system

Abstract

The system according to the embodiment aims to monitor the health condition of the driver in real time and immediately detect and respond to any abnormalities. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an alarm issuing unit, and a stop unit. The collection unit collects physical information of the driver. The analysis unit analyzes the physical information collected by the collection unit and detects abnormalities. The alarm issuing unit issues an alert based on the abnormality detected by the analysis unit. The stop unit automatically performs an emergency stop based on the alert issued by the alarm issuing unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to monitor the driver's health condition in real time and immediately detect and respond to any abnormalities.

[0005] The system according to the embodiment aims to monitor the health condition of the driver in real time and immediately detect and respond to any abnormalities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an alarm issuing unit, and a stop unit. The collection unit collects physical information of the driver. The analysis unit analyzes the physical information collected by the collection unit and detects abnormalities. The alarm issuing unit issues an alert based on the abnormality detected by the analysis unit. The stop unit automatically performs an emergency stop based on the alert issued by the alarm issuing unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the driver's health condition in real time and immediately detect and respond to any abnormalities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health monitoring system according to an embodiment of the present invention monitors the health of bus and truck drivers and detects myocardial infarction and stroke before they occur. This health monitoring system uses sensors attached to the driver's fingertips to collect physical information about the driver while driving, and then uses a generative AI to analyze and predict the driver's condition. The system detects abnormal conditions from the collected data and issues alerts to the driver and his or her supervisor. Furthermore, automatic braking and automatic hazard warnings automatically bring the vehicle to an emergency stop and notify those around the driver of the abnormality. This system ensures the driver's health and safe driving, protecting passengers from sudden accidents. For example, sensors attached to the driver's fingertips collect data such as heart rate, blood pressure, and oxygen saturation in real time. If the driver's heart rate suddenly increases while driving, the sensor collects the data. The generative AI then analyzes and predicts the collected data. The generative AI analyzes the collected data and predicts the driver's health condition. For example, it can predict the risk of myocardial infarction and stroke based on fluctuations in heart rate and blood pressure. If an abnormality is detected, an alert is issued to the driver and his or her supervisor. For example, if a driver's heart rate becomes abnormally high, the generating AI will detect the abnormality and send an alert to the driver and supervisor. This allows the driver to recognize the abnormality and take appropriate action. Furthermore, automatic braking and automatic hazard warning systems will automatically initiate an emergency stop and alert those around the vehicle to the abnormality. For example, if a driver loses consciousness, the generating AI will automatically apply the brakes and turn on the hazard lights. This will alert surrounding vehicles and pedestrians to the abnormality and prevent an accident. This system ensures the driver's health and safe driving, and protects passengers from sudden accidents. For example, if a driver suffers a myocardial infarction, the generating AI will detect the abnormality and initiate an emergency stop, ensuring passenger safety. This allows the health monitoring system to monitor the driver's health and ensure the safety of both the driver and passengers by detecting abnormalities and making an emergency stop.

[0029] A health monitoring system according to an embodiment includes a collection unit, an analysis unit, an alarm transmission unit, and a stopping unit. The collection unit collects physical information of the driver. The physical information of the driver includes, but is not limited to, heart rate, blood pressure, and oxygen saturation. For example, the collection unit collects the heart rate in real time using a sensor attached to the driver's fingertip. The collection unit can also collect blood pressure data using a sensor for measuring blood pressure. The collection unit can also collect oxygen saturation data using a sensor for measuring oxygen saturation. For example, the collection unit monitors the driver's heart rate in real time using a heart rate sensor. The blood pressure sensor periodically measures the driver's blood pressure and detects abnormal fluctuations. The oxygen saturation sensor measures the driver's oxygen saturation and issues an alert if it drops. The analysis unit analyzes the physical information collected by the collection unit and detects abnormalities. For example, the analysis unit analyzes the heart rate data using a generative AI to detect abnormal patterns. The analysis unit can also analyze blood pressure data and detect sudden fluctuations. Furthermore, the analysis unit can analyze oxygen saturation data and detect abnormalities when the data drops. For example, the analysis unit uses generative AI to analyze heart rate data and predict the risk of myocardial infarction. Analysis of blood pressure data can predict the risk of stroke. Analysis of oxygen saturation data can detect abnormalities in the respiratory system. The alarm unit issues an alert based on the abnormality detected by the analysis unit. For example, the alarm unit issues an audio alert to the driver. The alarm unit can also issue a visual alert to the manager. Furthermore, when an abnormality is detected, the alarm unit can simultaneously send an alert to the driver and the manager. For example, the alarm unit notifies the driver by audio, saying, "Your heart rate is abnormally high." Details of the abnormality are displayed to the manager as a visual alert. The stop unit automatically performs an emergency stop based on the alert issued by the alarm unit. The stop unit, for example, activates the automatic braking system to stop the vehicle. The stopping unit can also turn on the hazard lights to warn those around it of an abnormality. Furthermore, the stopping unit can automatically make an emergency stop if the driver loses consciousness.For example, the stopping unit activates an automatic braking system to safely stop the vehicle. It also turns on hazard lights to alert surrounding vehicles and pedestrians of an abnormality. If the driver loses consciousness, the stopping unit automatically makes an emergency stop to prevent an accident. As a result, the health monitoring system according to the embodiment can ensure the safety of the driver and passengers by monitoring the driver's health condition and detecting an abnormality to make an emergency stop.

[0030] The collection unit can collect data on heart rate, blood pressure, and oxygen saturation. The collection unit, for example, uses a heart rate sensor to collect the driver's heart rate in real time. The heart rate sensor is worn on the driver's fingertip and measures the heart rate. For example, if the driver's heart rate suddenly increases while driving, the heart rate sensor collects the data. The collection unit can also collect blood pressure data using a sensor for measuring blood pressure. The blood pressure sensor is worn on the driver's arm and measures blood pressure. For example, if the driver's blood pressure suddenly fluctuates while driving, the blood pressure sensor collects the data. The collection unit can also collect oxygen saturation data using a sensor for measuring oxygen saturation. The oxygen saturation sensor is worn on the driver's fingertip and measures oxygen saturation. For example, if the driver's oxygen saturation level drops while driving, the oxygen saturation sensor collects the data. This allows the driver's health condition to be monitored in detail. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI or without using a generation AI. For example, the collection unit can input data acquired by a heart rate sensor into the generation AI, which can then analyze the data and detect abnormalities.

[0031] The analysis unit can analyze the collected data and predict the risk of myocardial infarction or stroke. For example, the analysis unit uses a generating AI to analyze heart rate data and predict the risk of myocardial infarction. The generating AI detects abnormal patterns based on the heart rate data and evaluates the risk of myocardial infarction. For example, if the heart rate suddenly increases, the generating AI determines that the risk of myocardial infarction is high. The analysis unit can also analyze blood pressure data and predict the risk of stroke. The generating AI detects sudden fluctuations based on blood pressure data and evaluates the risk of stroke. For example, if blood pressure suddenly increases, the generating AI determines that the risk of stroke is high. Furthermore, the analysis unit can analyze oxygen saturation data and detect abnormalities in the respiratory system. The generating AI detects abnormalities based on a decrease in oxygen saturation data. For example, if oxygen saturation decreases, the generating AI detects an abnormality in the respiratory system. This makes it possible to predict the driver's health risks in advance. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the analysis unit can input heart rate data into the generation AI, which can then analyze the data to predict the risk of myocardial infarction.

[0032] The alarm unit can send an alert to the driver and the manager when an abnormality is detected. The alarm unit, for example, issues an audio alert to the driver. The audio alert is intended to notify the driver of an abnormality and is designed to make it easy for the driver to recognize the abnormality. For example, the alarm unit may audio-notify the driver, saying, "Your heart rate is abnormally high." The alarm unit can also issue a visual alert to the manager. The visual alert is intended to display details of the abnormality to the manager and is designed to enable the manager to respond quickly. For example, the alarm unit may issue a visual alert that displays details of the abnormality to the manager. Furthermore, the alarm unit can simultaneously send alerts to the driver and the manager when an abnormality is detected. This allows the driver and the manager to recognize the abnormality simultaneously and respond quickly. Some or all of the above-described processing in the alarm unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the alarm unit can send an alert to the driver and the manager when the generation AI detects an abnormality. This allows a quick response when an abnormality is detected.

[0033] The stopping unit can automatically apply the brakes and turn on the hazard lights when the driver loses consciousness. The stopping unit, for example, activates an automatic braking system to stop the vehicle. The automatic braking system automatically activates when the driver loses consciousness to safely stop the vehicle. For example, the stopping unit can activate the automatic braking system to stop the vehicle when the driver loses consciousness. The stopping unit can also turn on the hazard lights to notify those around it of an abnormality. Hazard lights are used to notify surrounding vehicles and pedestrians of an abnormality and play an important role in preventing accidents. For example, the stopping unit can turn on the hazard lights to notify those around it of an abnormality when the driver loses consciousness. Furthermore, the stopping unit can automatically make an emergency stop when the driver loses consciousness. This can automatically make an emergency stop when the driver loses consciousness and notify those around it of an abnormality. Some or all of the above-mentioned processing in the stopping unit can be performed, for example, using a generation AI or without using a generation AI. For example, the stopping unit can automatically apply the brakes and turn on the hazard lights if the generating AI detects that the driver has lost consciousness.

[0034] The collection unit can analyze the driver's past health data and select appropriate sensor placement. For example, the collection unit can analyze the driver's past heart rate data and determine the optimal placement of the heart rate sensor. The collection unit can refer to a health checkup database to collect the driver's past health data. For example, the collection unit acquires the driver's past health checkup results, and the generation AI analyzes the data to select the optimal sensor placement. The collection unit can also adjust the placement of the blood pressure sensor based on the driver's past blood pressure data. For example, the collection unit acquires the driver's past blood pressure data, and the generation AI analyzes the data to determine the optimal placement of the blood pressure sensor. Furthermore, the collection unit can also optimize the placement of the oxygen saturation sensor based on the driver's past oxygen saturation data. For example, the collection unit acquires the driver's past oxygen saturation data, and the generation AI analyzes the data to determine the optimal placement of the oxygen saturation sensor. This improves data accuracy by selecting the optimal sensor placement based on the driver's past health data. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the driver's past health data into the generation AI, which may then analyze the data and select optimal sensor placement.

[0035] When collecting physical information, the collection unit can filter the data based on the driver's current driving situation. For example, when the driver is driving on a highway, the collection unit filters noise from the heart rate data. The collection unit can use the vehicle's speed sensor to understand the driver's driving situation. For example, the collection unit acquires vehicle speed data, and the generation AI analyzes the data to understand the driving situation and filters noise from the heart rate data. The collection unit can also filter fluctuations in blood pressure data when the driver is driving in an urban area. For example, the collection unit acquires vehicle location data, and the generation AI analyzes the data to understand the driving situation and filters fluctuations in blood pressure data. Furthermore, when the driver is stopped, the collection unit can filter to confirm the stability of oxygen saturation data. For example, the collection unit acquires vehicle stop data, and the generation AI analyzes the data to understand the driving situation and filters to confirm the stability of the oxygen saturation data. In this way, by filtering data according to the driving situation, noise can be removed and accurate data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input driving situation data to the generation AI, and the generation AI may analyze the data and perform filtering.

[0036] When collecting physical information, the collection unit can prioritize collecting highly relevant data based on the driver's geographical location information. For example, when the driver is traveling in a mountainous area, the collection unit prioritizes collecting oxygen saturation data. The collection unit can use GPS data to determine the driver's geographical location information. For example, the collection unit acquires the driver's GPS data, and the generation AI analyzes the data to determine the geographical location information and prioritizes collecting oxygen saturation data. The collection unit can also prioritize collecting heart rate data when the driver is traveling in an urban area. For example, the collection unit acquires the driver's location data, and the generation AI analyzes the data to determine the geographical location information and prioritizes collecting heart rate data. Furthermore, the collection unit can prioritize collecting blood pressure data when the driver is traveling a long distance. For example, the collection unit acquires the driver's location data, and the generation AI analyzes the data to determine the geographical location information and prioritizes collecting blood pressure data. This enables more accurate health monitoring by prioritizing the collection of highly relevant data based on geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the driver's GPS data into the generation AI, which may then analyze the data and preferentially collect highly relevant data.

[0037] When collecting physical information, the collection unit can analyze the driver's online activities and collect related data. For example, if the driver posts on social media that he or she is stressed, the collection unit prioritizes collecting heart rate data. The collection unit can use social media posting data to understand the driver's online activities. For example, the collection unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the stress level and prioritizes collecting heart rate data. The collection unit can also prioritize collecting blood pressure data if the driver posts that he or she is relaxed. For example, the collection unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the level of relaxation and prioritizes collecting blood pressure data. Furthermore, the collection unit can also prioritize collecting oxygen saturation data if the driver posts that he or she is tired. For example, the collection unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the level of fatigue and prioritizes collecting oxygen saturation data. This makes it possible to monitor the driver's health condition in more detail by collecting related data based on social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input data posted by drivers on social media into the generation AI, which may then analyze the data and collect related data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, if heart rate data is abnormal, the analysis unit performs a detailed analysis. The analysis unit can use a generation AI to evaluate the importance of the collected data. For example, the analysis unit inputs heart rate data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. The analysis unit can also perform a detailed analysis if blood pressure data is abnormal. For example, the analysis unit inputs blood pressure data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. The analysis unit can also perform a detailed analysis if oxygen saturation data is abnormal. For example, the analysis unit inputs oxygen saturation data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. This enables efficient analysis by adjusting the level of detail of the analysis depending on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input collected data into the generation AI, which can then analyze the data, evaluate its importance, and adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the driver's health history. For example, if the driver has previously experienced a myocardial infarction, the analysis unit adjusts the analysis algorithm for the heart rate data. The analysis unit can refer to a health checkup database to understand the driver's health history. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of myocardial infarction and adjusts the analysis algorithm for the heart rate data. The analysis unit can also adjust the analysis algorithm for the blood pressure data if the driver has a history of high blood pressure. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of high blood pressure and adjusts the analysis algorithm for the blood pressure data. Furthermore, the analysis unit can adjust the analysis algorithm for the oxygen saturation data if the driver has a respiratory disease. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of respiratory disease and adjusts the analysis algorithm for the oxygen saturation data. This allows for more accurate analysis by adjusting the analysis algorithm based on the driver's health history. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit may input the driver's health history data into the generation AI, which may then analyze the data and adjust the analysis algorithm.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recently collected data. The analysis unit can refer to the database timestamp to determine the time when the data was collected. For example, the analysis unit obtains the database timestamp, and the generation AI analyzes the data to determine the time when the data was collected, and prioritizes analyzing the most recently collected data. The analysis unit can also refer to past data while emphasizing the most recent data. For example, the analysis unit obtains past data, and the generation AI analyzes the data to determine the time when the data was collected, and prioritizes analyzing the most recent data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific time period. For example, the analysis unit obtains data collected during a specific time period, and the generation AI analyzes the data to determine the time when the data was collected, and prioritizes analyzing the data collected during the specific time period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI, and the generation AI can analyze the data and determine the priority of the analysis.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit adjusts the order of analysis taking into account the relevance between heart rate data and blood pressure data. The analysis unit can use a generation AI to evaluate the relevance of the data. For example, the analysis unit inputs heart rate data and blood pressure data into the generation AI, which analyzes the data, evaluates the relevance, and adjusts the order of analysis. The analysis unit can also adjust the order of analysis taking into account the relevance between oxygen saturation data and heart rate data. For example, the analysis unit inputs oxygen saturation data and heart rate data into the generation AI, which analyzes the data, evaluates the relevance, and adjusts the order of analysis. The analysis unit can also adjust the order of analysis taking into account the relevance between blood pressure data and oxygen saturation data. For example, the analysis unit inputs blood pressure data and oxygen saturation data into the generation AI, which analyzes the data, evaluates the relevance, and adjusts the order of analysis. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data relevance to the generation AI, which can then analyze the data and adjust the order of analysis.

[0042] When issuing an alert, the alert issuing unit can adjust the level of detail of the alert based on the severity of the anomaly. For example, if a serious anomaly is detected, the alert issuing unit issues a detailed alert. The alert issuing unit can use a generation AI to evaluate the severity of the anomaly. For example, the alert issuing unit inputs abnormality data to the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues a detailed alert. The alert issuing unit can also issue a concise alert if a minor anomaly is detected. For example, the alert issuing unit inputs abnormality data to the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues a concise alert. Furthermore, the alert issuing unit can also issue an alert with a moderate level of detail if a moderate anomaly is detected. For example, the alert issuing unit inputs abnormality data to the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues an alert with a moderate level of detail. This allows appropriate responses to be taken by adjusting the level of detail of the alert depending on the severity of the anomaly. Some or all of the above-described processing in the alert issuing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the alert issuing unit may input abnormality data to the generation AI, which may analyze the data to evaluate the importance of the abnormality and adjust the level of detail of the alert.

[0043] When issuing an alert, the alert issuing unit can apply different alert algorithms depending on the category of the abnormality. For example, if an abnormality in heart rate is detected, the alert issuing unit applies an alert algorithm specialized for heart rate. The alert issuing unit can use a generation AI to classify the category of the abnormality. For example, the alert issuing unit inputs abnormal data to the generation AI, which analyzes the data to classify the category of the abnormality and applies an alert algorithm specialized for heart rate. Furthermore, if an abnormality in blood pressure is detected, the alert issuing unit can also apply an alert algorithm specialized for blood pressure. For example, the alert issuing unit inputs abnormal data to the generation AI, which analyzes the data to classify the category of the abnormality and applies an alert algorithm specialized for blood pressure. Furthermore, if an abnormality in oxygen saturation is detected, the alert issuing unit can also apply an alert algorithm specialized for oxygen saturation. For example, the alert issuing unit inputs abnormal data to the generation AI, which analyzes the data to classify the category of the abnormality and applies an alert algorithm specialized for oxygen saturation. This makes it possible to issue an optimal alert according to the category of the abnormality. Some or all of the above-described processing in the alarm issuing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the alarm issuing unit may input abnormality data to the generation AI, which may analyze the data, classify the abnormality into categories, and apply an alert algorithm.

[0044] When issuing an alert, the alert issuing unit can adjust the order of alerts based on the time of the anomaly occurrence. For example, the alert issuing unit prioritizes alerts for recently occurring anomalies. The alert issuing unit can refer to a timestamp in a database to determine when the anomaly occurred. For example, the alert issuing unit acquires a timestamp in a database, and the generation AI analyzes the data to determine the time of occurrence, and prioritizes alerts for recently occurring anomalies. The alert issuing unit can also emphasize the most recent anomaly while referring to past anomalies. For example, the alert issuing unit acquires past anomaly data, and the generation AI analyzes the data to determine the time of occurrence, and prioritizes alerts for the most recent anomaly. Furthermore, the alert issuing unit can prioritize alerts for anomalies that occurred during a specific time period. For example, the alert issuing unit acquires anomaly data that occurred during a specific time period, and the generation AI analyzes the data to determine the time of occurrence, and prioritizes alerts for anomalies that occurred during a specific time period. This allows the most recent anomaly to be addressed by adjusting the order of alerts based on the time of occurrence of the anomaly. Some or all of the above-described processing in the alert issuing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the alarm issuing unit can input the time when an abnormality occurred into the generation AI, which can then analyze the data and adjust the order of alerts.

[0045] When issuing an alert, the alert issuing unit can adjust the display method of the alert based on the correlation of the abnormalities. For example, if an abnormal heart rate and an abnormal blood pressure are correlated, the alert displays an alert that takes the correlation into consideration. The alert issuing unit can use a generation AI to evaluate the correlation of the abnormalities. For example, the alert issuing unit inputs heart rate data and blood pressure data into the generation AI, which analyzes the data to evaluate the correlation and displays an alert that takes the correlation into consideration. The alert issuing unit can also display an alert that takes the correlation into consideration if an abnormal oxygen saturation and an abnormal heart rate are correlated. For example, the alert issuing unit inputs oxygen saturation data and heart rate data into the generation AI, which analyzes the data to evaluate the correlation and displays an alert that takes the correlation into consideration. The alert issuing unit can also display an alert that takes the correlation into consideration if an abnormal blood pressure and an abnormal oxygen saturation are correlated. For example, the alert issuing unit inputs blood pressure data and oxygen saturation data into the generation AI, which analyzes the data to evaluate the correlation and displays an alert that takes the correlation into consideration. This allows related abnormalities to be identified at a glance by adjusting the display method of the alert based on the correlation of the abnormalities. Some or all of the above-described processing in the alert issuing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the alert issuing unit may input abnormality data to the generation AI, which may analyze the data, evaluate relevance, and adjust the alert display method.

[0046] The stopping unit can analyze the driver's past driving history to select the optimal stopping method during an emergency stop. For example, if the driver has previously experienced an emergency stop, the stopping unit can select the optimal stopping method by referring to the data from that experience. The stopping unit can refer to a driving history database to understand the driver's past driving history. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to select the optimal stopping method. The stopping unit can also analyze the driver's reaction time during an emergency stop from the driver's past driving history and select the optimal stopping method. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to evaluate the reaction time and select the optimal stopping method. Furthermore, the stopping unit can adjust the braking method during an emergency stop based on the driver's past driving history. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to evaluate the braking method and select the optimal stopping method. This enables safer stopping by selecting the optimal stopping method based on the driver's past driving history. Some or all of the above-described processing in the stopping unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the stopping unit may input the driver's past driving history data into the generation AI, which may then analyze the data and select the optimal stopping method.

[0047] In the event of an emergency stop, the stopping unit can customize the stopping means based on the driver's current driving situation. For example, when the driver is driving on a highway, the stopping unit applies gentle brakes to stop the vehicle. The stopping unit can use the vehicle's speed sensor to grasp the driver's current driving situation. For example, the stopping unit acquires vehicle speed data, and the generation AI analyzes the data to understand the driving situation and applies gentle brakes to stop the vehicle. The stopping unit can also quickly apply the brakes to stop the vehicle when the driver is driving in an urban area. For example, the stopping unit acquires vehicle position data, and the generation AI analyzes the data to understand the driving situation and quickly apply the brakes to stop the vehicle. Furthermore, when the driver is stopped, the stopping unit can turn on the hazard lights to alert those around the vehicle of an abnormality. For example, the stopping unit acquires vehicle stop data, and the generation AI analyzes the data to understand the driving situation and turns on the hazard lights to alert those around the vehicle of an abnormality. This enables safer stopping by customizing the stopping means based on the driver's current driving situation. Some or all of the above-described processing in the stopping unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the stopping unit may input driving situation data to the generation AI, and the generation AI may analyze the data and customize the stopping means.

[0048] In the event of an emergency stop, the stopping unit can select the optimal stopping method by taking into account the driver's geographical location information. For example, if the driver is driving in a mountainous area, the stopping unit applies gentle brakes to stop the vehicle. The stopping unit can use GPS data to determine the driver's geographical location. For example, the stopping unit acquires the driver's GPS data, and the generating AI analyzes the data to determine the geographical location and applies gentle brakes to stop the vehicle. The stopping unit can also quickly apply the brakes to stop the vehicle when the driver is driving in an urban area. For example, the stopping unit acquires the driver's location data, and the generating AI analyzes the data to determine the geographical location and quickly apply the brakes to stop the vehicle. Furthermore, if the driver is driving on a highway, the stopping unit can turn on the hazard lights to alert those around the vehicle of an abnormality. For example, the stopping unit acquires the driver's location data, and the generating AI analyzes the data to determine the geographical location and turns on the hazard lights to alert those around the vehicle of an abnormality. This enables safer stopping by selecting the optimal stopping method based on the driver's geographical location information. Some or all of the above-described processing in the stopping unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the stopping unit may input the driver's GPS data into the generation AI, which may then analyze the data and select the optimal stopping method.

[0049] In the event of an emergency stop, the stopping unit can analyze the driver's social media activity and suggest stopping measures. For example, if the driver posts on social media that he or she is stressed, the stopping unit applies gentle brakes to stop the vehicle. The stopping unit can use social media posting data to understand the driver's social media activity. For example, the stopping unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the stress level and applies gentle brakes to stop the vehicle. The stopping unit can also apply normal brakes to stop the vehicle if the driver posts that he or she is relaxed. For example, the stopping unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the level of relaxation and applies normal brakes to stop the vehicle. Furthermore, the stopping unit can quickly apply brakes to stop the vehicle if the driver posts that he or she is tired. For example, the stopping unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the level of fatigue and applies the brakes quickly to stop the vehicle. This enables more appropriate stopping by suggesting stopping measures based on the driver's social media activity. Some or all of the above-described processing in the stopping unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the stopping unit may input data posted by the driver on social media into the generation AI, which may then analyze the data and suggest stopping measures.

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

[0051] The collection unit not only collects the driver's physical information, but also analyzes the driver's past health data and selects appropriate sensor placement. For example, it analyzes the driver's past heart rate data and determines the optimal placement of the heart rate sensor. The collection unit can refer to a health checkup database to collect the driver's past health data. For example, the collection unit acquires the driver's past health checkup results, and the generation AI analyzes the data to select the optimal sensor placement. The collection unit can also adjust the placement of the blood pressure sensor based on the driver's past blood pressure data. For example, the collection unit acquires the driver's past blood pressure data, and the generation AI analyzes the data to determine the optimal placement of the blood pressure sensor. Furthermore, the collection unit can also optimize the placement of the oxygen saturation sensor based on the driver's past oxygen saturation data. For example, the collection unit acquires the driver's past oxygen saturation data, and the generation AI analyzes the data to determine the optimal placement of the oxygen saturation sensor. This improves data accuracy by selecting the optimal sensor placement based on the driver's past health data. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the driver's past health data into the generation AI, which may then analyze the data and select optimal sensor placement.

[0052] The analysis unit not only analyzes the collected data, but also applies different analysis algorithms depending on the driver's health history. For example, if the driver has previously experienced a myocardial infarction, it adjusts the analysis algorithm for heart rate data. The analysis unit can refer to a health checkup database to understand the driver's health history. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of myocardial infarction and adjusts the analysis algorithm for heart rate data. The analysis unit can also adjust the analysis algorithm for blood pressure data if the driver has a history of high blood pressure. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of high blood pressure and adjusts the analysis algorithm for blood pressure data. Furthermore, the analysis unit can adjust the analysis algorithm for oxygen saturation data if the driver has a respiratory disease. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of respiratory disease and adjusts the analysis algorithm for oxygen saturation data. This allows for more accurate analysis by adjusting the analysis algorithm based on the driver's health history. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit may input the driver's health history data into the generation AI, which may then analyze the data and adjust the analysis algorithm.

[0053] The alerting unit not only issues an alert when an anomaly is detected, but can also adjust the level of detail of the alert based on the severity of the anomaly. For example, if a serious anomaly is detected, a detailed alert is issued. The alerting unit can use a generation AI to evaluate the severity of the anomaly. For example, the alerting unit inputs abnormality data into the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues a detailed alert. The alerting unit can also issue a concise alert when a minor anomaly is detected. For example, the alerting unit inputs abnormality data into the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues a concise alert. Furthermore, the alerting unit can also issue an alert with a moderate level of detail when a moderate anomaly is detected. For example, the alerting unit inputs abnormality data into the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues an alert with a moderate level of detail. This allows appropriate responses to be taken by adjusting the level of detail of the alert depending on the severity of the anomaly. Some or all of the above-described processing in the alert issuing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the alert issuing unit may input abnormality data to the generation AI, which may analyze the data to evaluate the importance of the abnormality and adjust the level of detail of the alert.

[0054] The stopping unit not only automatically applies the brakes when the driver loses consciousness, but can also analyze the driver's past driving history to select the optimal stopping method. For example, if the driver has previously experienced an emergency stop, the optimal stopping method can be selected based on the data from that experience. The stopping unit can refer to a driving history database to understand the driver's past driving history. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to select the optimal stopping method. The stopping unit can also analyze the driver's reaction time in an emergency stop from the driver's past driving history and select the optimal stopping method. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to evaluate the reaction time and select the optimal stopping method. Furthermore, the stopping unit can adjust the braking method in an emergency stop based on the driver's past driving history. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to evaluate the braking method and select the optimal stopping method. This enables safer stopping by selecting the optimal stopping method based on the driver's past driving history. Some or all of the above-described processing in the stopping unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the stopping unit may input the driver's past driving history data into the generation AI, which may then analyze the data and select the optimal stopping method.

[0055] When collecting physical information, the collection unit can filter the data based on the driver's current driving situation. For example, if the driver is driving on a highway, the collection unit filters noise from the heart rate data. The collection unit can use the vehicle's speed sensor to understand the driver's driving situation. For example, the collection unit acquires vehicle speed data, and the generation AI analyzes the data to understand the driving situation and filters noise from the heart rate data. The collection unit can also filter fluctuations in blood pressure data when the driver is driving in an urban area. For example, the collection unit acquires vehicle location data, and the generation AI analyzes the data to understand the driving situation and filters fluctuations in blood pressure data. Furthermore, the collection unit can filter oxygen saturation data to confirm its stability when the driver is stopped. For example, the collection unit acquires vehicle stop data, and the generation AI analyzes the data to understand the driving situation and filters oxygen saturation data to confirm its stability. In this way, by filtering data according to the driving situation, noise can be removed and accurate data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input driving situation data to the generation AI, and the generation AI may analyze the data and perform filtering.

[0056] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, if heart rate data is abnormal, a detailed analysis is performed. The analysis unit can use a generation AI to evaluate the importance of the collected data. For example, the analysis unit inputs heart rate data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. The analysis unit can also perform a detailed analysis if blood pressure data is abnormal. For example, the analysis unit inputs blood pressure data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. The analysis unit can also perform a detailed analysis if oxygen saturation data is abnormal. For example, the analysis unit inputs oxygen saturation data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. This enables efficient analysis by adjusting the level of detail of the analysis depending on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input collected data into the generation AI, which can then analyze the data, evaluate its importance, and adjust the level of detail of the analysis.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects the driver's physical information, which includes heart rate, blood pressure, oxygen saturation, etc. The collection unit collects the driver's heart rate in real time using a sensor attached to the driver's fingertip, blood pressure data using a blood pressure sensor, and oxygen saturation data using an oxygen saturation sensor. Step 2: The analysis unit analyzes the physical information collected by the collection unit and detects abnormalities. Using the generative AI, the analysis unit analyzes heart rate data to detect abnormal patterns, blood pressure data to detect sudden fluctuations, and oxygen saturation data to detect declines. Step 3: The alarm unit issues an alert based on the abnormality detected by the analysis unit. The alarm unit issues an audio alert to the driver and a visual alert to the manager, and if an abnormality is detected, it sends an alert to both the driver and the manager simultaneously. Step 4: The stopping unit automatically makes an emergency stop based on the alert issued by the alarm unit. The stopping unit activates the automatic braking system to stop the vehicle, turns on the hazard lights to alert those around it of the abnormality, and automatically makes an emergency stop if the driver loses consciousness.

[0059] (Example 2) A health monitoring system according to an embodiment of the present invention monitors the health of bus and truck drivers and detects myocardial infarction and stroke before they occur. This health monitoring system uses sensors attached to the driver's fingertips to collect physical information about the driver while driving, and then uses a generative AI to analyze and predict the driver's condition. The system detects abnormal conditions from the collected data and issues alerts to the driver and his or her supervisor. Furthermore, automatic braking and automatic hazard warnings automatically bring the vehicle to an emergency stop and notify those around the driver of the abnormality. This system ensures the driver's health and safe driving, protecting passengers from sudden accidents. For example, sensors attached to the driver's fingertips collect data such as heart rate, blood pressure, and oxygen saturation in real time. If the driver's heart rate suddenly increases while driving, the sensor collects the data. The generative AI then analyzes and predicts the collected data. The generative AI analyzes the collected data and predicts the driver's health condition. For example, it can predict the risk of myocardial infarction and stroke based on fluctuations in heart rate and blood pressure. If an abnormality is detected, an alert is issued to the driver and his or her supervisor. For example, if a driver's heart rate becomes abnormally high, the generating AI will detect the abnormality and send an alert to the driver and supervisor. This allows the driver to recognize the abnormality and take appropriate action. Furthermore, automatic braking and automatic hazard warning systems will automatically initiate an emergency stop and alert those around the vehicle to the abnormality. For example, if a driver loses consciousness, the generating AI will automatically apply the brakes and turn on the hazard lights. This will alert surrounding vehicles and pedestrians to the abnormality and prevent an accident. This system ensures the driver's health and safe driving, and protects passengers from sudden accidents. For example, if a driver suffers a myocardial infarction, the generating AI will detect the abnormality and initiate an emergency stop, ensuring passenger safety. This allows the health monitoring system to monitor the driver's health and ensure the safety of both the driver and passengers by detecting abnormalities and making an emergency stop.

[0060] A health monitoring system according to an embodiment includes a collection unit, an analysis unit, an alarm transmission unit, and a stopping unit. The collection unit collects physical information of the driver. The physical information of the driver includes, but is not limited to, heart rate, blood pressure, and oxygen saturation. For example, the collection unit collects the heart rate in real time using a sensor attached to the driver's fingertip. The collection unit can also collect blood pressure data using a sensor for measuring blood pressure. The collection unit can also collect oxygen saturation data using a sensor for measuring oxygen saturation. For example, the collection unit monitors the driver's heart rate in real time using a heart rate sensor. The blood pressure sensor periodically measures the driver's blood pressure and detects abnormal fluctuations. The oxygen saturation sensor measures the driver's oxygen saturation and issues an alert if it drops. The analysis unit analyzes the physical information collected by the collection unit and detects abnormalities. For example, the analysis unit analyzes the heart rate data using a generative AI to detect abnormal patterns. The analysis unit can also analyze blood pressure data and detect sudden fluctuations. Furthermore, the analysis unit can analyze oxygen saturation data and detect abnormalities when the data drops. For example, the analysis unit uses generative AI to analyze heart rate data and predict the risk of myocardial infarction. Analysis of blood pressure data can predict the risk of stroke. Analysis of oxygen saturation data can detect abnormalities in the respiratory system. The alarm unit issues an alert based on the abnormality detected by the analysis unit. For example, the alarm unit issues an audio alert to the driver. The alarm unit can also issue a visual alert to the manager. Furthermore, when an abnormality is detected, the alarm unit can simultaneously send an alert to the driver and the manager. For example, the alarm unit notifies the driver by audio, saying, "Your heart rate is abnormally high." Details of the abnormality are displayed to the manager as a visual alert. The stop unit automatically performs an emergency stop based on the alert issued by the alarm unit. The stop unit, for example, activates the automatic braking system to stop the vehicle. The stopping unit can also turn on the hazard lights to warn those around it of an abnormality. Furthermore, the stopping unit can automatically make an emergency stop if the driver loses consciousness.For example, the stopping unit activates an automatic braking system to safely stop the vehicle. It also turns on hazard lights to alert surrounding vehicles and pedestrians of an abnormality. If the driver loses consciousness, the stopping unit automatically makes an emergency stop to prevent an accident. As a result, the health monitoring system according to the embodiment can ensure the safety of the driver and passengers by monitoring the driver's health condition and detecting an abnormality to make an emergency stop.

[0061] The collection unit can collect data on heart rate, blood pressure, and oxygen saturation. The collection unit, for example, uses a heart rate sensor to collect the driver's heart rate in real time. The heart rate sensor is worn on the driver's fingertip and measures the heart rate. For example, if the driver's heart rate suddenly increases while driving, the heart rate sensor collects the data. The collection unit can also collect blood pressure data using a sensor for measuring blood pressure. The blood pressure sensor is worn on the driver's arm and measures blood pressure. For example, if the driver's blood pressure suddenly fluctuates while driving, the blood pressure sensor collects the data. The collection unit can also collect oxygen saturation data using a sensor for measuring oxygen saturation. The oxygen saturation sensor is worn on the driver's fingertip and measures oxygen saturation. For example, if the driver's oxygen saturation level drops while driving, the oxygen saturation sensor collects the data. This allows the driver's health condition to be monitored in detail. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI or without using a generation AI. For example, the collection unit can input data acquired by a heart rate sensor into the generation AI, which can then analyze the data and detect abnormalities.

[0062] The analysis unit can analyze the collected data and predict the risk of myocardial infarction or stroke. For example, the analysis unit uses a generating AI to analyze heart rate data and predict the risk of myocardial infarction. The generating AI detects abnormal patterns based on the heart rate data and evaluates the risk of myocardial infarction. For example, if the heart rate suddenly increases, the generating AI determines that the risk of myocardial infarction is high. The analysis unit can also analyze blood pressure data and predict the risk of stroke. The generating AI detects sudden fluctuations based on blood pressure data and evaluates the risk of stroke. For example, if blood pressure suddenly increases, the generating AI determines that the risk of stroke is high. Furthermore, the analysis unit can analyze oxygen saturation data and detect abnormalities in the respiratory system. The generating AI detects abnormalities based on a decrease in oxygen saturation data. For example, if oxygen saturation decreases, the generating AI detects an abnormality in the respiratory system. This makes it possible to predict the driver's health risks in advance. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the analysis unit can input heart rate data into the generation AI, which can then analyze the data to predict the risk of myocardial infarction.

[0063] The alarm unit can send an alert to the driver and the manager when an abnormality is detected. The alarm unit, for example, issues an audio alert to the driver. The audio alert is intended to notify the driver of an abnormality and is designed to make it easy for the driver to recognize the abnormality. For example, the alarm unit may audio-notify the driver, saying, "Your heart rate is abnormally high." The alarm unit can also issue a visual alert to the manager. The visual alert is intended to display details of the abnormality to the manager and is designed to enable the manager to respond quickly. For example, the alarm unit may issue a visual alert that displays details of the abnormality to the manager. Furthermore, the alarm unit can simultaneously send alerts to the driver and the manager when an abnormality is detected. This allows the driver and the manager to recognize the abnormality simultaneously and respond quickly. Some or all of the above-described processing in the alarm unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the alarm unit can send an alert to the driver and the manager when the generation AI detects an abnormality. This allows a quick response when an abnormality is detected.

[0064] The stopping unit can automatically apply the brakes and turn on the hazard lights when the driver loses consciousness. The stopping unit, for example, activates an automatic braking system to stop the vehicle. The automatic braking system automatically activates when the driver loses consciousness to safely stop the vehicle. For example, the stopping unit can activate the automatic braking system to stop the vehicle when the driver loses consciousness. The stopping unit can also turn on the hazard lights to notify those around it of an abnormality. Hazard lights are used to notify surrounding vehicles and pedestrians of an abnormality and play an important role in preventing accidents. For example, the stopping unit can turn on the hazard lights to notify those around it of an abnormality when the driver loses consciousness. Furthermore, the stopping unit can automatically make an emergency stop when the driver loses consciousness. This can automatically make an emergency stop when the driver loses consciousness and notify those around it of an abnormality. Some or all of the above-mentioned processing in the stopping unit can be performed, for example, using a generation AI or without using a generation AI. For example, the stopping unit can automatically apply the brakes and turn on the hazard lights if the generating AI detects that the driver has lost consciousness.

[0065] The collection unit can estimate the driver's emotions and adjust the frequency of collecting physical information based on the estimated driver's emotions. For example, if the driver is feeling stressed, the collection unit increases the collection frequency to obtain more detailed data. The collection unit can use facial expression recognition technology to estimate the driver's emotions. For example, the collection unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the stress level. The collection unit can also reduce the collection frequency to reduce the data load if the driver is relaxed. For example, the collection unit records the driver's voice, and the generation AI analyzes the voice data to estimate the driver's level of relaxation. Furthermore, if the driver is tired, the collection unit can set the collection frequency to a medium level to efficiently collect the necessary data. For example, the collection unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the driver's level of fatigue. This enables more appropriate data collection by adjusting the collection frequency according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the driver's facial expression data into the generation AI, which may analyze the data to estimate the emotion and adjust the collection frequency.

[0066] The collection unit can analyze the driver's past health data and select appropriate sensor placement. For example, the collection unit can analyze the driver's past heart rate data and determine the optimal placement of the heart rate sensor. The collection unit can refer to a health checkup database to collect the driver's past health data. For example, the collection unit acquires the driver's past health checkup results, and the generation AI analyzes the data to select the optimal sensor placement. The collection unit can also adjust the placement of the blood pressure sensor based on the driver's past blood pressure data. For example, the collection unit acquires the driver's past blood pressure data, and the generation AI analyzes the data to determine the optimal placement of the blood pressure sensor. Furthermore, the collection unit can also optimize the placement of the oxygen saturation sensor based on the driver's past oxygen saturation data. For example, the collection unit acquires the driver's past oxygen saturation data, and the generation AI analyzes the data to determine the optimal placement of the oxygen saturation sensor. This improves data accuracy by selecting the optimal sensor placement based on the driver's past health data. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the driver's past health data into the generation AI, which may then analyze the data and select optimal sensor placement.

[0067] When collecting physical information, the collection unit can filter the data based on the driver's current driving situation. For example, when the driver is driving on a highway, the collection unit filters noise from the heart rate data. The collection unit can use the vehicle's speed sensor to understand the driver's driving situation. For example, the collection unit acquires vehicle speed data, and the generation AI analyzes the data to understand the driving situation and filters noise from the heart rate data. The collection unit can also filter fluctuations in blood pressure data when the driver is driving in an urban area. For example, the collection unit acquires vehicle location data, and the generation AI analyzes the data to understand the driving situation and filters fluctuations in blood pressure data. Furthermore, when the driver is stopped, the collection unit can filter to confirm the stability of oxygen saturation data. For example, the collection unit acquires vehicle stop data, and the generation AI analyzes the data to understand the driving situation and filters to confirm the stability of the oxygen saturation data. In this way, by filtering data according to the driving situation, noise can be removed and accurate data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input driving situation data to the generation AI, and the generation AI may analyze the data and perform filtering.

[0068] The collection unit can estimate the driver's emotions and determine the priority of data to be collected based on the estimated driver's emotions. For example, if the driver is nervous, the collection unit prioritizes collecting heart rate data. The collection unit can use facial expression recognition technology to estimate the driver's emotions. For example, the collection unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the level of tension and prioritizes collecting heart rate data. The collection unit can also prioritize collecting blood pressure data if the driver is relaxed. For example, the collection unit records the driver's voice, and the generation AI analyzes the voice data to estimate the level of relaxation and prioritizes collecting blood pressure data. Furthermore, the collection unit can prioritize collecting oxygen saturation data if the driver is tired. For example, the collection unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the level of fatigue and prioritizes collecting oxygen saturation data. This allows important data to be collected preferentially by prioritizing data according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the driver's facial expression data into the generation AI, which may analyze the data to estimate emotions and determine the priority of the data to be collected.

[0069] When collecting physical information, the collection unit can prioritize collecting highly relevant data based on the driver's geographical location information. For example, when the driver is traveling in a mountainous area, the collection unit prioritizes collecting oxygen saturation data. The collection unit can use GPS data to determine the driver's geographical location information. For example, the collection unit acquires the driver's GPS data, and the generation AI analyzes the data to determine the geographical location information and prioritizes collecting oxygen saturation data. The collection unit can also prioritize collecting heart rate data when the driver is traveling in an urban area. For example, the collection unit acquires the driver's location data, and the generation AI analyzes the data to determine the geographical location information and prioritizes collecting heart rate data. Furthermore, the collection unit can prioritize collecting blood pressure data when the driver is traveling a long distance. For example, the collection unit acquires the driver's location data, and the generation AI analyzes the data to determine the geographical location information and prioritizes collecting blood pressure data. This enables more accurate health monitoring by prioritizing the collection of highly relevant data based on geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the driver's GPS data into the generation AI, which may then analyze the data and preferentially collect highly relevant data.

[0070] When collecting physical information, the collection unit can analyze the driver's online activities and collect related data. For example, if the driver posts on social media that he or she is stressed, the collection unit prioritizes collecting heart rate data. The collection unit can use social media posting data to understand the driver's online activities. For example, the collection unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the stress level and prioritizes collecting heart rate data. The collection unit can also prioritize collecting blood pressure data if the driver posts that he or she is relaxed. For example, the collection unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the level of relaxation and prioritizes collecting blood pressure data. Furthermore, the collection unit can also prioritize collecting oxygen saturation data if the driver posts that he or she is tired. For example, the collection unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the level of fatigue and prioritizes collecting oxygen saturation data. This makes it possible to monitor the driver's health condition in more detail by collecting related data based on social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input data posted by drivers on social media into the generation AI, which may then analyze the data and collect related data.

[0071] The analysis unit can estimate the driver's emotions and adjust the analysis algorithm based on the estimated driver's emotions. For example, if the driver is nervous, the analysis unit adjusts the analysis algorithm for heart rate data. The analysis unit can use facial expression recognition technology to estimate the driver's emotions. For example, the analysis unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the level of tension and adjusts the analysis algorithm for heart rate data. The analysis unit can also adjust the analysis algorithm for blood pressure data if the driver is relaxed. For example, the analysis unit records the driver's voice, and the generation AI analyzes the voice data to estimate the level of relaxation and adjusts the analysis algorithm for blood pressure data. Furthermore, the analysis unit can adjust the analysis algorithm for oxygen saturation data if the driver is tired. For example, the analysis unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the level of fatigue and adjusts the analysis algorithm for oxygen saturation data. This allows for more accurate analysis by adjusting the analysis algorithm according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the driver's facial expression data into the generation AI, which may analyze the data to estimate the emotion and adjust the analysis algorithm.

[0072] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, if heart rate data is abnormal, the analysis unit performs a detailed analysis. The analysis unit can use a generation AI to evaluate the importance of the collected data. For example, the analysis unit inputs heart rate data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. The analysis unit can also perform a detailed analysis if blood pressure data is abnormal. For example, the analysis unit inputs blood pressure data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. The analysis unit can also perform a detailed analysis if oxygen saturation data is abnormal. For example, the analysis unit inputs oxygen saturation data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. This enables efficient analysis by adjusting the level of detail of the analysis depending on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input collected data into the generation AI, which can then analyze the data, evaluate its importance, and adjust the level of detail of the analysis.

[0073] During analysis, the analysis unit can apply different analysis algorithms depending on the driver's health history. For example, if the driver has previously experienced a myocardial infarction, the analysis unit adjusts the analysis algorithm for the heart rate data. The analysis unit can refer to a health checkup database to understand the driver's health history. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of myocardial infarction and adjusts the analysis algorithm for the heart rate data. The analysis unit can also adjust the analysis algorithm for the blood pressure data if the driver has a history of high blood pressure. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of high blood pressure and adjusts the analysis algorithm for the blood pressure data. Furthermore, the analysis unit can adjust the analysis algorithm for the oxygen saturation data if the driver has a respiratory disease. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of respiratory disease and adjusts the analysis algorithm for the oxygen saturation data. This allows for more accurate analysis by adjusting the analysis algorithm based on the driver's health history. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit may input the driver's health history data into the generation AI, which may then analyze the data and adjust the analysis algorithm.

[0074] The analysis unit can estimate the driver's emotions and adjust the display method of the analysis results based on the estimated driver's emotions. For example, if the driver is nervous, the analysis unit provides a simple, highly visible display method. The analysis unit can use facial expression recognition technology to estimate the driver's emotions. For example, the analysis unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the level of nervousness and provides a simple, highly visible display method. The analysis unit can also provide a display method that includes detailed information if the driver is relaxed. For example, the analysis unit records the driver's voice, and the generation AI analyzes the voice data to estimate the level of relaxation and provides a display method that includes detailed information. Furthermore, the analysis unit can provide a display method that focuses on the key points if the driver is in a hurry. For example, the analysis unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the level of hurry and provides a display method that focuses on the key points. This allows for a highly visible display by adjusting the display method of the analysis results according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the driver's facial expression data into the generation AI, which analyzes the data to estimate the emotion and adjust the display method of the analysis results.

[0075] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recently collected data. The analysis unit can refer to the database timestamp to determine the time when the data was collected. For example, the analysis unit obtains the database timestamp, and the generation AI analyzes the data to determine the time when the data was collected, and prioritizes analyzing the most recently collected data. The analysis unit can also refer to past data while emphasizing the most recent data. For example, the analysis unit obtains past data, and the generation AI analyzes the data to determine the time when the data was collected, and prioritizes analyzing the most recent data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific time period. For example, the analysis unit obtains data collected during a specific time period, and the generation AI analyzes the data to determine the time when the data was collected, and prioritizes analyzing the data collected during the specific time period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI, and the generation AI can analyze the data and determine the priority of the analysis.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit adjusts the order of analysis taking into account the relevance between heart rate data and blood pressure data. The analysis unit can use a generation AI to evaluate the relevance of the data. For example, the analysis unit inputs heart rate data and blood pressure data into the generation AI, which analyzes the data, evaluates the relevance, and adjusts the order of analysis. The analysis unit can also adjust the order of analysis taking into account the relevance between oxygen saturation data and heart rate data. For example, the analysis unit inputs oxygen saturation data and heart rate data into the generation AI, which analyzes the data, evaluates the relevance, and adjusts the order of analysis. The analysis unit can also adjust the order of analysis taking into account the relevance between blood pressure data and oxygen saturation data. For example, the analysis unit inputs blood pressure data and oxygen saturation data into the generation AI, which analyzes the data, evaluates the relevance, and adjusts the order of analysis. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data relevance to the generation AI, which can then analyze the data and adjust the order of analysis.

[0077] The alarm issuing unit can estimate the driver's emotions and adjust the way the alert is presented based on the estimated driver's emotions. For example, if the driver is nervous, the alarm issuing unit displays a simple, highly visible alert. The alarm issuing unit can use facial expression recognition technology to estimate the driver's emotions. For example, the alarm issuing unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the level of nervousness and displays a simple, highly visible alert. The alarm issuing unit can also display an alert with detailed information if the driver is relaxed. For example, the alarm issuing unit records the driver's voice, and the generation AI analyzes the voice data to estimate the level of relaxation and displays an alert with detailed information. Furthermore, the alarm issuing unit can display an alert that focuses on the key points if the driver is in a hurry. For example, the alarm issuing unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the level of hurry and displays an alert that focuses on the key points. This allows for a highly visible alert by adjusting the way the alert is expressed depending on the driver's emotions. Emotion estimation is achieved, for example, by using an emotion estimation function using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the alarm issuing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the alarm issuing unit can input facial expression data of the driver into the generation AI, which analyzes the data to estimate the emotion and adjust the way the alert is expressed.

[0078] When issuing an alert, the alert issuing unit can adjust the level of detail of the alert based on the severity of the anomaly. For example, if a serious anomaly is detected, the alert issuing unit issues a detailed alert. The alert issuing unit can use a generation AI to evaluate the severity of the anomaly. For example, the alert issuing unit inputs abnormality data to the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues a detailed alert. The alert issuing unit can also issue a concise alert if a minor anomaly is detected. For example, the alert issuing unit inputs abnormality data to the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues a concise alert. Furthermore, the alert issuing unit can also issue an alert with a moderate level of detail if a moderate anomaly is detected. For example, the alert issuing unit inputs abnormality data to the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues an alert with a moderate level of detail. This allows appropriate responses to be taken by adjusting the level of detail of the alert depending on the severity of the anomaly. Some or all of the above-described processing in the alert issuing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the alert issuing unit may input abnormality data to the generation AI, which may analyze the data to evaluate the importance of the abnormality and adjust the level of detail of the alert.

[0079] When issuing an alert, the alert issuing unit can apply different alert algorithms depending on the category of the abnormality. For example, if an abnormality in heart rate is detected, the alert issuing unit applies an alert algorithm specialized for heart rate. The alert issuing unit can use a generation AI to classify the category of the abnormality. For example, the alert issuing unit inputs abnormal data to the generation AI, which analyzes the data to classify the category of the abnormality and applies an alert algorithm specialized for heart rate. Furthermore, if an abnormality in blood pressure is detected, the alert issuing unit can also apply an alert algorithm specialized for blood pressure. For example, the alert issuing unit inputs abnormal data to the generation AI, which analyzes the data to classify the category of the abnormality and applies an alert algorithm specialized for blood pressure. Furthermore, if an abnormality in oxygen saturation is detected, the alert issuing unit can also apply an alert algorithm specialized for oxygen saturation. For example, the alert issuing unit inputs abnormal data to the generation AI, which analyzes the data to classify the category of the abnormality and applies an alert algorithm specialized for oxygen saturation. This makes it possible to issue an optimal alert according to the category of the abnormality. Some or all of the above-described processing in the alarm issuing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the alarm issuing unit may input abnormality data to the generation AI, which may analyze the data, classify the abnormality into categories, and apply an alert algorithm.

[0080] The alarm issuing unit can estimate the driver's emotions and determine the priority of alerts based on the estimated driver's emotions. For example, if the driver is nervous, the alarm issuing unit prioritizes displaying important alerts. The alarm issuing unit can use facial expression recognition technology to estimate the driver's emotions. For example, the alarm issuing unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the level of tension and prioritizes displaying important alerts. The alarm issuing unit can also prioritize displaying detailed alerts if the driver is relaxed. For example, the alarm issuing unit records the driver's voice, and the generation AI analyzes the voice data to estimate the level of relaxation and prioritizes displaying detailed alerts. Furthermore, the alarm issuing unit can prioritize displaying alerts that focus on the main points if the driver is in a hurry. For example, the alarm issuing unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the level of hurry and prioritizes displaying alerts that focus on the main points. This allows important alerts to be displayed preferentially by determining the priority of alerts according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the alarm issuing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the alarm issuing unit can input facial expression data of the driver into the generation AI, which then analyzes the data to estimate the emotion and determine the priority of the alert.

[0081] When issuing an alert, the alert issuing unit can adjust the order of alerts based on the time of the anomaly occurrence. For example, the alert issuing unit prioritizes alerts for recently occurring anomalies. The alert issuing unit can refer to a timestamp in a database to determine when the anomaly occurred. For example, the alert issuing unit acquires a timestamp in a database, and the generation AI analyzes the data to determine the time of occurrence, and prioritizes alerts for recently occurring anomalies. The alert issuing unit can also emphasize the most recent anomaly while referring to past anomalies. For example, the alert issuing unit acquires past anomaly data, and the generation AI analyzes the data to determine the time of occurrence, and prioritizes alerts for the most recent anomaly. Furthermore, the alert issuing unit can prioritize alerts for anomalies that occurred during a specific time period. For example, the alert issuing unit acquires anomaly data that occurred during a specific time period, and the generation AI analyzes the data to determine the time of occurrence, and prioritizes alerts for anomalies that occurred during a specific time period. This allows the most recent anomaly to be addressed by adjusting the order of alerts based on the time of occurrence of the anomaly. Some or all of the above-described processing in the alert issuing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the alarm issuing unit can input the time when an abnormality occurred into the generation AI, which can then analyze the data and adjust the order of alerts.

[0082] When issuing an alert, the alert issuing unit can adjust the display method of the alert based on the correlation of the abnormalities. For example, if an abnormal heart rate and an abnormal blood pressure are correlated, the alert displays an alert that takes the correlation into consideration. The alert issuing unit can use a generation AI to evaluate the correlation of the abnormalities. For example, the alert issuing unit inputs heart rate data and blood pressure data into the generation AI, which analyzes the data to evaluate the correlation and displays an alert that takes the correlation into consideration. The alert issuing unit can also display an alert that takes the correlation into consideration if an abnormal oxygen saturation and an abnormal heart rate are correlated. For example, the alert issuing unit inputs oxygen saturation data and heart rate data into the generation AI, which analyzes the data to evaluate the correlation and displays an alert that takes the correlation into consideration. The alert issuing unit can also display an alert that takes the correlation into consideration if an abnormal blood pressure and an abnormal oxygen saturation are correlated. For example, the alert issuing unit inputs blood pressure data and oxygen saturation data into the generation AI, which analyzes the data to evaluate the correlation and displays an alert that takes the correlation into consideration. This allows related abnormalities to be identified at a glance by adjusting the display method of the alert based on the correlation of the abnormalities. Some or all of the above-described processing in the alert issuing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the alert issuing unit may input abnormality data to the generation AI, which may analyze the data, evaluate relevance, and adjust the alert display method.

[0083] The stopping unit can estimate the driver's emotions and adjust the emergency stop method based on the estimated driver's emotions. For example, if the driver is nervous, the stopping unit performs a gradual emergency stop. The stopping unit can use facial expression recognition technology to estimate the driver's emotions. For example, the stopping unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the level of tension and performs a gradual emergency stop. The stopping unit can also perform a normal emergency stop if the driver is relaxed. For example, the stopping unit records the driver's voice, and the generation AI analyzes the voice data to estimate the level of relaxation and performs a normal emergency stop. Furthermore, the stopping unit can also perform a rapid emergency stop if the driver is in a hurry. For example, the stopping unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the level of hurry and performs a rapid emergency stop. This enables safer stops by adjusting the emergency stop method according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the stopping unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the stopping unit may input facial expression data of the driver into the generation AI, which may analyze the data to estimate the emotion and adjust the emergency stop method.

[0084] The stopping unit can analyze the driver's past driving history to select the optimal stopping method during an emergency stop. For example, if the driver has previously experienced an emergency stop, the stopping unit can select the optimal stopping method by referring to the data from that experience. The stopping unit can refer to a driving history database to understand the driver's past driving history. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to select the optimal stopping method. The stopping unit can also analyze the driver's reaction time during an emergency stop from the driver's past driving history and select the optimal stopping method. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to evaluate the reaction time and select the optimal stopping method. Furthermore, the stopping unit can adjust the braking method during an emergency stop based on the driver's past driving history. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to evaluate the braking method and select the optimal stopping method. This enables safer stopping by selecting the optimal stopping method based on the driver's past driving history. Some or all of the above-described processing in the stopping unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the stopping unit may input the driver's past driving history data into the generation AI, which may then analyze the data and select the optimal stopping method.

[0085] In the event of an emergency stop, the stopping unit can customize the stopping means based on the driver's current driving situation. For example, when the driver is driving on a highway, the stopping unit applies gentle brakes to stop the vehicle. The stopping unit can use the vehicle's speed sensor to grasp the driver's current driving situation. For example, the stopping unit acquires vehicle speed data, and the generation AI analyzes the data to understand the driving situation and applies gentle brakes to stop the vehicle. The stopping unit can also quickly apply the brakes to stop the vehicle when the driver is driving in an urban area. For example, the stopping unit acquires vehicle position data, and the generation AI analyzes the data to understand the driving situation and quickly apply the brakes to stop the vehicle. Furthermore, when the driver is stopped, the stopping unit can turn on the hazard lights to alert those around the vehicle of an abnormality. For example, the stopping unit acquires vehicle stop data, and the generation AI analyzes the data to understand the driving situation and turns on the hazard lights to alert those around the vehicle of an abnormality. This enables safer stopping by customizing the stopping means based on the driver's current driving situation. Some or all of the above-described processing in the stopping unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the stopping unit may input driving situation data to the generation AI, and the generation AI may analyze the data and customize the stopping means.

[0086] The stopping unit can estimate the driver's emotions and determine the priority of emergency stops based on the estimated driver's emotions. For example, if the driver is nervous, the stopping unit sets the priority of emergency stops to high. The stopping unit can use facial expression recognition technology to estimate the driver's emotions. For example, the stopping unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the level of tension and sets the priority of emergency stops to high. The stopping unit can also set the priority of emergency stops to normal if the driver is relaxed. For example, the stopping unit records the driver's voice, and the generation AI analyzes the voice data to estimate the level of relaxation and sets the priority of emergency stops to normal. Furthermore, the stopping unit can also set the priority of emergency stops to the highest if the driver is in a hurry. For example, the stopping unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the level of hurry and sets the priority of emergency stops to the highest. This allows for prioritizing emergency stops according to the driver's emotions, thereby prioritizing important emergency stops. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the stopping unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the stopping unit can input facial expression data of the driver into the generation AI, which then analyzes the data to estimate the emotion and determine the priority of emergency stops.

[0087] In the event of an emergency stop, the stopping unit can select the optimal stopping method by taking into account the driver's geographical location information. For example, if the driver is driving in a mountainous area, the stopping unit applies gentle brakes to stop the vehicle. The stopping unit can use GPS data to determine the driver's geographical location. For example, the stopping unit acquires the driver's GPS data, and the generating AI analyzes the data to determine the geographical location and applies gentle brakes to stop the vehicle. The stopping unit can also quickly apply the brakes to stop the vehicle when the driver is driving in an urban area. For example, the stopping unit acquires the driver's location data, and the generating AI analyzes the data to determine the geographical location and quickly apply the brakes to stop the vehicle. Furthermore, if the driver is driving on a highway, the stopping unit can turn on the hazard lights to alert those around the vehicle of an abnormality. For example, the stopping unit acquires the driver's location data, and the generating AI analyzes the data to determine the geographical location and turns on the hazard lights to alert those around the vehicle of an abnormality. This enables safer stopping by selecting the optimal stopping method based on the driver's geographical location information. Some or all of the above-described processing in the stopping unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the stopping unit may input the driver's GPS data into the generation AI, which may then analyze the data and select the optimal stopping method.

[0088] In the event of an emergency stop, the stopping unit can analyze the driver's social media activity and suggest stopping measures. For example, if the driver posts on social media that he or she is stressed, the stopping unit applies gentle brakes to stop the vehicle. The stopping unit can use social media posting data to understand the driver's social media activity. For example, the stopping unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the stress level and applies gentle brakes to stop the vehicle. The stopping unit can also apply normal brakes to stop the vehicle if the driver posts that he or she is relaxed. For example, the stopping unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the level of relaxation and applies normal brakes to stop the vehicle. Furthermore, the stopping unit can quickly apply brakes to stop the vehicle if the driver posts that he or she is tired. For example, the stopping unit acquires the driver's social media posting data, and the generation AI analyzes the data to estimate the level of fatigue and applies the brakes quickly to stop the vehicle. This enables more appropriate stopping by suggesting stopping measures based on the driver's social media activity. Some or all of the above-described processing in the stopping unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the stopping unit may input data posted by the driver on social media into the generation AI, which may then analyze the data and suggest stopping measures. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, alarm issuing unit, and stop unit, described above, is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses sensors in the smart device 14 to collect data such as the driver's heart rate, blood pressure, and oxygen saturation in real time. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI to detect abnormalities. The alarm issuing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and issues an alert to the driver and manager when an abnormality is detected. The stop unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and activates an automatic braking system to stop the vehicle. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, alarm issuing unit, and stop unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses sensors in the smart glasses 214 to collect data such as the driver's heart rate, blood pressure, and oxygen saturation in real time. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI to detect abnormalities. The alarm issuing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and issues an alert to the driver and manager when an abnormality is detected. The stop unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and activates an automatic braking system to stop the vehicle. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, alarm issuing unit, and stop unit, described above, is realized by, for example, at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit uses sensors in the headset-type terminal 314 to collect data such as the driver's heart rate, blood pressure, and oxygen saturation in real time. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI to detect abnormalities. The alarm issuing unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and issues an alert to the driver and a manager when an abnormality is detected. The stop unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and activates an automatic braking system to stop the vehicle. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, alarm issuing unit, and stopping unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses sensors in the robot 414 to collect data such as the driver's heart rate, blood pressure, and oxygen saturation in real time. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI to detect abnormalities. The alarm issuing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and issues an alert to the driver and a manager when an abnormality is detected. The stopping unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and activates an automatic braking system to stop the vehicle.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The collection unit not only collects the driver's physical information, but also analyzes the driver's past health data and selects appropriate sensor placement. For example, it analyzes the driver's past heart rate data and determines the optimal placement of the heart rate sensor. The collection unit can refer to a health checkup database to collect the driver's past health data. For example, the collection unit acquires the driver's past health checkup results, and the generation AI analyzes the data to select the optimal sensor placement. The collection unit can also adjust the placement of the blood pressure sensor based on the driver's past blood pressure data. For example, the collection unit acquires the driver's past blood pressure data, and the generation AI analyzes the data to determine the optimal placement of the blood pressure sensor. Furthermore, the collection unit can also optimize the placement of the oxygen saturation sensor based on the driver's past oxygen saturation data. For example, the collection unit acquires the driver's past oxygen saturation data, and the generation AI analyzes the data to determine the optimal placement of the oxygen saturation sensor. This improves data accuracy by selecting the optimal sensor placement based on the driver's past health data. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the driver's past health data into the generation AI, which may then analyze the data and select optimal sensor placement.

[0091] The analysis unit not only analyzes the collected data, but also applies different analysis algorithms depending on the driver's health history. For example, if the driver has previously experienced a myocardial infarction, it adjusts the analysis algorithm for heart rate data. The analysis unit can refer to a health checkup database to understand the driver's health history. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of myocardial infarction and adjusts the analysis algorithm for heart rate data. The analysis unit can also adjust the analysis algorithm for blood pressure data if the driver has a history of high blood pressure. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of high blood pressure and adjusts the analysis algorithm for blood pressure data. Furthermore, the analysis unit can adjust the analysis algorithm for oxygen saturation data if the driver has a respiratory disease. For example, the analysis unit obtains the driver's past health checkup results, and the generation AI analyzes the data to assess the risk of respiratory disease and adjusts the analysis algorithm for oxygen saturation data. This allows for more accurate analysis by adjusting the analysis algorithm based on the driver's health history. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit may input the driver's health history data into the generation AI, which may then analyze the data and adjust the analysis algorithm.

[0092] The alerting unit not only issues an alert when an anomaly is detected, but can also adjust the level of detail of the alert based on the severity of the anomaly. For example, if a serious anomaly is detected, a detailed alert is issued. The alerting unit can use a generation AI to evaluate the severity of the anomaly. For example, the alerting unit inputs abnormality data into the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues a detailed alert. The alerting unit can also issue a concise alert when a minor anomaly is detected. For example, the alerting unit inputs abnormality data into the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues a concise alert. Furthermore, the alerting unit can also issue an alert with a moderate level of detail when a moderate anomaly is detected. For example, the alerting unit inputs abnormality data into the generation AI, which analyzes the data to evaluate the severity of the anomaly and issues an alert with a moderate level of detail. This allows appropriate responses to be taken by adjusting the level of detail of the alert depending on the severity of the anomaly. Some or all of the above-described processing in the alert issuing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the alert issuing unit may input abnormality data to the generation AI, which may analyze the data to evaluate the importance of the abnormality and adjust the level of detail of the alert.

[0093] The stopping unit not only automatically applies the brakes when the driver loses consciousness, but can also analyze the driver's past driving history to select the optimal stopping method. For example, if the driver has previously experienced an emergency stop, the optimal stopping method can be selected based on the data from that experience. The stopping unit can refer to a driving history database to understand the driver's past driving history. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to select the optimal stopping method. The stopping unit can also analyze the driver's reaction time in an emergency stop from the driver's past driving history and select the optimal stopping method. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to evaluate the reaction time and select the optimal stopping method. Furthermore, the stopping unit can adjust the braking method in an emergency stop based on the driver's past driving history. For example, the stopping unit acquires the driver's past driving history data, and the generation AI analyzes the data to evaluate the braking method and select the optimal stopping method. This enables safer stopping by selecting the optimal stopping method based on the driver's past driving history. Some or all of the above-described processing in the stopping unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the stopping unit may input the driver's past driving history data into the generation AI, which may then analyze the data and select the optimal stopping method.

[0094] The collection unit can estimate the driver's emotions and adjust the frequency of collecting physical information based on the estimated driver's emotions. For example, if the driver is feeling stressed, the collection frequency can be increased to obtain more detailed data. The collection unit can use facial expression recognition technology to estimate the driver's emotions. For example, the collection unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the stress level. The collection unit can also reduce the collection frequency to reduce the data load if the driver is relaxed. For example, the collection unit records the driver's voice, and the generation AI analyzes the voice data to estimate the driver's level of relaxation. Furthermore, if the driver is tired, the collection unit can set the collection frequency to a medium level to efficiently collect the necessary data. For example, the collection unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the driver's fatigue level. This allows for more appropriate data collection by adjusting the collection frequency according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input the driver's facial expression data into the generation AI, which may analyze the data to estimate the driver's emotions and adjust the collection frequency.

[0095] The analysis unit can estimate the driver's emotions and adjust the analysis algorithm based on the estimated driver's emotions. For example, if the driver is nervous, it adjusts the analysis algorithm for heart rate data. The analysis unit can use facial expression recognition technology to estimate the driver's emotions. For example, the analysis unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the level of nervousness and adjusts the analysis algorithm for heart rate data. The analysis unit can also adjust the analysis algorithm for blood pressure data if the driver is relaxed. For example, the analysis unit records the driver's voice, and the generation AI analyzes the voice data to estimate the level of relaxation and adjusts the analysis algorithm for blood pressure data. Furthermore, the analysis unit can adjust the analysis algorithm for oxygen saturation data if the driver is tired. For example, the analysis unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the level of fatigue and adjusts the analysis algorithm for oxygen saturation data. This allows for more accurate analysis by adjusting the analysis algorithm according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the driver's facial expression data into the generation AI, which may analyze the data to estimate the emotion and adjust the analysis algorithm.

[0096] The alarm unit can estimate the driver's emotions and adjust the way the alert is presented based on the estimated driver's emotions. For example, if the driver is nervous, a simple, highly visible alert is displayed. The alarm unit can use facial expression recognition technology to estimate the driver's emotions. For example, the alarm unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the level of nervousness and display a simple, highly visible alert. The alarm unit can also display an alert with detailed information if the driver is relaxed. For example, the alarm unit records the driver's voice, and the generation AI analyzes the voice data to estimate the level of relaxation and display an alert with detailed information. Furthermore, the alarm unit can display an alert that focuses on the key points if the driver is in a hurry. For example, the alarm unit collects the driver's biometric data (heart rate and electrodermal activity) with a sensor, and the generation AI analyzes the data to estimate the level of hurry and display an alert that focuses on the key points. This enables highly visible alerts to be displayed by adjusting the way the alert is presented based on the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the alarm issuing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the alarm issuing unit inputs the driver's facial expression data into the generation AI, which analyzes the data to estimate the emotion and adjusts the way the alert is expressed.

[0097] The stopping unit can estimate the driver's emotions and adjust the emergency stop method based on the estimated driver's emotions. For example, if the driver is nervous, a gradual emergency stop is performed. The stopping unit can use facial expression recognition technology to estimate the driver's emotions. For example, the stopping unit captures the driver's facial expressions with a camera, and the generation AI analyzes the facial expression data to estimate the level of tension and then performs a gradual emergency stop. The stopping unit can also perform a normal emergency stop if the driver is relaxed. For example, the stopping unit records the driver's voice, and the generation AI analyzes the voice data to estimate the level of relaxation and then performs a normal emergency stop. Furthermore, the stopping unit can also perform a rapid emergency stop if the driver is in a hurry. For example, the stopping unit collects the driver's biometric data (heart rate and electrodermal activity) with sensors, and the generation AI analyzes the data to estimate the level of hurry and then performs a rapid emergency stop. This enables safer stops by adjusting the emergency stop method according to the driver's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the stopping unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the stopping unit may input facial expression data of the driver into the generation AI, which may analyze the data to estimate the emotion and adjust the emergency stop method.

[0098] When collecting physical information, the collection unit can filter the data based on the driver's current driving situation. For example, if the driver is driving on a highway, the collection unit filters noise from the heart rate data. The collection unit can use the vehicle's speed sensor to understand the driver's driving situation. For example, the collection unit acquires vehicle speed data, and the generation AI analyzes the data to understand the driving situation and filters noise from the heart rate data. The collection unit can also filter fluctuations in blood pressure data when the driver is driving in an urban area. For example, the collection unit acquires vehicle location data, and the generation AI analyzes the data to understand the driving situation and filters fluctuations in blood pressure data. Furthermore, the collection unit can filter oxygen saturation data to confirm its stability when the driver is stopped. For example, the collection unit acquires vehicle stop data, and the generation AI analyzes the data to understand the driving situation and filters oxygen saturation data to confirm its stability. In this way, by filtering data according to the driving situation, noise can be removed and accurate data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input driving situation data to the generation AI, and the generation AI may analyze the data and perform filtering.

[0099] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, if heart rate data is abnormal, a detailed analysis is performed. The analysis unit can use a generation AI to evaluate the importance of the collected data. For example, the analysis unit inputs heart rate data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. The analysis unit can also perform a detailed analysis if blood pressure data is abnormal. For example, the analysis unit inputs blood pressure data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. The analysis unit can also perform a detailed analysis if oxygen saturation data is abnormal. For example, the analysis unit inputs oxygen saturation data to the generation AI, which analyzes the data to detect abnormalities and performs a detailed analysis. This enables efficient analysis by adjusting the level of detail of the analysis depending on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input collected data into the generation AI, which can then analyze the data, evaluate its importance, and adjust the level of detail of the analysis.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects the driver's physical information, which includes heart rate, blood pressure, oxygen saturation, etc. The collection unit collects the driver's heart rate in real time using a sensor attached to the driver's fingertip, blood pressure data using a blood pressure sensor, and oxygen saturation data using an oxygen saturation sensor. Step 2: The analysis unit analyzes the physical information collected by the collection unit and detects abnormalities. Using the generative AI, the analysis unit analyzes heart rate data to detect abnormal patterns, blood pressure data to detect sudden fluctuations, and oxygen saturation data to detect declines. Step 3: The alarm unit issues an alert based on the abnormality detected by the analysis unit. The alarm unit issues an audio alert to the driver and a visual alert to the manager, and if an abnormality is detected, it sends an alert to both the driver and the manager simultaneously. Step 4: The stopping unit automatically makes an emergency stop based on the alert issued by the alarm unit. The stopping unit activates the automatic braking system to stop the vehicle, turns on the hazard lights to alert those around it of the abnormality, and automatically makes an emergency stop if the driver loses consciousness.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] 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.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] 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.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 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 collection unit for collecting physical information of the driver; an analysis unit that analyzes the physical information collected by the collection unit and detects abnormalities; an alert issuing unit that issues an alert based on the abnormality detected by the analysis unit; a stop unit that automatically performs an emergency stop based on the alert issued by the alert issuing unit; Equipped with A system characterized by:

2. The collecting unit Collects heart rate, blood pressure, and oxygen saturation data The system of claim 1 .

3. The analysis unit Analyzing the collected data to predict the risk of myocardial infarction or stroke The system of claim 1 .

4. The alarm issuing unit If an abnormality is detected, an alert is sent to the driver and manager. The system of claim 1 .

5. The stop portion is If the driver loses consciousness, the system automatically applies the brakes and turns on the hazard lights. The system of claim 1 .

6. The collecting unit Estimate the driver's emotions and adjust the frequency of collecting physical information based on the estimated driver's emotions. The system of claim 1 .

7. The collecting unit Analyzing the driver's past health data and selecting appropriate sensor placement The system of claim 1 .

8. The collecting unit When collecting physical information, the data is filtered based on the driver's current driving situation. The system of claim 1 .

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