet.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the detection unit, operation unit, location identification unit, and notification unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the detection unit uses the AI ​​of the smart device 14 to detect abnormal heart rate or arrhythmia. The operation unit uses the identification processing unit 290 of the data processing unit 12 to remotely control the car and park it in a safe place. The location identification unit uses the GPS function of the smart device 14 to determine the current location. The notification unit uses the identification processing unit 290 of the data processing unit 12 to call an ambulance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the detection unit, operation unit, location identification unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit uses the AI ​​of the smart glasses 214 to detect abnormal heart rates or arrhythmias. The operation unit uses the identification processing unit 290 of the data processing unit 12 to remotely control the car and park it in a safe place. The location identification unit uses the GPS function of the smart glasses 214 to determine the current location. The notification unit uses the identification processing unit 290 of the data processing unit 12 to call an ambulance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the detection unit, operation unit, location identification unit, and notification unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the detection unit uses the AI ​​of the headset terminal 314 to detect abnormal heart rate or arrhythmia. The operation unit uses the identification processing unit 290 of the data processing unit 12 to remotely control the vehicle and park it in a safe place. The location identification unit uses the GPS function of the headset terminal 314 to determine the current location. The notification unit uses the identification processing unit 290 of the data processing unit 12 to call an ambulance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the detection unit, operation unit, location identification unit, and notification unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the detection unit uses the AI ​​of the robot 414 to detect abnormal heart rates or arrhythmias. The operation unit uses the identification processing unit 290 of the data processing unit 12 to remotely control the vehicle and park it in a safe place. The location identification unit uses the GPS function of the robot 414 to determine the current location. The notification unit uses the identification processing unit 290 of the data processing unit 12 to call an ambulance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A detection unit for detecting a heart attack, An operating unit that remotely controls the car based on the heart attack detected by the detection unit, A location identification unit that identifies the current location of a vehicle that has been stopped by the aforementioned operating unit, The system includes a notification unit that calls an ambulance based on the current location identified by the location identification unit. A system characterized by the following features. (Note 2) It is equipped with a heart rate detection unit that detects abnormal heart rates. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned operating unit is The car is stopped in conjunction with the remote control system. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned position identification unit is Use GPS functionality to determine your current location. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting unit, The procedure for calling an ambulance is based on your current location. The system described in Appendix 1, characterized by the features described herein. (Note 6) The detection unit is The system estimates the user's emotions and adjusts the accuracy of heart attack detection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit is By referencing the user's past health data, early signs of seizures can be detected. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit is Adjust the detection algorithm based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit is It estimates the user's emotions and adjusts the notification method of the detection results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit is The risk of seizures is assessed by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is We analyze users' social media activity and improve detection accuracy by taking stress levels into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned operating unit is The system estimates the user's emotions and adjusts the car's stopping method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned operating unit is The system selects a stopping position based on the car's current speed and traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned operating unit is Safety is ensured by referring to information from other sensors inside the vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned operating unit is The system estimates the user's emotions and adjusts the notification method after stopping based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned operating unit is The optimal parking position is selected considering the vehicle's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned operating unit is The system selects the optimal parking method by referring to the car's past driving history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned position identification unit is It estimates the user's emotions and adjusts the accuracy of location determination based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned position identification unit is By referencing past location data, we can improve the accuracy of determining the current location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned position identification unit is Improve accuracy based on surrounding geographical information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned position identification unit is The system estimates the user's emotions and adjusts the notification method for location tracking results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned position identification unit is Improve identification accuracy by taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the reporting method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, Select the most appropriate reporting method by referring to past reporting history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, Optimize the notification method considering the current communication status. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the content of the report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting unit, The optimal notification method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reporting unit, Analyze users' social media activity and optimize reporting content. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned heart rate detection unit is It estimates the user's emotions and adjusts the accuracy of heart rate detection based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned heart rate detection unit is By referring to past heart rate data, abnormalities can be detected early. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned heart rate detection unit is The detection algorithm is optimized by considering the user's current activity status. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned heart rate detection unit is It estimates the user's emotions and adjusts the heart rate notification method based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned heart rate detection unit is Anomalies are evaluated considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned heart rate detection unit is We analyze users' social media activity and improve detection accuracy by taking stress levels into consideration. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A heart rate sensor that acquires the user's heart rate data, An emotion estimation unit that estimates the user's emotions based on the user's voice data acquired by the microphone, A detection unit for detecting a heart attack based on heart rate data acquired by the heart rate sensor, An operating unit that remotely controls the car based on the heart attack detected by the detection unit, A location identification unit that identifies the current location of a vehicle that has been stopped by the aforementioned operating unit, It comprises a notification unit that calls an ambulance based on the current location identified by the aforementioned location identification unit, The detection unit adjusts the accuracy of detecting a heart attack by adjusting the heart rate threshold for detecting a heart attack to a stricter value than usual when the emotion estimation unit determines that the user's emotion is in a stressful state. A system characterized by the following features.

2. It is equipped with a heart rate detection unit that detects abnormal heart rates. The system according to feature 1.

3. The aforementioned operating unit is The car is stopped in conjunction with the remote control system. The system according to feature 1.

4. The aforementioned position identification unit is Use GPS functionality to determine your current location. The system according to feature 1.

5. The aforementioned reporting unit, The procedure for calling an ambulance is based on your current location. The system according to feature 1.

6. The detection unit is When the emotion estimation unit determines that the user's emotion is relaxed, the heart rate threshold for detecting a heart attack is adjusted to a more relaxed value than usual to reduce false detections. The system according to feature 1.

7. The detection unit is By referencing the user's past health data, early signs of seizures can be detected. The system according to feature 1.

8. The detection unit is Adjust the detection algorithm based on the user's current activity. The system according to feature 1.

9. The system further includes a notification unit that notifies the user of the detection result from the detection unit on the display of the smart device the user possesses. The notification unit adjusts the notification method of the detection result based on the user's emotions estimated by the emotion estimation unit. The system according to feature 1.

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