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 technology]

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult to respond quickly and safely when a heart attack occurs while driving.

[0005] The system according to the embodiment aims to respond quickly and safely when a heart attack occurs while driving. [Means for solving the problem]

[0006] The system according to the 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 based on 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 based on the current location identified by the location identification unit. Effect of the Invention

[0007] The system of the embodiment allows for a quick and safe response in the event of a heart attack occurring while driving. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) In the system according to the embodiment of the present invention, when a heart attack occurs while driving, the AI ​​detects the attack by bringing the smartphone close to the heart and stops the car in a safe place in cooperation with the remote control system. This system is designed to prevent accidents. In addition, the current location can be detected by the GPS function, so an ambulance can be called quickly. AI can respond to situations where every second counts. For example, when a heart attack occurs while driving, the user brings the smartphone close to the heart. The AI ​​installed in the smartphone detects the heart attack and remotely controls the car in cooperation with the remote control system. The car stops in a safe place to prevent an accident. Next, the current location can be detected by the GPS function 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 abnormality or arrhythmia is detected in the heart rate, the car is immediately stopped and the procedure for calling an ambulance is performed. This system prevents accidents caused by heart attacks while driving and enables rapid medical response. The user can drive with peace of mind because the AI ​​automatically responds by simply bringing the smartphone close to the heart. This allows the system to prevent accidents caused by heart attacks while driving and provide rapid medical response.

[0029] The system according to the embodiment includes a detection unit, an operation unit, a position identification unit, and a reporting unit. The detection unit detects a heart attack. The detection unit can detect abnormalities in heart rate and arrhythmia using, for example, AI. For example, the detection unit can detect a sudden increase in heart rate and arrhythmia and detect a sign of a heart attack early. The detection unit can also monitor the fluctuation of the heart rate in real time and detect an abnormality. The operation unit remotely controls the car based on the heart attack detected by the detection unit. The operation unit can, for example, stop the car in cooperation with a remote operation system. For example, the operation unit remotely controls the car using wireless communication and stops the car in a safe place. The operation unit can also remotely control the car via the Internet. The position identification unit identifies the current location of the car stopped by the operation unit. The position identification unit can identify the current location using, for example, a GPS function. For example, the position identification unit identifies the current location of the car using GPS data. The position identification unit can also identify the current location using geographical coordinates. The reporting unit calls an ambulance based on the current location identified by the location identifying unit. The reporting unit can call an ambulance using, for example, an emergency reporting system. For example, the reporting unit can call an ambulance using a telephone report. The reporting unit can also call an ambulance via the Internet. In this way, the system according to the embodiment can detect a heart attack, safely stop the vehicle, and quickly call an ambulance.

[0030] The detection unit detects a heart attack. The detection unit can detect abnormalities in heart rate and arrhythmia, for example, using AI. Specifically, the detection unit analyzes heart rate data acquired from a heart rate sensor or a wearable device in real time. The AI ​​has an algorithm that distinguishes between 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 arrhythmia, the AI ​​analyzes fluctuations in time series data and detects 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 rapid response. Furthermore, the detection unit has a function of issuing an alert when an abnormality is detected. For example, a notification is sent to the user's smartphone or the vehicle's infotainment system to notify the user of the abnormality. In addition, the detection unit can automatically send information to the operation unit or the reporting unit when an abnormality is detected, and prompt a rapid response. This allows the detection unit to support early detection of a heart attack and rapid response, thereby ensuring the safety of the user.

[0031] The operation unit remotely controls the car based on the heart attack detected by the detection unit. Specifically, the operation unit can work with a remote operation system to stop the car. For example, the operation unit remotely controls the car using wireless communication to stop the car in a safe place. The operation unit can work with a vehicle control system to stop the engine, apply the brakes, turn on the hazard lights, and the like. This allows the vehicle to be safely stopped even if the driver has a heart attack. The operation unit can also remotely control the car via the Internet. For example, the operation unit can access the vehicle control system via a cloud server and operate the vehicle from a remote location. This allows the operation unit to quickly and safely stop the vehicle when the driver has a heart attack. Furthermore, the operation unit has a function of monitoring the current location of the vehicle and the surrounding situation in real time and selecting the optimal stopping position. For example, it can analyze GPS data and camera images to identify a safe stopping position. This allows the operation unit to minimize the impact on other traffic participants while ensuring the safety of the driver.

[0032] The position identification unit identifies the current location of the vehicle stopped by the operation unit. Specifically, the position identification unit can use a GPS function to identify the current location. For example, the position identification unit analyzes data acquired from a GPS module mounted on the vehicle to identify the exact location of the vehicle. The GPS data includes geographical coordinates such as latitude, longitude, and altitude, which allows the current location of the vehicle to be grasped in detail. The position identification unit can also identify the current location using the geographical coordinates. For example, the position identification unit can work with a map database to analyze which point a specific coordinate corresponds to. This allows the position identification unit to quickly and accurately identify the current location of the vehicle and provide information to the reporting unit. Furthermore, the position identification unit can also record the movement history of the vehicle and refer to past position data. This allows the position identification unit to grasp the movement pattern of the vehicle and the history of the stopping position, and use it to respond to an emergency. For example, it is possible to provide reference information for selecting an appropriate stopping position in an emergency based on past stopping position data. This allows the position identification unit to accurately identify the current location of the vehicle and support a quick response.

[0033] The reporting unit calls an ambulance based on the current location identified by the location identification unit. Specifically, the reporting unit can call an ambulance using an emergency reporting system. For example, the reporting unit calls an ambulance using a telephone report. The reporting unit can automatically transmit the current location information of the vehicle to the emergency reporting center to encourage a quick response. The reporting unit can also call an ambulance via the Internet. For example, the reporting unit can access the emergency reporting system via the Internet and transmit the current location information of the vehicle. This allows the reporting unit to call an ambulance quickly and reliably. Furthermore, the reporting unit has a function of automatically generating the report content. For example, the report content can be automatically generated using AI and transmitted to the emergency reporting center. This allows the reporting unit to quickly and accurately create the report content and support emergency response. In addition, the reporting unit has a follow-up function after the report. For example, the expected arrival time of the ambulance and the current location information can be monitored in real time and notified to the user. This allows the reporting unit to reduce anxiety while waiting for the arrival of the ambulance and support a quick response.

[0034] The heart rate detection unit can detect abnormalities in the heart rate. The heart rate detection unit can detect abnormalities in the heart rate, for example, using AI. For example, the heart rate detection unit sets an upper limit and a lower limit of the heart rate, and detects abnormalities when the heart rate exceeds the upper limit. 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 the fluctuation of the heart rate in real time and detect abnormalities. As a result, early detection of a heart attack is possible by detecting abnormalities in the heart rate. A part or all of the above-mentioned processing in the heart rate detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the heart rate detection unit can input heart rate data to the generation AI and cause the generation AI to detect abnormalities.

[0035] The operation unit can stop the car in cooperation with the remote operation system. The operation unit can stop the car in cooperation with the remote operation system, for example. For example, the operation unit remotely controls the car using wireless communication to stop the car in a safe place. The operation unit can also remotely control the car via the Internet. For example, the operation unit executes a control algorithm for stopping the car using the remote operation system. As a result, the car can be stopped safely by working with the remote operation system. A part or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the control algorithm of the remote operation system to the generation AI and cause the generation AI to stop the car.

[0036] The position identification unit can identify the current location by using a GPS function. The position identification unit can identify the current location by using, for example, a GPS function. For example, the position identification unit identifies the current location of the car by using GPS data. The position identification unit can also identify the current location by using geographic coordinates. For example, the position identification unit specifies a satellite system to be used to improve the accuracy of the position identification. In this way, the current location can be accurately identified by using the GPS function. A part or all of the above-mentioned processing in the position identification unit may be performed by using, for example, an AI, or may be performed without using an AI. For example, the position identification unit can input GPS data to the generation AI and cause the generation AI to identify the current location.

[0037] The reporting unit can perform a procedure to call an ambulance based on the current location. The reporting unit can perform a procedure to call an ambulance based on the current location, for example. For example, the reporting unit calls an ambulance using an emergency reporting system. The reporting unit can also call an ambulance using a telephone report. For example, the reporting unit specifies a report destination and transmits the report contents. This makes it possible to quickly call an ambulance based on the current location. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reporting unit can input the report contents to a generation AI and cause the generation AI to execute the call for an ambulance.

[0038] The detection unit can detect signs of a seizure early by referring to the user's past health data. The detection unit can detect signs of a seizure early by referring to the user's past health data, for example. For example, the detection unit can detect abnormal patterns early by referring to the user's past heart rate data. The detection unit can also issue a warning when the risk of a heart attack is high by referring to the user's past medical records. Furthermore, the detection unit can also detect signs of a seizure early by referring to the user's past exercise history and taking into account the fluctuation of the heart rate after exercise. In this way, signs of a seizure can be detected early by referring to the past health data. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input past health data to the generation AI and cause the generation AI to execute early detection of signs of a seizure.

[0039] The detection unit can adjust the detection algorithm based on the user's current activity status. The detection unit can optimize the detection algorithm, for example, by taking into account the user's current activity status. For example, when the user is exercising, the detection unit detects a sign of a seizure by taking into account a sudden increase in heart rate. In addition, when the user is resting, the detection unit can detect an abnormal decrease in heart rate and evaluate the risk of a seizure. Furthermore, when the user is driving, the detection unit can monitor the heart rate fluctuation in real time and detect the sign of a seizure early. This allows the detection algorithm to be optimized by taking into account the current activity status. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the current activity status data to the generation AI and cause the generation AI to execute optimization of the detection algorithm.

[0040] The detection unit can evaluate the risk of a seizure taking into account the geographical location information of the user. The detection unit can evaluate the risk of a seizure, for example, taking into account the geographical location information of the user. For example, when the user is at high altitude, the detection unit evaluates the risk of a seizure taking into account a decrease in oxygen concentration. In addition, when the user is in an urban area, the detection unit can also evaluate the risk of a seizure taking into account stress factors. Furthermore, when the user is at home, the detection unit can also evaluate the risk of a seizure taking into account a relaxing environment. This allows the risk of a seizure to be accurately evaluated by taking into account the geographical location information. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input geographical location information to the generation AI and cause the generation AI to perform a seizure risk assessment.

[0041] The detection unit can analyze the social media activity of the user and improve the detection accuracy by taking into account the stress level. The detection unit can, for example, analyze the social media activity of the user and improve the detection accuracy by taking into account the stress level. For example, the detection unit closely monitors the heart rate fluctuations when the user frequently posts stressful things, and detects the signs of a heart attack early. In addition, the detection unit can determine that the heart rate fluctuations are within a normal range when the user posts relaxing things, and reduce false positives. Furthermore, the detection unit can evaluate the risk of a heart attack by taking into account a sudden increase in the heart rate when the user posts excited things. This makes it possible to improve the detection accuracy by analyzing the social media activity and taking into account the stress level. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input social media activity data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy.

[0042] The operation unit can select a stopping position based on the current speed and traffic conditions of the car. The operation unit can select an optimal stopping position, for example, taking into account the current speed and traffic conditions of the car. For example, when the car is traveling on a highway, the operation unit causes the AI ​​to select the nearest service area as the stopping position. In addition, when the car is traveling in an urban area, the operation unit can also cause the AI ​​to select a safe road shoulder as the stopping position. Furthermore, when the car is in a traffic jam, the operation unit can also cause the AI ​​to select the safest stopping position. In this way, the optimal stopping position can be selected by taking into account the speed and traffic conditions. Part or all of the above-mentioned processing in the operation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the operation unit can input speed and traffic condition data to the generation AI and cause the generation AI to select a stopping position.

[0043] The operation unit can ensure safety by referring to other sensor information in the vehicle. The operation unit can ensure safety by referring to other sensor information in the vehicle, for example. For example, if the seat belt is not fastened, the operation unit causes the AI ​​to issue a warning before stopping the vehicle. The operation unit can also select an appropriate stopping position by referring to temperature sensor information in the vehicle. Furthermore, the operation unit can also select a stopping method for ensuring the safety of the occupants by referring to camera information in the vehicle. In this way, safety can be ensured by referring to other sensor information. A part or all of the above-mentioned processing in the operation unit may be performed, for example, using AI, or may be performed without using AI. For example, the operation unit can input other sensor information to the generation AI and cause the generation AI to ensure safety.

[0044] The operation unit can select the optimal stopping position in consideration of the geographical position information of the vehicle. The operation unit can select the optimal stopping position in consideration of, for example, the geographical position information of the vehicle. For example, when the vehicle is traveling in a mountainous area, the operation unit causes the AI ​​to select the nearest wide space as the stopping position. In addition, when the vehicle is traveling in an urban area, the operation unit can also cause the AI ​​to select a safe parking lot as the stopping position. Furthermore, when the vehicle is traveling in a suburban area, the operation unit can also cause the AI ​​to select a safe road shoulder as the stopping position. In this way, the optimal stopping position can be selected by taking the geographical position information into consideration. Part or all of the above-mentioned processing in the operation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the operation unit can input the geographical position information to the generation AI and cause the generation AI to select the stopping position.

[0045] The operation unit can select the optimal stopping method by referring to the past driving history of the car. The operation unit can select the optimal stopping method by referring to the past driving history of the car, for example. For example, the operation unit selects the optimal stopping position based on the places where the car has stopped in the past. The operation unit can also select a stopping method that avoids congestion from the past driving history of the car. Furthermore, the operation unit can analyze the past driving history of the car and select the safest stopping method. In this way, the optimal stopping method can be selected by referring to the past driving history. A part or all of the above-mentioned processing in the operation unit may be performed, for example, using AI, or may be performed without using AI. For example, the operation unit can input past driving history data to the generation AI and cause the generation AI to select a stopping method.

[0046] The position identification unit can improve the accuracy of identifying the current location by referring to past position data. The position identification unit can improve the accuracy of identifying the current location by referring to past position data, for example. For example, the position identification unit improves the accuracy of identifying the current location by referring to the user's past position data. The position identification unit can also improve the accuracy of identifying the current location at a specific location from the past position data. Furthermore, the position identification unit can analyze the past position data and optimize the accuracy of identifying the current location. As a result, the accuracy of identifying the current location can be improved by referring to the past position data. A part or all of the above-mentioned processing in the position identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the position identification unit can input the past position data to the generation AI and cause the generation AI to improve the accuracy of identifying the current location.

[0047] The position identification unit can improve the accuracy based on surrounding geographic information. The position identification unit can improve the accuracy, for example, by taking into account surrounding geographic information. For example, the position identification unit refers to surrounding building information and improves the accuracy of identifying the current location. The position identification unit can also refer to road layout information and improve the accuracy of identifying the current location. Furthermore, the position identification unit can analyze geographic information and optimize the accuracy of identifying the current location. Thereby, the accuracy of identifying the location can be improved by taking into account surrounding geographic information. A part or all of the above-mentioned processing in the position identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the position identification unit can input surrounding geographic information to the generation AI and cause the generation AI to execute the improvement of the accuracy.

[0048] The position identification unit can improve the identification accuracy by taking into account the geographical location information of the user. The position identification unit can improve the identification accuracy by taking into account the geographical location information of the user, for example. For example, when the user is in an urban area, the position identification unit can improve the identification accuracy by referring to surrounding building information. In addition, when the user is in the suburbs, the position identification unit can also improve the identification accuracy by referring to road layout information. Furthermore, when the user is in a mountainous area, the position identification unit can also improve the identification accuracy by referring to topographical information. In this way, the identification accuracy can be improved by taking into account the geographical location information. Part or all of the above-mentioned processing in the position identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the position identification unit can input geographical location information to the generation AI and cause the generation AI to execute the improvement of the identification accuracy.

[0049] The reporting unit can select the optimal reporting means by referring to the past reporting history. The reporting unit can select the optimal reporting means by referring to the past reporting history, for example. For example, the reporting unit selects the optimal reporting means based on the reporting means used by the user in the past. The reporting unit can also select a quick reporting means from the past reporting history. Furthermore, the reporting unit can analyze the reporting history and select the most effective reporting means. As a result, the optimal reporting means can be selected by referring to the past reporting history. A part or all of the above-mentioned processing in the reporting unit may be performed, for example, using AI, or may be performed without using AI. For example, the reporting unit can input past reporting history data to the generation AI and cause the generation AI to select the reporting means.

[0050] The reporting unit can optimize the reporting means in consideration of the current communication situation. The reporting unit can optimize the reporting means in consideration of the current communication situation, for example. For example, when the radio wave strength is weak, the reporting unit selects the most stable communication means by the AI. Also, when the communication situation is good, the reporting unit can select the quickest reporting means by the AI. Furthermore, the reporting unit can monitor the communication situation in real time and select the optimal reporting means. In this way, the optimal reporting means can be selected by considering the communication situation. A part or all of the above-mentioned processing in the reporting unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reporting unit can input communication situation data to the generation AI and cause the generation AI to execute optimization of the reporting means.

[0051] The reporting unit can select the optimal reporting means in consideration of the geographical location information of the user. The reporting unit can select the optimal reporting means in consideration of, for example, the geographical location information of the user. For example, when the user is in an urban area, the reporting unit performs a procedure to call the nearest ambulance. In addition, when the user is in the suburbs, the reporting unit can also perform a procedure to call an ambulance that can arrive most quickly. Furthermore, when the user is in a mountainous area, the reporting unit can also select the most appropriate emergency means. As a result, the optimal reporting means can be selected by considering the geographical location information. Part or all of the above-mentioned processing in the reporting unit may be performed, for example, using AI, or may be performed without using AI. For example, the reporting unit can input geographical location information to the generation AI and cause the generation AI to select the reporting means.

[0052] The reporting unit can analyze the social media activity of the user and optimize the report content. The reporting unit can, for example, analyze the social media activity of the user and optimize the report content. For example, the reporting unit refers to location information posted by the user on social media and optimizes the report content. The reporting unit can also select optimal report content from the social media activity of the user. Furthermore, the reporting unit can also analyze social media activity and optimize the report content. As a result, optimal report content can be selected by analyzing social media activity. A part or all of the above-mentioned processing in the reporting unit may be performed, for example, using AI or may be performed without using AI. For example, the reporting unit can input social media activity data to a generation AI and cause the generation AI to optimize the report content.

[0053] The heart rate detection unit can detect abnormalities early by referring to past heart rate data. The heart rate detection unit can detect abnormalities early by referring to past heart rate data, for example. For example, the heart rate detection unit can detect abnormal patterns early by referring to the user's past heart rate data. The heart rate detection unit can also evaluate abnormalities in the heart rate by referring to the user's past medical records. Furthermore, the heart rate detection unit can also detect abnormalities by referring to the user's past exercise history and taking into account fluctuations in the heart rate after exercise. Thereby, abnormalities can be detected early by referring to the past heart rate data. A part or all of the above-mentioned processing in the heart rate detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the heart rate detection unit can input past heart rate data to the generation AI and cause the generation AI to execute early detection of abnormalities.

[0054] The heart rate detection unit can optimize the detection algorithm in consideration of the current activity status of the user. The heart rate detection unit can optimize the detection algorithm in consideration of the current activity status of the user, for example. For example, when the user is exercising, the heart rate detection unit detects an abnormality in consideration of a sudden increase in the heart rate. In addition, when the user is resting, the heart rate detection unit can also detect an abnormal decrease in the heart rate and evaluate the abnormality. Furthermore, when the user is driving, the heart rate detection unit can also monitor the fluctuation of the heart rate in real time and detect an abnormality early. This makes it possible to optimize the detection algorithm by considering the current activity status. Part or all of the above-mentioned processing in the heart rate detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the heart rate detection unit can input current activity status data to the generation AI and cause the generation AI to execute optimization of the detection algorithm.

[0055] The heart rate detection unit can evaluate the abnormality taking into account the geographical location information of the user. The heart rate detection unit can evaluate the abnormality, for example, taking into account the geographical location information of the user. For example, when the user is at high altitude, the heart rate detection unit evaluates the abnormality of the heart rate taking into account a decrease in oxygen concentration. In addition, when the user is in an urban area, the heart rate detection unit can also evaluate the abnormality of the heart rate taking into account stress factors. Furthermore, when the user is at home, the heart rate detection unit can also evaluate the abnormality of the heart rate taking into account a relaxed environment. Thereby, by taking into account the geographical location information, the abnormality can be accurately evaluated. Part or all of the above-mentioned processing in the heart rate detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the heart rate detection unit can input the geographical location information to the generation AI and cause the generation AI to execute the evaluation of the abnormality.

[0056] The heart rate detection unit can analyze the social media activity of the user and improve the detection accuracy by taking into account the stress level. The heart rate detection unit can, for example, analyze the social media activity of the user and improve the detection accuracy by taking into account the stress level. For example, when the user frequently posts something that makes him / her feel stressed, the heart rate detection unit closely monitors the fluctuation of the heart rate and detects an abnormality early. In addition, when the user posts something that makes him / her feel relaxed, the heart rate detection unit can determine that the fluctuation of the heart rate is within a normal range and reduce false detection. Furthermore, when the user posts something that makes him / her feel excited, the heart rate detection unit can also evaluate the abnormality by taking into account a sudden increase in the heart rate. This makes it possible to improve the detection accuracy by analyzing the social media activity and taking into account the stress level. A part or all of the above-mentioned processing in the heart rate detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the heart rate detection unit can input social media activity data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy.

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

[0058] The detection unit can detect the user's body temperature and detect abnormalities in the body temperature. For example, if the user's body temperature rises suddenly, the detection unit can determine that the risk of a seizure is increasing and issue an early warning. The detection unit can also monitor body temperature fluctuations in real time and detect abnormalities. Furthermore, the detection unit can combine body temperature data with other health data for analysis to detect more accurate signs of a seizure. In this way, by detecting abnormalities in body temperature, the risk of a seizure can be detected early.

[0059] The operation unit can analyze the driving style of the user and select the optimal stopping method. For example, if the user normally brakes frequently, the operation unit selects a more careful stopping method. In addition, if the user frequently uses expressways, the operation unit can also select a safe stopping method on expressways. Furthermore, if the user mainly drives in urban areas, the operation unit can also select the optimal stopping method in urban areas. In this way, the optimal stopping method can be selected based on the user's driving style.

[0060] The position determining unit can improve the accuracy of determining the current location by taking into account the surrounding weather information. For example, when the GPS signal is weak during rainy weather, the position determining unit can improve the accuracy of determining the location by referring to other data sources. In addition, when it is snowing, the position determining unit can improve the accuracy of determining the location by referring to topographical information. Furthermore, when a strong wind is blowing, the position determining unit can optimize the accuracy of determining the location by taking into account wind speed data. In this way, the accuracy of determining the location can be improved by taking into account the weather information.

[0061] The notification unit can select the most appropriate notification content by referring to the user's medical history. For example, if the user has experienced a heart attack in the past, the notification unit selects notification content including detailed medical information. In addition, if the user has an allergy, the notification unit can also select notification content including allergy information. Furthermore, if the user is taking a specific medication, the notification unit can also select notification content including that information. In this way, the most appropriate notification content can be selected by referring to the user's medical history.

[0062] The detection unit can detect signs of a seizure early by referring to the user's past exercise data. For example, the detection unit can detect abnormal patterns early by referring to the user's past exercise data. The detection unit can also detect signs of a seizure early by referring to the user's past exercise history and taking into account fluctuations in heart rate after exercise. Furthermore, the detection unit can analyze the user's past exercise data and issue a warning if there is a high risk of a seizure. In this way, signs of a seizure can be detected early by referring to past exercise data.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The detection unit detects a heart attack. The detection unit can detect abnormalities in heart rate or arrhythmia, for example, using AI. For example, it can detect a sudden increase in heart rate or arrhythmia and detect early signs of a heart attack. It can also monitor heart rate fluctuations in real time and detect abnormalities. Step 2: The operation unit remotely controls the car based on the heart attack detected by the detection unit. The operation unit can, for example, work with a remote control system to stop the car. The operation unit remotely controls the car via wireless communication or the Internet to stop it in a safe place. Step 3: The position identifying unit identifies the current location of the vehicle that has been stopped by the operation unit. The position identifying unit can identify the current location by using, for example, a GPS function. The current location of the vehicle is identified by 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 an emergency reporting system, for example. The ambulance is called by telephone or via the Internet.

[0065] (Example 2) In the system according to the embodiment of the present invention, when a heart attack occurs while driving, the AI ​​detects the attack by bringing the smartphone close to the heart and stops the car in a safe place in cooperation with the remote control system. This system is designed to prevent accidents. In addition, the current location can be detected by the GPS function, so an ambulance can be called quickly. AI can respond to situations where every second counts. For example, when a heart attack occurs while driving, the user brings the smartphone close to the heart. The AI ​​installed in the smartphone detects the heart attack and remotely controls the car in cooperation with the remote control system. The car stops in a safe place to prevent an accident. Next, the current location can be detected by the GPS function 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 abnormality or arrhythmia is detected in the heart rate, the car is immediately stopped and the procedure for calling an ambulance is performed. This system prevents accidents caused by heart attacks while driving and enables rapid medical response. The user can drive with peace of mind because the AI ​​automatically responds by simply bringing the smartphone close to the heart. This allows the system to prevent accidents caused by heart attacks while driving and provide rapid medical response.

[0066] The system according to the embodiment includes a detection unit, an operation unit, a position identification unit, and a reporting unit. The detection unit detects a heart attack. The detection unit can detect abnormalities in heart rate and arrhythmia using, for example, AI. For example, the detection unit can detect a sudden increase in heart rate and arrhythmia and detect a sign of a heart attack early. The detection unit can also monitor the fluctuation of the heart rate in real time and detect an abnormality. The operation unit remotely controls the car based on the heart attack detected by the detection unit. The operation unit can, for example, stop the car in cooperation with a remote operation system. For example, the operation unit remotely controls the car using wireless communication and stops the car in a safe place. The operation unit can also remotely control the car via the Internet. The position identification unit identifies the current location of the car stopped by the operation unit. The position identification unit can identify the current location using, for example, a GPS function. For example, the position identification unit identifies the current location of the car using GPS data. The position identification unit can also identify the current location using geographical coordinates. The reporting unit calls an ambulance based on the current location identified by the location identifying unit. The reporting unit can call an ambulance using, for example, an emergency reporting system. For example, the reporting unit can call an ambulance using a telephone report. The reporting unit can also call an ambulance via the Internet. In this way, the system according to the embodiment can detect a heart attack, safely stop the vehicle, and quickly call an ambulance.

[0067] The detection unit detects a heart attack. The detection unit can detect abnormalities in heart rate and arrhythmia, for example, using AI. Specifically, the detection unit analyzes heart rate data acquired from a heart rate sensor or a wearable device in real time. The AI ​​has an algorithm that distinguishes between 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 arrhythmia, the AI ​​analyzes fluctuations in time series data and detects 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 rapid response. Furthermore, the detection unit has a function of issuing an alert when an abnormality is detected. For example, a notification is sent to the user's smartphone or the vehicle's infotainment system to notify the user of the abnormality. In addition, the detection unit can automatically send information to the operation unit or the reporting unit when an abnormality is detected, and prompt a rapid response. This allows the detection unit to support early detection of a heart attack and rapid response, thereby ensuring the safety of the user.

[0068] The operation unit remotely controls the car based on the heart attack detected by the detection unit. Specifically, the operation unit can work with a remote operation system to stop the car. For example, the operation unit remotely controls the car using wireless communication to stop the car in a safe place. The operation unit can work with a vehicle control system to stop the engine, apply the brakes, turn on the hazard lights, and the like. This allows the vehicle to be safely stopped even if the driver has a heart attack. The operation unit can also remotely control the car via the Internet. For example, the operation unit can access the vehicle control system via a cloud server and operate the vehicle from a remote location. This allows the operation unit to quickly and safely stop the vehicle when the driver has a heart attack. Furthermore, the operation unit has a function of monitoring the current location of the vehicle and the surrounding situation in real time and selecting the optimal stopping position. For example, it can analyze GPS data and camera images to identify a safe stopping position. This allows the operation unit to minimize the impact on other traffic participants while ensuring the safety of the driver.

[0069] The position identification unit identifies the current location of the vehicle stopped by the operation unit. Specifically, the position identification unit can use a GPS function to identify the current location. For example, the position identification unit analyzes data acquired from a GPS module mounted on the vehicle to identify the exact location of the vehicle. The GPS data includes geographical coordinates such as latitude, longitude, and altitude, which allows the current location of the vehicle to be grasped in detail. The position identification unit can also identify the current location using the geographical coordinates. For example, the position identification unit can work with a map database to analyze which point a specific coordinate corresponds to. This allows the position identification unit to quickly and accurately identify the current location of the vehicle and provide information to the reporting unit. Furthermore, the position identification unit can also record the movement history of the vehicle and refer to past position data. This allows the position identification unit to grasp the movement pattern of the vehicle and the history of the stopping position, and use it to respond to an emergency. For example, it is possible to provide reference information for selecting an appropriate stopping position in an emergency based on past stopping position data. This allows the position identification unit to accurately identify the current location of the vehicle and support a quick response.

[0070] The reporting unit calls an ambulance based on the current location identified by the location identification unit. Specifically, the reporting unit can call an ambulance using an emergency reporting system. For example, the reporting unit calls an ambulance using a telephone report. The reporting unit can automatically transmit the current location information of the vehicle to the emergency reporting center to encourage a quick response. The reporting unit can also call an ambulance via the Internet. For example, the reporting unit can access the emergency reporting system via the Internet and transmit the current location information of the vehicle. This allows the reporting unit to call an ambulance quickly and reliably. Furthermore, the reporting unit has a function of automatically generating the report content. For example, the report content can be automatically generated using AI and transmitted to the emergency reporting center. This allows the reporting unit to quickly and accurately create the report content and support emergency response. In addition, the reporting unit has a follow-up function after the report. For example, the expected arrival time of the ambulance and the current location information can be monitored in real time and notified to the user. This allows the reporting unit to reduce anxiety while waiting for the arrival of the ambulance and support a quick response.

[0071] The heart rate detection unit can detect abnormalities in the heart rate. The heart rate detection unit can detect abnormalities in the heart rate, for example, using AI. For example, the heart rate detection unit sets an upper limit and a lower limit of the heart rate, and detects abnormalities when the heart rate exceeds the upper limit. 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 the fluctuation of the heart rate in real time and detect abnormalities. As a result, early detection of a heart attack is possible by detecting abnormalities in the heart rate. A part or all of the above-mentioned processing in the heart rate detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the heart rate detection unit can input heart rate data to the generation AI and cause the generation AI to detect abnormalities.

[0072] The operation unit can stop the car in cooperation with the remote operation system. The operation unit can stop the car in cooperation with the remote operation system, for example. For example, the operation unit remotely controls the car using wireless communication to stop the car in a safe place. The operation unit can also remotely control the car via the Internet. For example, the operation unit executes a control algorithm for stopping the car using the remote operation system. As a result, the car can be stopped safely by working with the remote operation system. A part or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the control algorithm of the remote operation system to the generation AI and cause the generation AI to stop the car.

[0073] The position identification unit can identify the current location by using a GPS function. The position identification unit can identify the current location by using, for example, a GPS function. For example, the position identification unit identifies the current location of the car by using GPS data. The position identification unit can also identify the current location by using geographic coordinates. For example, the position identification unit specifies a satellite system to be used to improve the accuracy of the position identification. In this way, the current location can be accurately identified by using the GPS function. A part or all of the above-mentioned processing in the position identification unit may be performed by using, for example, an AI, or may be performed without using an AI. For example, the position identification unit can input GPS data to the generation AI and cause the generation AI to identify the current location.

[0074] The reporting unit can perform a procedure to call an ambulance based on the current location. The reporting unit can perform a procedure to call an ambulance based on the current location, for example. For example, the reporting unit calls an ambulance using an emergency reporting system. The reporting unit can also call an ambulance using a telephone report. For example, the reporting unit specifies a report destination and transmits the report contents. This makes it possible to quickly call an ambulance based on the current location. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reporting unit can input the report contents to a generation AI and cause the generation AI to execute the call for an ambulance.

[0075] The detection unit can estimate the user's emotion and adjust the accuracy of heart attack detection based on the estimated user's emotion. The detection unit can, for example, estimate the user's emotion and adjust the accuracy of heart attack detection based on the estimated user's emotion. For example, when the user is stressed, the detection unit monitors the heart rate fluctuation more closely and detects the signs of a heart attack early. In addition, when the user is relaxed, the detection unit can determine that the heart rate fluctuation is within a normal range and reduce false positives. Furthermore, when the user is excited, the detection unit can detect a sudden increase in the heart rate and consider it as a factor that increases the risk of a heart attack. In this way, false positives can be reduced by adjusting the accuracy of heart attack detection based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the accuracy of heart attack detection.

[0076] The detection unit can detect signs of a seizure early by referring to the user's past health data. The detection unit can detect signs of a seizure early by referring to the user's past health data, for example. For example, the detection unit can detect abnormal patterns early by referring to the user's past heart rate data. The detection unit can also issue a warning when the risk of a heart attack is high by referring to the user's past medical records. Furthermore, the detection unit can also detect signs of a seizure early by referring to the user's past exercise history and taking into account the fluctuation of the heart rate after exercise. In this way, signs of a seizure can be detected early by referring to the past health data. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input past health data to the generation AI and cause the generation AI to execute early detection of signs of a seizure.

[0077] The detection unit can adjust the detection algorithm based on the user's current activity status. The detection unit can optimize the detection algorithm, for example, by taking into account the user's current activity status. For example, when the user is exercising, the detection unit detects a sign of a seizure by taking into account a sudden increase in heart rate. In addition, when the user is resting, the detection unit can detect an abnormal decrease in heart rate and evaluate the risk of a seizure. Furthermore, when the user is driving, the detection unit can monitor the heart rate fluctuation in real time and detect the sign of a seizure early. This allows the detection algorithm to be optimized by taking into account the current activity status. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the current activity status data to the generation AI and cause the generation AI to execute optimization of the detection algorithm.

[0078] The detection unit can estimate the user's emotion and adjust the notification method of the detection result based on the estimated user's emotion. The detection unit can, for example, estimate the user's emotion and adjust the notification method of the detection result based on the estimated user's emotion. For example, if the user is nervous, the detection unit notifies the detection result in a calm voice. Also, if the user is relaxed, the detection unit can provide a notification including 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 notification by adjusting the notification method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the notification method.

[0079] The detection unit can evaluate the risk of a seizure taking into account the geographical location information of the user. The detection unit can evaluate the risk of a seizure, for example, taking into account the geographical location information of the user. For example, when the user is at high altitude, the detection unit evaluates the risk of a seizure taking into account a decrease in oxygen concentration. In addition, when the user is in an urban area, the detection unit can also evaluate the risk of a seizure taking into account stress factors. Furthermore, when the user is at home, the detection unit can also evaluate the risk of a seizure taking into account a relaxing environment. This allows the risk of a seizure to be accurately evaluated by taking into account the geographical location information. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input geographical location information to the generation AI and cause the generation AI to perform a seizure risk assessment.

[0080] The detection unit can analyze the social media activity of the user and improve the detection accuracy by taking into account the stress level. The detection unit can, for example, analyze the social media activity of the user and improve the detection accuracy by taking into account the stress level. For example, the detection unit closely monitors the heart rate fluctuations when the user frequently posts stressful things, and detects the signs of a heart attack early. In addition, the detection unit can determine that the heart rate fluctuations are within a normal range when the user posts relaxing things, and reduce false positives. Furthermore, the detection unit can evaluate the risk of a heart attack by taking into account a sudden increase in the heart rate when the user posts excited things. This makes it possible to improve the detection accuracy by analyzing the social media activity and taking into account the stress level. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input social media activity data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy.

[0081] The operation unit can estimate the user's emotion and adjust the stopping method of the car based on the estimated user's emotion. The operation unit can, for example, estimate the user's emotion and adjust the stopping method of the car based on the estimated user's emotion. For example, when the user is nervous, the operation unit allows the AI ​​to stop the car slowly and safely. In addition, when the user is relaxed, the operation unit can also allow the AI ​​to quickly select a stopping position. Furthermore, when the user is in a hurry, the operation unit can also allow the AI ​​to select a method for stopping the car safely in the shortest time. As a result, a safe stop is possible by adjusting the stopping method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the operation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the operation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the stopping method.

[0082] The operation unit can select a stopping position based on the current speed and traffic conditions of the car. The operation unit can select an optimal stopping position, for example, taking into account the current speed and traffic conditions of the car. For example, when the car is traveling on a highway, the operation unit causes the AI ​​to select the nearest service area as the stopping position. In addition, when the car is traveling in an urban area, the operation unit can also cause the AI ​​to select a safe road shoulder as the stopping position. Furthermore, when the car is in a traffic jam, the operation unit can also cause the AI ​​to select the safest stopping position. In this way, the optimal stopping position can be selected by taking into account the speed and traffic conditions. Part or all of the above-mentioned processing in the operation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the operation unit can input speed and traffic condition data to the generation AI and cause the generation AI to select a stopping position.

[0083] The operation unit can ensure safety by referring to other sensor information in the vehicle. The operation unit can ensure safety by referring to other sensor information in the vehicle, for example. For example, if the seat belt is not fastened, the operation unit causes the AI ​​to issue a warning before stopping the vehicle. The operation unit can also select an appropriate stopping position by referring to temperature sensor information in the vehicle. Furthermore, the operation unit can also select a stopping method for ensuring the safety of the occupants by referring to camera information in the vehicle. In this way, safety can be ensured by referring to other sensor information. A part or all of the above-mentioned processing in the operation unit may be performed, for example, using AI, or may be performed without using AI. For example, the operation unit can input other sensor information to the generation AI and cause the generation AI to ensure safety.

[0084] The operation unit can estimate the user's emotion and adjust the notification method after stopping based on the estimated user's emotion. The operation unit can, for example, estimate the user's emotion and adjust the notification method after stopping based on the estimated user's emotion. For example, when the user is nervous, the operation unit notifies the user of the completion of stopping with a calm voice. In addition, when the user is relaxed, the operation unit can also provide a notification including a detailed explanation. Furthermore, when the user is in a hurry, the operation unit can also provide a concise and quick notification. This enables appropriate notification by adjusting the notification method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the operation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the operation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the notification method.

[0085] The operation unit can select the optimal stopping position in consideration of the geographical position information of the vehicle. The operation unit can select the optimal stopping position in consideration of, for example, the geographical position information of the vehicle. For example, when the vehicle is traveling in a mountainous area, the operation unit causes the AI ​​to select the nearest wide space as the stopping position. In addition, when the vehicle is traveling in an urban area, the operation unit can also cause the AI ​​to select a safe parking lot as the stopping position. Furthermore, when the vehicle is traveling in a suburban area, the operation unit can also cause the AI ​​to select a safe road shoulder as the stopping position. In this way, the optimal stopping position can be selected by taking the geographical position information into consideration. Part or all of the above-mentioned processing in the operation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the operation unit can input the geographical position information to the generation AI and cause the generation AI to select the stopping position.

[0086] The operation unit can select the optimal stopping method by referring to the past driving history of the car. The operation unit can select the optimal stopping method by referring to the past driving history of the car, for example. For example, the operation unit selects the optimal stopping position based on the places where the car has stopped in the past. The operation unit can also select a stopping method that avoids congestion from the past driving history of the car. Furthermore, the operation unit can analyze the past driving history of the car and select the safest stopping method. In this way, the optimal stopping method can be selected by referring to the past driving history. A part or all of the above-mentioned processing in the operation unit may be performed, for example, using AI, or may be performed without using AI. For example, the operation unit can input past driving history data to the generation AI and cause the generation AI to select a stopping method.

[0087] The location identification unit can estimate the user's emotion and adjust the accuracy of location identification based on the estimated user's emotion. The location identification unit can, for example, estimate the user's emotion and adjust the accuracy of location identification based on the estimated user's emotion. For example, when the user is nervous, the location identification unit refers to multiple data sources so that the AI ​​can increase the accuracy of location identification. In addition, when the user is relaxed, the location identification unit can also perform location identification with normal accuracy. Furthermore, when the user is in a hurry, the location identification unit can also select an optimal data source so that the AI ​​can quickly perform location identification. As a result, accurate location identification is possible by adjusting the accuracy of location identification based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the location identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the location identification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the accuracy of location identification.

[0088] The position identification unit can improve the accuracy of identifying the current location by referring to past position data. The position identification unit can improve the accuracy of identifying the current location by referring to past position data, for example. For example, the position identification unit improves the accuracy of identifying the current location by referring to the user's past position data. The position identification unit can also improve the accuracy of identifying the current location at a specific location from the past position data. Furthermore, the position identification unit can analyze the past position data and optimize the accuracy of identifying the current location. As a result, the accuracy of identifying the current location can be improved by referring to the past position data. A part or all of the above-mentioned processing in the position identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the position identification unit can input the past position data to the generation AI and cause the generation AI to improve the accuracy of identifying the current location.

[0089] The position identification unit can improve the accuracy based on surrounding geographic information. The position identification unit can improve the accuracy, for example, by taking into account surrounding geographic information. For example, the position identification unit refers to surrounding building information and improves the accuracy of identifying the current location. The position identification unit can also refer to road layout information and improve the accuracy of identifying the current location. Furthermore, the position identification unit can analyze geographic information and optimize the accuracy of identifying the current location. Thereby, the accuracy of identifying the location can be improved by taking into account surrounding geographic information. A part or all of the above-mentioned processing in the position identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the position identification unit can input surrounding geographic information to the generation AI and cause the generation AI to execute the improvement of the accuracy.

[0090] The location identification unit can estimate the user's emotion and adjust the notification method of the location identification result based on the estimated user's emotion. The location identification unit can, for example, estimate the user's emotion and adjust the notification method of the location identification result based on the estimated user's emotion. For example, when the user is nervous, the location identification unit notifies the location identification result in a calm voice. Furthermore, when the user is relaxed, the location identification unit can also provide a notification including a detailed explanation. Furthermore, when the user is in a hurry, the location identification unit can also provide a concise and quick notification. This enables appropriate notification by adjusting the notification method based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the location identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the location identification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the notification method.

[0091] The position identification unit can improve the identification accuracy by taking into account the geographical location information of the user. The position identification unit can improve the identification accuracy by taking into account the geographical location information of the user, for example. For example, when the user is in an urban area, the position identification unit can improve the identification accuracy by referring to surrounding building information. In addition, when the user is in the suburbs, the position identification unit can also improve the identification accuracy by referring to road layout information. Furthermore, when the user is in a mountainous area, the position identification unit can also improve the identification accuracy by referring to topographical information. In this way, the identification accuracy can be improved by taking into account the geographical location information. Part or all of the above-mentioned processing in the position identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the position identification unit can input geographical location information to the generation AI and cause the generation AI to execute the improvement of the identification accuracy.

[0092] The reporting unit can estimate the user's emotion and adjust the reporting method based on the estimated user's emotion. The reporting unit can, for example, estimate the user's emotion and adjust the reporting method based on the estimated user's emotion. For example, when the user is nervous, the reporting unit performs the reporting procedure in a calm voice. Also, when the user is relaxed, the reporting unit can perform the reporting procedure including a detailed explanation. Furthermore, when the user is in a hurry, the reporting unit can perform a concise and quick reporting procedure. This enables appropriate reporting by adjusting the reporting method based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reporting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reporting unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the reporting method.

[0093] The reporting unit can select the optimal reporting means by referring to the past reporting history. The reporting unit can select the optimal reporting means by referring to the past reporting history, for example. For example, the reporting unit selects the optimal reporting means based on the reporting means used by the user in the past. The reporting unit can also select a quick reporting means from the past reporting history. Furthermore, the reporting unit can analyze the reporting history and select the most effective reporting means. As a result, the optimal reporting means can be selected by referring to the past reporting history. A part or all of the above-mentioned processing in the reporting unit may be performed, for example, using AI, or may be performed without using AI. For example, the reporting unit can input past reporting history data to the generation AI and cause the generation AI to select the reporting means.

[0094] The reporting unit can optimize the reporting means in consideration of the current communication situation. The reporting unit can optimize the reporting means in consideration of the current communication situation, for example. For example, when the radio wave strength is weak, the reporting unit selects the most stable communication means by the AI. Also, when the communication situation is good, the reporting unit can select the quickest reporting means by the AI. Furthermore, the reporting unit can monitor the communication situation in real time and select the optimal reporting means. In this way, the optimal reporting means can be selected by considering the communication situation. A part or all of the above-mentioned processing in the reporting unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reporting unit can input communication situation data to the generation AI and cause the generation AI to execute optimization of the reporting means.

[0095] The reporting unit can estimate the user's emotion and adjust the report content based on the estimated user's emotion. The reporting unit can, for example, estimate the user's emotion and adjust the report content based on the estimated user's emotion. For example, when the user is nervous, the reporting unit conveys the report content in a calm voice. Also, when the user is relaxed, the reporting unit can convey the report content including a detailed explanation. Furthermore, when the user is in a hurry, the reporting unit can convey the report content in a concise and quick manner. This enables appropriate reporting by adjusting the report content based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reporting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reporting unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the report content.

[0096] The reporting unit can select the optimal reporting means in consideration of the geographical location information of the user. The reporting unit can select the optimal reporting means in consideration of, for example, the geographical location information of the user. For example, when the user is in an urban area, the reporting unit performs a procedure to call the nearest ambulance. In addition, when the user is in the suburbs, the reporting unit can also perform a procedure to call an ambulance that can arrive most quickly. Furthermore, when the user is in a mountainous area, the reporting unit can also select the most appropriate emergency means. As a result, the optimal reporting means can be selected by considering the geographical location information. Part or all of the above-mentioned processing in the reporting unit may be performed, for example, using AI, or may be performed without using AI. For example, the reporting unit can input geographical location information to the generation AI and cause the generation AI to select the reporting means.

[0097] The reporting unit can analyze the social media activity of the user and optimize the report content. The reporting unit can, for example, analyze the social media activity of the user and optimize the report content. For example, the reporting unit refers to location information posted by the user on social media and optimizes the report content. The reporting unit can also select optimal report content from the social media activity of the user. Furthermore, the reporting unit can also analyze social media activity and optimize the report content. As a result, optimal report content can be selected by analyzing social media activity. A part or all of the above-mentioned processing in the reporting unit may be performed, for example, using AI or may be performed without using AI. For example, the reporting unit can input social media activity data to a generation AI and cause the generation AI to optimize the report content.

[0098] The heart rate detection unit can estimate the user's emotion and adjust the detection accuracy of the heart rate based on the estimated user's emotion. The heart rate detection unit can, for example, estimate the user's emotion and adjust the detection accuracy of the heart rate based on the estimated user's emotion. For example, when the user is nervous, the heart rate detection unit monitors the heart rate fluctuation more closely and detects abnormalities early. In addition, when the user is relaxed, the heart rate detection unit can determine that the heart rate fluctuation is within a normal range and reduce false detection. Furthermore, when the user is excited, the heart rate detection unit can detect a sudden increase in the heart rate and evaluate the abnormality. In this way, false detection can be reduced by adjusting the detection accuracy of the heart rate based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the heart rate detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate detection unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the detection accuracy of the heart rate.

[0099] The heart rate detection unit can detect abnormalities early by referring to past heart rate data. The heart rate detection unit can detect abnormalities early by referring to past heart rate data, for example. For example, the heart rate detection unit can detect abnormal patterns early by referring to the user's past heart rate data. The heart rate detection unit can also evaluate abnormalities in the heart rate by referring to the user's past medical records. Furthermore, the heart rate detection unit can also detect abnormalities by referring to the user's past exercise history and taking into account fluctuations in the heart rate after exercise. Thereby, abnormalities can be detected early by referring to the past heart rate data. A part or all of the above-mentioned processing in the heart rate detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the heart rate detection unit can input past heart rate data to the generation AI and cause the generation AI to execute early detection of abnormalities.

[0100] The heart rate detection unit can optimize the detection algorithm in consideration of the current activity status of the user. The heart rate detection unit can optimize the detection algorithm in consideration of the current activity status of the user, for example. For example, when the user is exercising, the heart rate detection unit detects an abnormality in consideration of a sudden increase in the heart rate. In addition, when the user is resting, the heart rate detection unit can also detect an abnormal decrease in the heart rate and evaluate the abnormality. Furthermore, when the user is driving, the heart rate detection unit can also monitor the fluctuation of the heart rate in real time and detect an abnormality early. This makes it possible to optimize the detection algorithm by considering the current activity status. Part or all of the above-mentioned processing in the heart rate detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the heart rate detection unit can input current activity status data to the generation AI and cause the generation AI to execute optimization of the detection algorithm.

[0101] The heart rate detection unit can estimate the user's emotion and adjust the notification method of the heart rate based on the estimated user's emotion. The heart rate detection unit can, for example, estimate the user's emotion and adjust the notification method of the heart rate based on the estimated user's emotion. For example, when the user is nervous, the heart rate detection unit notifies the user of an abnormal heart rate with a calm voice. In addition, when the user is relaxed, the heart rate detection unit can also provide a notification including a detailed explanation. Furthermore, when the user is in a hurry, the heart rate detection unit can also provide a concise and quick notification. This allows for appropriate notification by adjusting the notification method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the heart rate detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the heart rate detection unit can input the user's emotional data into the generation AI and have the generation AI adjust the notification method.

[0102] The heart rate detection unit can evaluate the abnormality taking into account the geographical location information of the user. The heart rate detection unit can evaluate the abnormality, for example, taking into account the geographical location information of the user. For example, when the user is at high altitude, the heart rate detection unit evaluates the abnormality of the heart rate taking into account a decrease in oxygen concentration. In addition, when the user is in an urban area, the heart rate detection unit can also evaluate the abnormality of the heart rate taking into account stress factors. Furthermore, when the user is at home, the heart rate detection unit can also evaluate the abnormality of the heart rate taking into account a relaxed environment. Thereby, by taking into account the geographical location information, the abnormality can be accurately evaluated. Part or all of the above-mentioned processing in the heart rate detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the heart rate detection unit can input the geographical location information to the generation AI and cause the generation AI to execute the evaluation of the abnormality.

[0103] The heart rate detection unit can analyze the social media activity of the user and improve the detection accuracy by taking into account the stress level. The heart rate detection unit can, for example, analyze the social media activity of the user and improve the detection accuracy by taking into account the stress level. For example, when the user frequently posts something that makes him / her feel stressed, the heart rate detection unit closely monitors the fluctuation of the heart rate and detects an abnormality early. In addition, when the user posts something that makes him / her feel relaxed, the heart rate detection unit can determine that the fluctuation of the heart rate is within a normal range and reduce false detection. Furthermore, when the user posts something that makes him / her feel excited, the heart rate detection unit can also evaluate the abnormality by taking into account a sudden increase in the heart rate. This makes it possible to improve the detection accuracy by analyzing the social media activity and taking into account the stress level. A part or all of the above-mentioned processing in the heart rate detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the heart rate detection unit can input social media activity data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy.

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

[0105] The detection unit can detect the user's body temperature and detect abnormalities in the body temperature. For example, if the user's body temperature rises suddenly, the detection unit can determine that the risk of a seizure is increasing and issue an early warning. The detection unit can also monitor body temperature fluctuations in real time and detect abnormalities. Furthermore, the detection unit can combine body temperature data with other health data for analysis to detect more accurate signs of a seizure. In this way, by detecting abnormalities in body temperature, the risk of a seizure can be detected early.

[0106] The operation unit can analyze the driving style of the user and select the optimal stopping method. For example, if the user normally brakes frequently, the operation unit selects a more careful stopping method. In addition, if the user frequently uses expressways, the operation unit can also select a safe stopping method on expressways. Furthermore, if the user mainly drives in urban areas, the operation unit can also select the optimal stopping method in urban areas. In this way, the optimal stopping method can be selected based on the user's driving style.

[0107] The position determining unit can improve the accuracy of determining the current location by taking into account the surrounding weather information. For example, when the GPS signal is weak during rainy weather, the position determining unit can improve the accuracy of determining the location by referring to other data sources. In addition, when it is snowing, the position determining unit can improve the accuracy of determining the location by referring to topographical information. Furthermore, when a strong wind is blowing, the position determining unit can optimize the accuracy of determining the location by taking into account wind speed data. In this way, the accuracy of determining the location can be improved by taking into account the weather information.

[0108] The notification unit can select the most appropriate notification content by referring to the user's medical history. For example, if the user has experienced a heart attack in the past, the notification unit selects notification content including detailed medical information. In addition, if the user has an allergy, the notification unit can also select notification content including allergy information. Furthermore, if the user is taking a specific medication, the notification unit can also select notification content including that information. In this way, the most appropriate notification content can be selected by referring to the user's medical history.

[0109] The detection unit can estimate the user's emotion and detect an abnormality in the heart rate based on the estimated user's emotion. For example, when the user is stressed, the detection unit monitors the heart rate fluctuation more closely and detects an abnormality early. In addition, when the user is relaxed, the detection unit can determine that the heart rate fluctuation is within a normal range and reduce false detections. Furthermore, when the user is excited, the detection unit can detect a sudden increase in the heart rate and evaluate the abnormality. In this way, false detections can be reduced by detecting an abnormality in the heart rate based on the user's emotion.

[0110] The operation unit can estimate the user's emotions and adjust the car's stopping method based on the estimated user's emotions. For example, if the user is nervous, the operation unit allows the AI ​​to stop the car slowly and safely. Also, if the user is relaxed, the operation unit can allow the AI ​​to quickly select a stopping position. Furthermore, if the user is in a hurry, the operation unit can also allow the AI ​​to select a method to stop the car safely in the shortest time. This makes it possible to stop the car safely by adjusting the stopping method based on the user's emotions.

[0111] The location identification unit can estimate the user's emotions and adjust the accuracy of location identification based on the estimated user's emotions. For example, when the user is nervous, the location identification unit refers to multiple data sources so that the AI ​​can increase the accuracy of location identification. Also, when the user is relaxed, the location identification unit can also perform location identification with normal accuracy. Furthermore, when the user is in a hurry, the location identification unit can also select the optimal data source so that the AI ​​can quickly perform location identification. As a result, accurate location identification is possible by adjusting the accuracy of location identification based on the user's emotions.

[0112] The reporting unit can estimate the user's emotions and adjust the reporting method based on the estimated user's emotions. For example, when the user is nervous, the reporting unit performs the reporting procedure in a calm voice. When the user is relaxed, the reporting unit can also perform the reporting procedure including a detailed explanation. Furthermore, when the user is in a hurry, the reporting unit can also perform a concise and quick reporting procedure. In this way, by adjusting the reporting method based on the user's emotions, appropriate reporting is possible.

[0113] The heart rate detection unit can estimate the user's emotions and adjust the notification method of the heart rate based on the estimated user's emotions. For example, if the user is nervous, 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 including a detailed explanation. Furthermore, if the user is in a hurry, the heart rate detection unit can also provide a concise and quick notification. This allows for appropriate notification by adjusting the notification method based on the user's emotions.

[0114] The detection unit can detect signs of a seizure early by referring to the user's past exercise data. For example, the detection unit can detect abnormal patterns early by referring to the user's past exercise data. The detection unit can also detect signs of a seizure early by referring to the user's past exercise history and taking into account fluctuations in heart rate after exercise. Furthermore, the detection unit can analyze the user's past exercise data and issue a warning if there is a high risk of a seizure. In this way, signs of a seizure can be detected early by referring to past exercise data.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The detection unit detects a heart attack. The detection unit can detect abnormalities in heart rate or arrhythmia, for example, using AI. For example, it can detect a sudden increase in heart rate or arrhythmia and detect early signs of a heart attack. It can also monitor heart rate fluctuations in real time and detect abnormalities. Step 2: The operation unit remotely controls the car based on the heart attack detected by the detection unit. The operation unit can, for example, work with a remote control system to stop the car. The operation unit remotely controls the car via wireless communication or the Internet to stop it in a safe place. Step 3: The position identifying unit identifies the current location of the vehicle that has been stopped by the operation unit. The position identifying unit can identify the current location by using, for example, a GPS function. The current location of the vehicle is identified by 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 an emergency reporting system, for example. The ambulance is called by telephone or via 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0119] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements including the above-mentioned detection unit, operation unit, position identification unit, and notification unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the detection unit detects abnormal heart rate or arrhythmia using the AI ​​of the smart device 14. The operation unit remotely controls the car by the specific processing unit 290 of the data processing device 12 and stops the car in a safe place. The position identification unit identifies the current location using the GPS function of the smart device 14. The notification unit calls an ambulance using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0122] 3, the data processing system 210 includes the 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 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

[0128] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements including the above-mentioned detection unit, operation unit, position identification unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit detects abnormal heart rate or arrhythmia using the AI ​​of the smart glasses 214. The operation unit remotely controls the car by the specific processing unit 290 of the data processing device 12 and stops the car in a safe place. The position identification unit identifies the current location using the GPS function of the smart glasses 214. The notification unit calls an ambulance using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0139] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

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

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements including the above-mentioned detection unit, operation unit, position identification unit, and notification unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the detection unit detects abnormal heart rate or arrhythmia using the AI ​​of the headset type terminal 314. The operation unit remotely controls the car by the specific processing unit 290 of the data processing device 12 and stops the car in a safe place. The position identification unit identifies the current location using the GPS function of the headset type terminal 314. The notification unit calls an ambulance using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0155] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] Each of the multiple elements including the above-mentioned detection unit, operation unit, position identification unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit detects abnormalities in heart rate and arrhythmia using the AI ​​of the robot 414. The operation unit remotely controls the car by the specific processing unit 290 of the data processing device 12 and stops it in a safe place. The position identification unit identifies the current location using the GPS function of the robot 414. The notification unit calls an ambulance using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0171] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0174] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0175] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this 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 the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

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

[0179] In addition, 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 upon request from the data processing device 12.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

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

[0183] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0186] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0188] (Appendix 1) a detection unit for detecting a heart attack; an operating unit that remotely operates the vehicle based on the heart attack detected by the detection unit; a location identification unit that identifies a current location of a vehicle stopped by the operation unit; A reporting unit that calls an ambulance based on the current location identified by the location identifying unit. A system characterized by: (Appendix 2) Equipped with a heart rate detector that detects abnormalities in the heart rate 2. The system of claim 1, (Appendix 3) The operation unit includes: Works in conjunction with a remote control system to stop the vehicle 2. The system of claim 1, (Appendix 4) The position identification unit is Use the GPS function to identify your current location 2. The system of claim 1, (Appendix 5) The reporting unit includes: Call an ambulance based on your location 2. The system of claim 1, (Appendix 6) The detection unit is Inferring a user's emotion and adjusting the accuracy of heart attack detection based on the estimated user's emotion 2. The system of claim 1, (Appendix 7) The detection unit is Detect early signs of seizures by referencing a user's past health data 2. The system of claim 1, (Appendix 8) The detection unit is Adjusting the detection algorithm based on the user's current activity 2. The system of claim 1, (Appendix 9) The detection unit is Estimate the user's emotions and adjust the notification method of the detection result based on the estimated user's emotions 2. The system of claim 1, (Appendix 10) The detection unit is Assessing seizure risk by taking into account the user's geographic location 2. The system of claim 1, (Appendix 11) The detection unit is Analyzes users' social media activity and takes their stress levels into account to improve detection accuracy 2. The system of claim 1, (Appendix 12) The operation unit includes: Estimating a user's emotions and adjusting the car's stopping method based on the estimated user's emotions 2. The system of claim 1, (Appendix 13) The operation unit includes: Select a stopping location based on the vehicle's current speed and traffic conditions 2. The system of claim 1, (Appendix 14) The operation unit includes: Ensure safety by referencing information from other sensors inside the vehicle 2. The system of claim 1, (Appendix 15) The operation unit includes: Estimate the user's emotions and adjust the notification method after stopping based on the estimated user emotions 2. The system of claim 1, (Appendix 16) The operation unit includes: Select the best stopping location by taking into account the vehicle's geographic location 2. The system of claim 1, (Appendix 17) The operation unit includes: Select the optimal stopping method by referring to the vehicle's past driving history 2. The system of claim 1, (Appendix 18) The position identification unit is Estimating user emotions and adjusting location accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 19) The position identification unit is Referencing past location data to improve accuracy of current location 2. The system of claim 1, (Appendix 20) The position identification unit is Improve accuracy based on surrounding geography 2. The system of claim 1, (Appendix 21) The position identification unit is A method for notifying a user of a location determination result based on the estimated user's emotion is provided. 2. The system of claim 1, (Appendix 22) The position identification unit is Improve identification accuracy by taking into account the user's geographic location information 2. The system of claim 1, (Appendix 23) The reporting unit includes: Estimate the user's emotions and adjust the reporting method based on the estimated user's emotions 2. The system of claim 1, (Appendix 24) The reporting unit includes: Refer to past reporting history to select the most appropriate reporting method 2. The system of claim 1, (Appendix 25) The reporting unit includes: Optimize reporting methods by taking into account current communication conditions 2. The system of claim 1, (Appendix 26) The reporting unit includes: Estimate the user's emotions and adjust the report content based on the estimated user emotions 2. The system of claim 1, (Appendix 27) The reporting unit includes: Select the optimal reporting method based on the user's geographic location 2. The system of claim 1, (Appendix 28) The reporting unit includes: Analyze users' social media activity to optimize report content 2. The system of claim 1, (Appendix 29) The heart rate detection unit To estimate a user's emotion and adjust a detection accuracy of a heart rate based on the estimated user's emotion. 3. The system of claim 2, (Appendix 30) The heart rate detection unit Refer to past heart rate data to detect abnormalities early 3. The system of claim 2, (Appendix 31) The heart rate detection unit Optimize the detection algorithm by taking into account the user's current activity 3. The system of claim 2, (Appendix 32) The heart rate detection unit To estimate a user's emotion and adjust a method of notifying a heart rate based on the estimated user's emotion. 3. The system of claim 2, (Appendix 33) The heart rate detection unit Evaluating anomalies taking into account users' geographic location 3. The system of claim 2, (Appendix 34) The heart rate detection unit Analyzes users' social media activity and takes their stress levels into account to improve detection accuracy 3. The system of claim 2, [Explanation of symbols]

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

Claims

1. a detection unit for detecting a heart attack; an operating unit that remotely operates the vehicle based on the heart attack detected by the detection unit; a location identification unit that identifies a current location of a vehicle stopped by the operation unit; A reporting unit that calls an ambulance based on the current location identified by the location identifying unit. A system characterized by:

2. Equipped with a heart rate detector that detects abnormalities in the heart rate 2. The system of claim 1 .

3. The operation unit includes: Works in conjunction with a remote control system to stop the vehicle 2. The system of claim 1 .

4. The position identification unit is Use the GPS function to identify your current location 2. The system of claim 1 .

5. The reporting unit includes: Call an ambulance based on your location 2. The system of claim 1 .

6. The detection unit is Inferring a user's emotion and adjusting the accuracy of heart attack detection based on the estimated user's emotion 2. The system of claim 1 .

7. The detection unit is Detect early signs of seizures by referencing a user's past health data 2. The system of claim 1 .

8. The detection unit is Adjusting the detection algorithm based on the user's current activity 2. The system of claim 1 .

9. The detection unit is Estimate the user's emotions and adjust the notification method of the detection result based on the estimated user's emotions 2. The system of claim 1 .

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