System

Abstract

An object of the system according to the embodiment is to prevent an accident by managing a driver's drowsiness, carelessness, and health condition in real time.SOLUTION: A system includes a situation monitoring part, a health management part, a warning providing part, a data recording part, and a communication part. The situation monitoring unit monitors the motion of the face or body of the driver using the AI camera. The health management unit collects vital data of the driver and evaluates the health condition. The warning providing section provides an appropriate warning or advice based on the data obtained by the situation monitoring section and the health management section. The data recorder records data relating to the condition and health of the driver for later analysis. The communication unit communicates with the driver in real time using a AI communication tool.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately monitor long-distance drivers' drowsiness, inattention, and health status in real time to prevent accidents, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent accidents by managing the driver's drowsiness, inattention, and health condition in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a situation monitoring unit, a health management unit, a warning provision unit, a data recording unit, and a communication unit. The situation monitoring unit monitors the driver's facial and body movements using an AI camera. The health management unit collects the driver's vital signs data and evaluates his / her health condition. The warning provision unit provides appropriate warnings and advice based on the data obtained by the situation monitoring unit and the health management unit. The data recording unit records data related to the driver's situation and health condition for later analysis. The communication unit communicates with the driver in real time using an AI communication tool. [Effects of the Invention]

[0007] The system according to the embodiment can prevent accidents by managing the driver's drowsiness, inattention, and health condition in real time. [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) The SMART DRIVE AI system according to an embodiment of the present invention is a system that manages the driver's drowsiness, inattention, and health condition to prevent accidents. As a result, the SMART DRIVE AI system can monitor the driver's condition in real time and provide warnings and advice as necessary.

[0029] The SMART DRIVE AI system according to the present embodiment includes a situation monitoring unit, a health management unit, a warning provision unit, a data recording unit, and a communication unit. The situation monitoring unit monitors the driver's facial and body movements. For example, an AI camera can be used to detect the frequency with which the driver closes their eyes and tilts their head, and the generation AI then determines signs of drowsiness. The situation monitoring unit can also analyze the driver's facial expressions and movements to detect signs of inattention. The health management unit collects the driver's vital signs and evaluates their health status. For example, the generation AI can monitor their heart rate and body temperature to determine whether they are fatigued or if their health status changes. The health management unit can also detect driver fatigue due to long driving periods and encourage them to take appropriate breaks. The warning provision unit provides appropriate warnings and advice based on the data obtained by the situation monitoring unit and the health management unit. For example, if signs of drowsiness are detected, the warning provision unit issues a warning such as "Please take a break." If their health status is deteriorating, the warning provision unit provides advice such as "Please hydrate" and "Please stretch." The data recording unit records data related to the driver's situation and health status for later analysis. For example, by analyzing past data, the system can identify situations in which accidents are likely to occur and take preventative measures. The communication unit communicates with the driver in real time using AI communication tools. For example, if the driver has a question or wants to consult, the generation AI provides appropriate answers and advice. This allows the SMART DRIVE AI system according to the embodiment to manage the driver's drowsiness, inattention, and health condition, preventing accidents before they occur. For example, if the driver has been driving for a long time, the generation AI can encourage the driver to take a break at the appropriate time, preventing accidents caused by fatigue. Furthermore, if the driver's health condition deteriorates, the generation AI can provide appropriate advice to maintain the driver's health.

[0030] The situation monitoring unit can use the driver's facial recognition data to customize and detect signs of drowsiness based on the characteristics of each individual driver. For example, the situation monitoring unit uses the driver's facial recognition data to analyze the eye opening and closing patterns and changes in facial expression of each individual driver to customize and detect signs of drowsiness. For example, if a particular driver frequently closes their eyes, the risk of drowsiness is assessed based on that pattern. The situation monitoring unit also learns the facial features of each individual driver based on the driver's facial recognition data to customize and detect signs of drowsiness. For example, if a particular driver has a unique facial expression when fatigued, the risk of drowsiness is assessed based on that facial expression. The situation monitoring unit also uses the driver's facial recognition data to analyze the eye movement and blinking patterns of each individual driver to customize and detect signs of drowsiness. For example, if a particular driver frequently closes their eyes, the risk of drowsiness is assessed based on that pattern. This improves detection accuracy by detecting signs of drowsiness based on the characteristics of each individual driver.

[0031] The situation monitoring unit can analyze the driver's past driving data and predict the risk of dozing off at the wheel under specific time periods and environmental conditions. For example, the situation monitoring unit analyzes the driver's past driving data and predicts the risk of dozing off at the wheel under specific time periods and environmental conditions. For example, if the risk of dozing off is higher at night or during long driving periods, the situation monitoring unit issues a warning during those periods. The situation monitoring unit also predicts the risk of dozing off at the wheel under specific environmental conditions (e.g., weather or road conditions) based on the driver's past driving data. For example, if the risk of dozing off is higher during rainy weather or traffic jams, the situation monitoring unit issues a warning under those conditions. The situation monitoring unit also analyzes the driver's past driving data and predicts the risk of dozing off at the wheel under specific driving patterns (e.g., driving at a constant speed for a long period of time). For example, if the risk of dozing off is higher during long driving periods on a highway, the situation monitoring unit issues a warning based on that pattern. In this way, by predicting the risk of dozing off based on past driving data, preventive measures can be taken.

[0032] The situation monitoring unit can detect abnormal driving patterns by monitoring vehicle behavior in addition to monitoring the driver's situation. For example, in addition to monitoring the driver's situation, the situation monitoring unit monitors vehicle behavior (e.g., sudden braking or abrupt steering) to detect abnormal driving patterns. For example, if the frequency of sudden braking is high, the risk of drowsiness or inattention is evaluated. Furthermore, in addition to monitoring the driver's situation, the situation monitoring unit monitors vehicle behavior (e.g., sudden acceleration or sudden deceleration) to detect abnormal driving patterns. For example, if the frequency of sudden acceleration is high, the risk of drowsiness or inattention is evaluated. Furthermore, in addition to monitoring the driver's situation, the situation monitoring unit monitors vehicle behavior (e.g., lane departure or snaking driving) to detect abnormal driving patterns. For example, if lane departure is frequent, the risk of drowsiness or inattention is evaluated. In this way, abnormal driving patterns can be detected by monitoring vehicle behavior.

[0033] The situation monitoring unit can integrate the driver's situation monitoring data with other vehicle data to perform risk assessment. For example, the situation monitoring unit integrates the driver's situation monitoring data with other vehicle data (e.g., GPS data and traffic information) to perform a comprehensive risk assessment. For example, the risk assessment is performed based on congestion information and road construction information. The situation monitoring unit also integrates the driver's situation monitoring data with other vehicle data (e.g., speed data and fuel consumption data) to perform a comprehensive risk assessment. For example, the risk assessment is performed based on speed fluctuations and increases in fuel consumption. The situation monitoring unit also integrates the driver's situation monitoring data with other vehicle data (e.g., vehicle maintenance data and breakdown data) to perform a comprehensive risk assessment. For example, the risk assessment is performed based on delays in maintenance and the frequency of breakdowns. In this way, by integrating the situation monitoring data with other vehicle data, a comprehensive risk assessment is possible.

[0034] The health management unit can collect vital data of the driver over a long period of time and analyze trends in the driver's health condition. The health management unit, for example, collects vital data of the driver over a long period of time and analyzes trends in the driver's health condition. For example, it analyzes fluctuations in heart rate and body temperature to evaluate changes in the driver's health condition. The health management unit also collects vital data of the driver over a long period of time and analyzes trends in the driver's health condition. For example, it analyzes fluctuations in blood pressure and blood sugar levels to evaluate changes in the driver's health condition. The health management unit also collects vital data of the driver over a long period of time and analyzes trends in the driver's health condition. For example, it analyzes fluctuations in sleep patterns and exercise volume to evaluate changes in the driver's health condition. This makes it easier to understand changes in the driver's health condition by analyzing long-term vital data.

[0035] The health management unit can monitor vehicle environmental data and assess health risks in addition to managing the driver's health condition. For example, in addition to managing the driver's health condition, the health management unit monitors vehicle environmental data (e.g., temperature and humidity) and assesses health risks. For example, if the temperature inside the vehicle is high, it assesses the risk of heatstroke. In addition to managing the driver's health condition, the health management unit also monitors vehicle environmental data (e.g., air quality and noise level) and assesses health risks. For example, if the air quality is poor, it assesses risks to the respiratory system. In addition to managing the driver's health condition, the health management unit also monitors vehicle environmental data (e.g., vibration and shaking) and assesses health risks. For example, if the vibration is strong, it assesses risks to muscles and joints. In this way, health risks can be assessed by monitoring vehicle environmental data.

[0036] The health management unit can compare the driver's health data with other drivers and set a benchmark to evaluate the health condition. For example, the health management unit compares the driver's health data with other drivers and sets ...

[0037] The warning providing unit can provide individually customized warnings and advice based on the driver's situation and health condition. The warning providing unit provides individually customized warnings and advice based on, for example, the driver's situation and health condition. For example, for a specific driver, the unit suggests the optimal timing for taking a break based on past data. The warning providing unit also provides individually customized warnings and advice based on the driver's situation and health condition. For example, for a specific driver, the unit suggests appropriate hydration based on fluctuations in heart rate and body temperature. The warning providing unit also provides individually customized warnings and advice based on the driver's situation and health condition. For example, for a specific driver, the unit suggests relaxation methods based on stress levels. In this way, by providing individually customized warnings and advice, the safety and health of the driver are improved.

[0038] The warning providing unit can analyze the driver's past data and provide warnings and advice at the optimal timing. The warning providing unit, for example, analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past driving data, it issues a warning encouraging the driver to take a break at a specific time period. The warning providing unit also analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past health data, it provides advice encouraging the driver to stay hydrated in a specific situation. The warning providing unit also analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past emotional data, it issues a warning encouraging the driver to relax in a specific emotional state. In this way, by providing warnings and advice at the optimal timing based on past data, driver safety is improved.

[0039] The warning providing unit can monitor the vehicle's condition in addition to the driver's condition and health state, and provide comprehensive warnings and advice. For example, the warning providing unit can monitor the vehicle's condition (e.g., fuel level and tire condition) in addition to the driver's condition and health state, and provide comprehensive warnings and advice. For example, it can guide the driver to the nearest gas station when fuel is low. The warning providing unit can also monitor the vehicle's condition (e.g., engine condition and brake condition) in addition to the driver's condition and health state, and provide comprehensive warnings and advice. For example, it can prompt the driver to perform maintenance when an engine abnormality is detected. The warning providing unit can also monitor the vehicle's condition (e.g., oil level and coolant condition) in addition to the driver's condition and health state, and provide comprehensive warnings and advice. For example, it can prompt the driver to refill the oil when the oil is low. In this way, comprehensive warnings and advice can be provided by monitoring the vehicle's condition.

[0040] The warning providing unit can share the driver's warnings and advice with other drivers to introduce best practices. For example, the warning providing unit may share the driver's warnings and advice with other drivers to introduce best practices. For example, the warning providing unit may share the optimal timing for taking a break in a specific situation. The warning providing unit may also share the driver's warnings and advice with other drivers to introduce best practices. For example, the warning providing unit may share the optimal method for hydration in a specific health condition. The warning providing unit may also share the driver's warnings and advice with other drivers to introduce best practices. For example, the warning providing unit may share the driver's warnings and advice with other drivers to introduce best practices. For example, the warning providing unit may share the optimal method for relaxation in a specific emotional state. In this way, sharing warnings and advice improves overall safety and efficiency.

[0041] The data recording unit compares the driver's vital data with past data to detect abnormal changes at an early stage. The data recording unit, for example, compares the driver's vital data with past data to detect abnormal changes at an early stage. For example, if the heart rate is significantly higher than the past average value, a warning is issued. The data recording unit also compares the driver's vital data with past data to detect abnormal changes at an early stage. For example, if the body temperature is significantly higher than the past average value, a warning is issued. The data recording unit also compares the driver's vital data with past data to detect abnormal changes at an early stage. For example, if the blood pressure is significantly higher than the past average value, a warning is issued. In this way, by comparing with past data, abnormal changes can be detected at an early stage.

[0042] The data recording unit can collect data on the driver's behavior while driving in addition to vital data and perform a comprehensive health assessment. For example, the data recording unit collects data on the driver's behavior while driving (e.g., driving style) in addition to vital data and perform a comprehensive health assessment. For example, health risks are evaluated based on the frequency of sudden braking and sudden acceleration. The data recording unit also collects data on the driver's behavior while driving (e.g., driving time and rest time) in addition to vital data and perform a comprehensive health assessment. For example, health risks are evaluated based on long driving times and insufficient rest. The data recording unit also collects data on the driver's behavior while driving (e.g., frequency of lane changes and speed fluctuations) in addition to vital data and perform a comprehensive health assessment. For example, health risks are evaluated based on frequent lane changes and sudden speed changes. In this way, a comprehensive health assessment is possible by collecting data on the driver's behavior while driving.

[0043] The data recording unit can compare the driver's vital data with other drivers and set a benchmark to evaluate the health condition. The data recording unit, for example, compares the driver's vital data with other drivers and sets a benchmark to evaluate the health condition. For example, the data recording unit compares the driver's vital data with the average heart rate of drivers of the same age to evaluate the health condition. The data recording unit also compares the driver's vital data with other drivers and sets a benchmark to evaluate the health condition. For example, the data recording unit compares the driver's vital data with the average body temperature of drivers during the same driving period to evaluate the health condition. The data recording unit also compares the driver's vital data with other drivers and sets a benchmark to evaluate the health condition. For example, the data recording unit compares the driver's vital data with the average blood pressure of drivers under the same driving conditions to evaluate the health condition. This makes the evaluation of the health condition more accurate by comparing with other drivers.

[0044] The communication unit can analyze the driver's situation and health condition in real time and provide immediate warnings and advice. The communication unit, for example, analyzes the driver's situation and health condition in real time and provides immediate warnings and advice. For example, if the heart rate suddenly rises, it issues a warning urging the driver to take a break. The communication unit also analyzes the driver's situation and health condition in real time and provides immediate warnings and advice. For example, if the body temperature suddenly rises, it provides advice urging the driver to drink more water. The communication unit also analyzes the driver's situation and health condition in real time and provides immediate warnings and advice. For example, if the blood pressure suddenly rises, it issues a warning urging the driver to relax. This enables rapid response by analyzing in real time and providing immediate warnings and advice.

[0045] The communication unit can analyze the driver's past data and provide warnings and advice at the optimal timing. The communication unit, for example, analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past driving data, it issues a warning encouraging the driver to take a break at a specific time period. The communication unit also analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past health data, it provides advice encouraging the driver to stay hydrated in a specific situation. The communication unit also analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past emotional data, it issues a warning encouraging the driver to relax when the driver is in a specific emotional state. In this way, by providing warnings and advice at the optimal timing based on past data, driver safety is improved.

[0046] The communication unit can monitor the vehicle's status in addition to the driver's status and health condition, and provide comprehensive warnings and advice. For example, the communication unit can monitor the vehicle's status (e.g., fuel level and tire condition) in addition to the driver's status and health condition, and provide comprehensive warnings and advice. For example, it can guide the driver to the nearest gas station when fuel is low. The communication unit can also monitor the vehicle's status (e.g., engine condition and brake condition) in addition to the driver's status and health condition, and provide comprehensive warnings and advice. For example, it can prompt the driver to perform maintenance when an engine abnormality is detected. The communication unit can also monitor the vehicle's status (e.g., oil level and coolant condition) in addition to the driver's status and health condition, and provide comprehensive warnings and advice. For example, it can prompt the driver to refill the oil when the oil is low. In this way, comprehensive warnings and advice can be provided by monitoring the vehicle's status.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The SMART DRIVE AI system can also learn the driver's driving style and provide individually optimized driving advice. For example, if a driver frequently brakes or accelerates suddenly, it can provide advice to encourage smoother driving. It can also suggest driving methods to improve fuel efficiency. It can also suggest optimal routes based on the driver's driving style. This can improve the driver's driving skills and increase fuel efficiency.

[0049] The SMART DRIVE AI system can also analyze a driver's driving history and perform risk assessments based on specific driving patterns. For example, it can identify time periods or locations where accidents have frequently occurred in the past and issue warnings based on that information. It can also perform risk assessments based on specific driving patterns (for example, a high frequency of sudden braking or sudden acceleration) and provide appropriate advice. This makes it possible to predict risks based on past driving history and prevent accidents before they occur.

[0050] The SMART DRIVE AI system can also analyze the driver's voice data while driving and detect abnormal voice patterns. For example, if the driver suddenly shouts or makes an abnormal sound, the system can use that voice pattern to assess risk. It can also analyze changes in the driver's tone of voice and speaking style to detect signs of stress or fatigue. This allows the system to evaluate the driver's condition based on the voice data and provide appropriate warnings and advice.

[0051] The SMART DRIVE AI system can also analyze the driver's gaze data while driving and detect a lapse in attention from gaze movements. For example, if the driver's gaze remains in a certain area for a long period of time, it can be evaluated as a sign of a lapse in attention. Irregular gaze movements can also be detected as signs of fatigue or stress. This allows the system to evaluate the driver's attention based on gaze data and provide appropriate warnings and advice.

[0052] The SMART DRIVE AI system can also analyze the driver's posture data while driving and detect signs of fatigue or inattention from changes in posture. For example, if the driver's posture is frequently disrupted, it can be evaluated as a sign of fatigue. Also, if the posture changes suddenly, it can detect signs of inattention. This allows the system to evaluate the driver's condition based on posture data and provide appropriate warnings and advice.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The situation monitoring unit uses an AI camera to monitor the driver's facial and body movements. For example, it detects how often the driver's eyes are closed and whether the driver is tilting their head, and the generation AI determines whether the driver is drowsy. It also analyzes the driver's facial expressions and movements to detect signs of inattention. Step 2: The health management unit collects the driver's vital data and evaluates their health condition. For example, it monitors their heart rate and body temperature, and the AI ​​generator determines their fatigue and changes in their health condition. It also detects fatigue caused by long driving periods and encourages them to take appropriate breaks. Step 3: The warning unit provides appropriate warnings and advice based on the data obtained by the situation monitoring unit and health management unit. For example, if signs of drowsiness are detected, a warning such as "Please take a break" is issued. Also, if the health condition is deteriorating, advice such as "Please drink fluids" and "Please stretch" is provided. Step 4: The data recording unit records data on the driver's condition and health status for later analysis. For example, analyzing past data can identify situations in which accidents are more likely to occur and take preventative measures. Step 5: The communication department communicates with the driver in real time using AI communication tools. For example, if the driver has a question or wants to ask for advice, the generation AI will provide an appropriate answer or advice.

[0055] (Example 2) The SMART DRIVE AI system according to an embodiment of the present invention is a system that manages the driver's drowsiness, inattention, and health condition to prevent accidents. As a result, the SMART DRIVE AI system can monitor the driver's condition in real time and provide warnings and advice as necessary.

[0056] The SMART DRIVE AI system according to the present embodiment includes a situation monitoring unit, a health management unit, a warning provision unit, a data recording unit, and a communication unit. The situation monitoring unit monitors the driver's facial and body movements. For example, an AI camera can be used to detect the frequency with which the driver closes their eyes and tilts their head, and the generation AI then determines signs of drowsiness. The situation monitoring unit can also analyze the driver's facial expressions and movements to detect signs of inattention. The health management unit collects the driver's vital signs and evaluates their health status. For example, the generation AI can monitor their heart rate and body temperature to determine whether they are fatigued or if their health status changes. The health management unit can also detect driver fatigue due to long driving periods and encourage them to take appropriate breaks. The warning provision unit provides appropriate warnings and advice based on the data obtained by the situation monitoring unit and the health management unit. For example, if signs of drowsiness are detected, the warning provision unit issues a warning such as "Please take a break." If their health status is deteriorating, the warning provision unit provides advice such as "Please hydrate" and "Please stretch." The data recording unit records data related to the driver's situation and health status for later analysis. For example, by analyzing past data, the system can identify situations in which accidents are likely to occur and take preventative measures. The communication unit communicates with the driver in real time using AI communication tools. For example, if the driver has a question or wants to consult, the generation AI provides appropriate answers and advice. This allows the SMART DRIVE AI system according to the embodiment to manage the driver's drowsiness, inattention, and health condition, preventing accidents before they occur. For example, if the driver has been driving for a long time, the generation AI can encourage the driver to take a break at the appropriate time, preventing accidents caused by fatigue. Furthermore, if the driver's health condition deteriorates, the generation AI can provide appropriate advice to maintain the driver's health.

[0057] The situation monitoring unit can use the driver's facial recognition data to customize and detect signs of drowsiness based on the characteristics of each individual driver. For example, the situation monitoring unit uses the driver's facial recognition data to analyze the eye opening and closing patterns and changes in facial expression of each individual driver to customize and detect signs of drowsiness. For example, if a particular driver frequently closes their eyes, the risk of drowsiness is assessed based on that pattern. The situation monitoring unit also learns the facial features of each individual driver based on the driver's facial recognition data to customize and detect signs of drowsiness. For example, if a particular driver has a unique facial expression when fatigued, the risk of drowsiness is assessed based on that facial expression. The situation monitoring unit also uses the driver's facial recognition data to analyze the eye movement and blinking patterns of each individual driver to customize and detect signs of drowsiness. For example, if a particular driver frequently closes their eyes, the risk of drowsiness is assessed based on that pattern. This improves detection accuracy by detecting signs of drowsiness based on the characteristics of each individual driver.

[0058] The situation monitoring unit can analyze the driver's past driving data and predict the risk of dozing off at the wheel under specific time periods and environmental conditions. For example, the situation monitoring unit analyzes the driver's past driving data and predicts the risk of dozing off at the wheel under specific time periods and environmental conditions. For example, if the risk of dozing off is higher at night or during long driving periods, the situation monitoring unit issues a warning during those periods. The situation monitoring unit also predicts the risk of dozing off at the wheel under specific environmental conditions (e.g., weather or road conditions) based on the driver's past driving data. For example, if the risk of dozing off is higher during rainy weather or traffic jams, the situation monitoring unit issues a warning under those conditions. The situation monitoring unit also analyzes the driver's past driving data and predicts the risk of dozing off at the wheel under specific driving patterns (e.g., driving at a constant speed for a long period of time). For example, if the risk of dozing off is higher during long driving periods on a highway, the situation monitoring unit issues a warning based on that pattern. In this way, by predicting the risk of dozing off based on past driving data, preventive measures can be taken.

[0059] The situation monitoring unit can use the emotion estimation function to detect signs of stress and fatigue from the driver's facial expression and assess the risk of dozing off. The situation monitoring unit, for example, uses the emotion estimation function to detect signs of stress and fatigue from the driver's facial expression and assess the risk of dozing off. For example, it detects facial expressions such as furrowed brows and dark circles under the eyes to assess the signs of stress and fatigue. The situation monitoring unit also uses the emotion estimation function to detect signs of stress and fatigue from the driver's facial expression and assess the risk of dozing off. For example, it detects facial expressions such as drooping corners of the mouth and narrowed eyes to assess the signs of stress and fatigue. The situation monitoring unit also uses the emotion estimation function to detect signs of stress and fatigue from the driver's facial expression and assess the risk of dozing off. For example, it detects tension in facial muscles and sluggish eye movements to assess the signs of stress and fatigue. In this way, the risk of dozing off can be assessed by detecting signs of stress and fatigue from the driver's facial expression.

[0060] The situation monitoring unit can detect abnormal driving patterns by monitoring vehicle behavior in addition to monitoring the driver's situation. For example, in addition to monitoring the driver's situation, the situation monitoring unit monitors vehicle behavior (e.g., sudden braking or abrupt steering) to detect abnormal driving patterns. For example, if the frequency of sudden braking is high, the risk of drowsiness or inattention is evaluated. Furthermore, in addition to monitoring the driver's situation, the situation monitoring unit monitors vehicle behavior (e.g., sudden acceleration or sudden deceleration) to detect abnormal driving patterns. For example, if the frequency of sudden acceleration is high, the risk of drowsiness or inattention is evaluated. Furthermore, in addition to monitoring the driver's situation, the situation monitoring unit monitors vehicle behavior (e.g., lane departure or snaking driving) to detect abnormal driving patterns. For example, if lane departure is frequent, the risk of drowsiness or inattention is evaluated. In this way, abnormal driving patterns can be detected by monitoring vehicle behavior.

[0061] The situation monitoring unit can integrate the driver's situation monitoring data with other vehicle data to perform risk assessment. For example, the situation monitoring unit integrates the driver's situation monitoring data with other vehicle data (e.g., GPS data and traffic information) to perform a comprehensive risk assessment. For example, the risk assessment is performed based on congestion information and road construction information. The situation monitoring unit also integrates the driver's situation monitoring data with other vehicle data (e.g., speed data and fuel consumption data) to perform a comprehensive risk assessment. For example, the risk assessment is performed based on speed fluctuations and increases in fuel consumption. The situation monitoring unit also integrates the driver's situation monitoring data with other vehicle data (e.g., vehicle maintenance data and breakdown data) to perform a comprehensive risk assessment. For example, the risk assessment is performed based on delays in maintenance and the frequency of breakdowns. In this way, by integrating the situation monitoring data with other vehicle data, a comprehensive risk assessment is possible.

[0062] The situation monitoring unit can use the emotion estimation function to monitor the driver's emotional state in real time and provide a warning in response to changes in emotion. For example, the situation monitoring unit uses the emotion estimation function to monitor the driver's emotional state in real time and provide a warning in response to changes in emotion. For example, if stress increases, a warning urging the driver to relax is issued. The situation monitoring unit also uses the emotion estimation function to monitor the driver's emotional state in real time and provide a warning in response to changes in emotion. For example, if fatigue accumulates, a warning urging the driver to take a break is issued. The situation monitoring unit also uses the emotion estimation function to monitor the driver's emotional state in real time and provide a warning in response to changes in emotion. For example, if irritation increases, a warning urging the driver to stay calm is issued. In this way, by monitoring the driver's emotional state in real time and providing a warning in response to changes in emotion, the risk of an accident can be reduced.

[0063] The health management unit can collect vital data of the driver over a long period of time and analyze trends in the driver's health condition. The health management unit, for example, collects vital data of the driver over a long period of time and analyzes trends in the driver's health condition. For example, it analyzes fluctuations in heart rate and body temperature to evaluate changes in the driver's health condition. The health management unit also collects vital data of the driver over a long period of time and analyzes trends in the driver's health condition. For example, it analyzes fluctuations in blood pressure and blood sugar levels to evaluate changes in the driver's health condition. The health management unit also collects vital data of the driver over a long period of time and analyzes trends in the driver's health condition. For example, it analyzes fluctuations in sleep patterns and exercise volume to evaluate changes in the driver's health condition. This makes it easier to understand changes in the driver's health condition by analyzing long-term vital data.

[0064] The health management unit can use the emotion estimation function to analyze the relationship between the driver's emotional state and health state and evaluate health risks. The health management unit, for example, uses the emotion estimation function to analyze the relationship between the driver's emotional state and health state and evaluate health risks. For example, if heart rate increases as stress increases, the health risk is evaluated based on this relationship. The health management unit also uses the emotion estimation function to analyze the relationship between the driver's emotional state and health state and evaluate health risks. For example, if body temperature increases as fatigue accumulates, the health risk is evaluated based on this relationship. The health management unit also uses the emotion estimation function to analyze the relationship between the driver's emotional state and health state and evaluate health risks. For example, if blood pressure increases as irritation increases, the health risk is evaluated based on this relationship. In this way, health risks can be evaluated by analyzing the relationship between the emotional state and health state.

[0065] The health management unit can monitor vehicle environmental data and assess health risks in addition to managing the driver's health condition. For example, in addition to managing the driver's health condition, the health management unit monitors vehicle environmental data (e.g., temperature and humidity) and assesses health risks. For example, if the temperature inside the vehicle is high, it assesses the risk of heatstroke. In addition to managing the driver's health condition, the health management unit also monitors vehicle environmental data (e.g., air quality and noise level) and assesses health risks. For example, if the air quality is poor, it assesses risks to the respiratory system. In addition to managing the driver's health condition, the health management unit also monitors vehicle environmental data (e.g., vibration and shaking) and assesses health risks. For example, if the vibration is strong, it assesses risks to muscles and joints. In this way, health risks can be assessed by monitoring vehicle environmental data.

[0066] The health management unit can compare the driver's health data with other drivers and set a benchmark to evaluate the health condition. For example, the health management unit compares the driver's health data with other drivers and sets ...

[0067] The health management unit can use the emotion estimation function to monitor the emotional state of the driver in real time and provide health advice according to changes in emotions. For example, the health management unit can use the emotion estimation function to monitor the emotional state of the driver in real time and provide health advice according to changes in emotions. For example, if stress increases, advice to encourage relaxation is provided. The health management unit can also use the emotion estimation function to monitor the emotional state of the driver in real time and provide health advice according to changes in emotions. For example, advice to encourage taking a break is provided if fatigue accumulates. The health management unit can also use the emotion estimation function to monitor the emotional state of the driver in real time and provide health advice according to changes in emotions. For example, advice to encourage calmness is provided if irritation increases. In this way, the driver's health can be maintained by providing health advice according to the emotional state.

[0068] The warning providing unit can provide individually customized warnings and advice based on the driver's situation and health condition. The warning providing unit provides individually customized warnings and advice based on, for example, the driver's situation and health condition. For example, for a specific driver, the unit suggests the optimal timing for taking a break based on past data. The warning providing unit also provides individually customized warnings and advice based on the driver's situation and health condition. For example, for a specific driver, the unit suggests appropriate hydration based on fluctuations in heart rate and body temperature. The warning providing unit also provides individually customized warnings and advice based on the driver's situation and health condition. For example, for a specific driver, the unit suggests relaxation methods based on stress levels. In this way, by providing individually customized warnings and advice, the safety and health of the driver are improved.

[0069] The warning providing unit can analyze the driver's past data and provide warnings and advice at the optimal timing. The warning providing unit, for example, analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past driving data, it issues a warning encouraging the driver to take a break at a specific time period. The warning providing unit also analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past health data, it provides advice encouraging the driver to stay hydrated in a specific situation. The warning providing unit also analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past emotional data, it issues a warning encouraging the driver to relax in a specific emotional state. In this way, by providing warnings and advice at the optimal timing based on past data, driver safety is improved.

[0070] The warning providing unit can use the emotion estimation function to provide warnings and advice according to the emotional state of the driver. The warning providing unit, for example, uses the emotion estimation function to provide warnings and advice according to the emotional state of the driver. For example, when stress increases, the warning providing unit issues a warning urging the driver to relax. The warning providing unit also uses the emotion estimation function to provide warnings and advice according to the emotional state of the driver. For example, when fatigue accumulates, the warning providing unit issues a warning urging the driver to take a break. The warning providing unit also uses the emotion estimation function to provide warnings and advice according to the emotional state of the driver. For example, when irritation increases, the warning providing unit issues a warning urging the driver to stay calm. In this way, by providing warnings and advice according to the emotional state, the safety and health of the driver are improved.

[0071] The warning providing unit can monitor the vehicle's condition in addition to the driver's condition and health state, and provide comprehensive warnings and advice. For example, the warning providing unit can monitor the vehicle's condition (e.g., fuel level and tire condition) in addition to the driver's condition and health state, and provide comprehensive warnings and advice. For example, it can guide the driver to the nearest gas station when fuel is low. The warning providing unit can also monitor the vehicle's condition (e.g., engine condition and brake condition) in addition to the driver's condition and health state, and provide comprehensive warnings and advice. For example, it can prompt the driver to perform maintenance when an engine abnormality is detected. The warning providing unit can also monitor the vehicle's condition (e.g., oil level and coolant condition) in addition to the driver's condition and health state, and provide comprehensive warnings and advice. For example, it can prompt the driver to refill the oil when the oil is low. In this way, comprehensive warnings and advice can be provided by monitoring the vehicle's condition.

[0072] The warning providing unit can share the driver's warnings and advice with other drivers to introduce best practices. For example, the warning providing unit may share the driver's warnings and advice with other drivers to introduce best practices. For example, the warning providing unit may share the optimal timing for taking a break in a specific situation. The warning providing unit may also share the driver's warnings and advice with other drivers to introduce best practices. For example, the warning providing unit may share the optimal method for hydration in a specific health condition. The warning providing unit may also share the driver's warnings and advice with other drivers to introduce best practices. For example, the warning providing unit may share the driver's warnings and advice with other drivers to introduce best practices. For example, the warning providing unit may share the optimal method for relaxation in a specific emotional state. In this way, sharing warnings and advice improves overall safety and efficiency.

[0073] The warning providing unit can use the emotion estimation function to monitor the driver's emotional state in real time and provide warnings and advice according to changes in emotion. The warning providing unit, for example, uses the emotion estimation function to monitor the driver's emotional state in real time and provide warnings and advice according to changes in emotion. For example, if stress increases, it issues a warning urging the driver to relax. The warning providing unit also uses the emotion estimation function to monitor the driver's emotional state in real time and provide warnings and advice according to changes in emotion. For example, if fatigue accumulates, it issues a warning urging the driver to take a break. The warning providing unit also uses the emotion estimation function to monitor the driver's emotional state in real time and provide warnings and advice according to changes in emotion. For example, if irritation increases, it issues a warning urging the driver to stay calm. In this way, the driver's safety and health are improved by monitoring the emotional state in real time and providing warnings and advice according to changes in emotion.

[0074] The data recording unit compares the driver's vital data with past data to detect abnormal changes at an early stage. The data recording unit, for example, compares the driver's vital data with past data to detect abnormal changes at an early stage. For example, if the heart rate is significantly higher than the past average value, a warning is issued. The data recording unit also compares the driver's vital data with past data to detect abnormal changes at an early stage. For example, if the body temperature is significantly higher than the past average value, a warning is issued. The data recording unit also compares the driver's vital data with past data to detect abnormal changes at an early stage. For example, if the blood pressure is significantly higher than the past average value, a warning is issued. In this way, by comparing with past data, abnormal changes can be detected at an early stage.

[0075] The data recording unit can use the emotion estimation function to analyze the relationship between the driver's emotional state and vital data and evaluate health risks. The data recording unit, for example, uses the emotion estimation function to analyze the relationship between the driver's emotional state and vital data and evaluate health risks. For example, if heart rate increases as stress increases, the health risks are evaluated based on this relationship. The data recording unit also uses the emotion estimation function to analyze the relationship between the driver's emotional state and vital data and evaluate health risks. For example, if body temperature increases as fatigue accumulates, the health risks are evaluated based on this relationship. The data recording unit also uses the emotion estimation function to analyze the relationship between the driver's emotional state and vital data and evaluate health risks. For example, if blood pressure increases as irritation increases, the health risks are evaluated based on this relationship. In this way, health risks can be evaluated by analyzing the relationship between the emotional state and vital data.

[0076] The data recording unit can collect data on the driver's behavior while driving in addition to vital data and perform a comprehensive health assessment. For example, the data recording unit collects data on the driver's behavior while driving (e.g., driving style) in addition to vital data and perform a comprehensive health assessment. For example, health risks are evaluated based on the frequency of sudden braking and sudden acceleration. The data recording unit also collects data on the driver's behavior while driving (e.g., driving time and rest time) in addition to vital data and perform a comprehensive health assessment. For example, health risks are evaluated based on long driving times and insufficient rest. The data recording unit also collects data on the driver's behavior while driving (e.g., frequency of lane changes and speed fluctuations) in addition to vital data and perform a comprehensive health assessment. For example, health risks are evaluated based on frequent lane changes and sudden speed changes. In this way, a comprehensive health assessment is possible by collecting data on the driver's behavior while driving.

[0077] The data recording unit can compare the driver's vital data with other drivers and set a benchmark to evaluate the health condition. The data recording unit, for example, compares the driver's vital data with other drivers and sets a benchmark to evaluate the health condition. For example, the data recording unit compares the driver's vital data with the average heart rate of drivers of the same age to evaluate the health condition. The data recording unit also compares the driver's vital data with other drivers and sets a benchmark to evaluate the health condition. For example, the data recording unit compares the driver's vital data with the average body temperature of drivers during the same driving period to evaluate the health condition. The data recording unit also compares the driver's vital data with other drivers and sets a benchmark to evaluate the health condition. For example, the data recording unit compares the driver's vital data with the average blood pressure of drivers under the same driving conditions to evaluate the health condition. This makes the evaluation of the health condition more accurate by comparing with other drivers.

[0078] The data recording unit can use the emotion estimation function to monitor the driver's emotional state in real time and provide health advice in response to changes in emotion. The data recording unit, for example, uses the emotion estimation function to monitor the driver's emotional state in real time and provide health advice in response to changes in emotion. For example, when stress increases, advice is provided to encourage relaxation. The data recording unit can also use the emotion estimation function to monitor the driver's emotional state in real time and provide health advice in response to changes in emotion. For example, when fatigue accumulates, advice is provided to encourage taking a break. The data recording unit can also use the emotion estimation function to monitor the driver's emotional state in real time and provide health advice in response to changes in emotion. For example, when irritation increases, advice is provided to encourage calm down. In this way, the driver's health can be maintained by monitoring the emotional state in real time and providing health advice in response to changes in emotion.

[0079] The communication unit can analyze the driver's situation and health condition in real time and provide immediate warnings and advice. The communication unit, for example, analyzes the driver's situation and health condition in real time and provides immediate warnings and advice. For example, if the heart rate suddenly rises, it issues a warning urging the driver to take a break. The communication unit also analyzes the driver's situation and health condition in real time and provides immediate warnings and advice. For example, if the body temperature suddenly rises, it provides advice urging the driver to drink more water. The communication unit also analyzes the driver's situation and health condition in real time and provides immediate warnings and advice. For example, if the blood pressure suddenly rises, it issues a warning urging the driver to relax. This enables rapid response by analyzing in real time and providing immediate warnings and advice.

[0080] The communication unit can analyze the driver's past data and provide warnings and advice at the optimal timing. The communication unit, for example, analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past driving data, it issues a warning encouraging the driver to take a break at a specific time period. The communication unit also analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past health data, it provides advice encouraging the driver to stay hydrated in a specific situation. The communication unit also analyzes the driver's past data and provides warnings and advice at the optimal timing. For example, based on past emotional data, it issues a warning encouraging the driver to relax when the driver is in a specific emotional state. In this way, by providing warnings and advice at the optimal timing based on past data, driver safety is improved.

[0081] The communication unit can use the emotion estimation function to provide warnings and advice according to the driver's emotional state. The communication unit, for example, uses the emotion estimation function to provide warnings and advice according to the driver's emotional state. For example, if stress increases, the communication unit issues a warning urging the driver to relax. The communication unit also uses the emotion estimation function to provide warnings and advice according to the driver's emotional state. For example, if fatigue accumulates, the communication unit issues a warning urging the driver to take a break. The communication unit also uses the emotion estimation function to provide warnings and advice according to the driver's emotional state. For example, if frustration increases, the communication unit issues a warning urging the driver to stay calm. In this way, by providing warnings and advice according to the emotional state, the safety and health of the driver are improved.

[0082] The communication unit can monitor the vehicle's status in addition to the driver's status and health condition, and provide comprehensive warnings and advice. For example, the communication unit can monitor the vehicle's status (e.g., fuel level and tire condition) in addition to the driver's status and health condition, and provide comprehensive warnings and advice. For example, it can guide the driver to the nearest gas station when fuel is low. The communication unit can also monitor the vehicle's status (e.g., engine condition and brake condition) in addition to the driver's status and health condition, and provide comprehensive warnings and advice. For example, it can prompt the driver to perform maintenance when an engine abnormality is detected. The communication unit can also monitor the vehicle's status (e.g., oil level and coolant condition) in addition to the driver's status and health condition, and provide comprehensive warnings and advice. For example, it can prompt the driver to refill the oil when the oil is low. In this way, comprehensive warnings and advice can be provided by monitoring the vehicle's status.

[0083] The communication unit can use the emotion estimation function to monitor the driver's emotional state in real time and provide warnings and advice according to changes in emotion. For example, the communication unit uses the emotion estimation function to monitor the driver's emotional state in real time and provide warnings and advice according to changes in emotion. For example, if stress increases, the communication unit issues a warning urging the driver to relax. The communication unit also uses the emotion estimation function to monitor the driver's emotional state in real time and provide warnings and advice according to changes in emotion. For example, if fatigue accumulates, the communication unit issues a warning urging the driver to take a break. The communication unit also uses the emotion estimation function to monitor the driver's emotional state in real time and provide warnings and advice according to changes in emotion. For example, if irritation increases, the communication unit issues a warning urging the driver to stay calm. In this way, the safety and health of the driver can be improved by monitoring the emotional state in real time and providing warnings and advice according to changes in emotion.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The SMART DRIVE AI system can also learn the driver's driving style and provide individually optimized driving advice. For example, if a driver frequently brakes or accelerates suddenly, it can provide advice to encourage smoother driving. It can also suggest driving methods to improve fuel efficiency. It can also suggest optimal routes based on the driver's driving style. This can improve the driver's driving skills and increase fuel efficiency.

[0086] The SMART DRIVE AI system can also analyze a driver's driving history and perform risk assessments based on specific driving patterns. For example, it can identify time periods or locations where accidents have frequently occurred in the past and issue warnings based on that information. It can also perform risk assessments based on specific driving patterns (for example, a high frequency of sudden braking or sudden acceleration) and provide appropriate advice. This makes it possible to predict risks based on past driving history and prevent accidents before they occur.

[0087] The SMART DRIVE AI system can also analyze the driver's voice data while driving and detect abnormal voice patterns. For example, if the driver suddenly shouts or makes an abnormal sound, the system can use that voice pattern to assess risk. It can also analyze changes in the driver's tone of voice and speaking style to detect signs of stress or fatigue. This allows the system to evaluate the driver's condition based on the voice data and provide appropriate warnings and advice.

[0088] The SMART DRIVE AI system can also analyze the driver's gaze data while driving and detect a lapse in attention from gaze movements. For example, if the driver's gaze remains in a certain area for a long period of time, it can be evaluated as a sign of a lapse in attention. Irregular gaze movements can also be detected as signs of fatigue or stress. This allows the system to evaluate the driver's attention based on gaze data and provide appropriate warnings and advice.

[0089] The SMART DRIVE AI system can also analyze the driver's posture data while driving and detect signs of fatigue or inattention from changes in posture. For example, if the driver's posture is frequently disrupted, it can be evaluated as a sign of fatigue. Also, if the posture changes suddenly, it can detect signs of inattention. This allows the system to evaluate the driver's condition based on posture data and provide appropriate warnings and advice.

[0090] The SMART DRIVE AI system can also estimate the driver's emotional state and adjust the music and ambient sounds based on the estimated emotion. For example, if stress levels rise, it can play relaxing music, and if fatigue accumulates, it can play music with an awakening effect. It can also adjust the interior lighting according to the driver's emotional state. This allows the environment to be adjusted according to the driver's emotional state, providing a comfortable driving environment.

[0091] The SMART DRIVE AI system can also estimate the driver's emotional state and adjust driving assistance functions based on the estimated emotions. For example, it can strengthen driving assistance functions when stress levels increase and recommend autonomous driving mode when fatigue levels increase. It can also adjust the frequency of alerts for driving assistance functions based on the driver's emotional state. This allows for driving assistance tailored to the driver's emotional state, improving safety.

[0092] The SMART DRIVE AI system can also estimate the driver's emotional state and adjust its communication methods based on the estimated emotions. For example, if stress levels rise, the system will provide gentle advice, and if fatigue builds up, it will offer encouraging words. The system can also adjust the content and frequency of advice depending on the driver's emotional state. This allows the system to communicate in line with the driver's emotional state and reduce stress while driving.

[0093] The SMART DRIVE AI system can also estimate the driver's emotional state and adjust the driving route based on the estimated emotion. For example, if stress levels rise, it will suggest a route that avoids crowded areas, and if fatigue accumulates, it will suggest a route with many rest facilities. It can also adjust the difficulty of the driving route according to the driver's emotional state. This allows it to suggest a driving route that suits the driver's emotional state and support comfortable driving.

[0094] The SMART DRIVE AI system can also estimate the driver's emotional state and adjust tasks while driving based on the estimated emotions. For example, it can change task priorities when stress levels rise, or automate some tasks when fatigue builds up. It can also adjust the difficulty of tasks according to the driver's emotional state. This allows for task management based on the driver's emotional state, reducing the burden on the driver while driving.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The situation monitoring unit uses an AI camera to monitor the driver's facial and body movements. For example, it detects how often the driver's eyes are closed and whether the driver is tilting their head, and the generation AI determines whether the driver is drowsy. It also analyzes the driver's facial expressions and movements to detect signs of inattention. Step 2: The health management unit collects the driver's vital data and evaluates their health condition. For example, it monitors their heart rate and body temperature, and the AI ​​generator determines their fatigue and changes in their health condition. It also detects fatigue caused by long driving periods and encourages them to take appropriate breaks. Step 3: The warning unit provides appropriate warnings and advice based on the data obtained by the situation monitoring unit and health management unit. For example, if signs of drowsiness are detected, a warning such as "Please take a break" is issued. Also, if the health condition is deteriorating, advice such as "Please drink fluids" and "Please stretch" is provided. Step 4: The data recording unit records data on the driver's condition and health status for later analysis. For example, analyzing past data can identify situations in which accidents are more likely to occur and take preventative measures. Step 5: The communication department communicates with the driver in real time using AI communication tools. For example, if the driver has a question or wants to ask for advice, the generation AI will provide an appropriate answer or advice.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 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 system that uses a generative AI system to manage the driver's drowsiness, inattention, and health condition to prevent accidents. a situation monitoring unit that monitors the face and body movements of the driver using an AI camera; a health management unit that collects vital data of the driver and evaluates the driver's health condition; a warning providing unit that provides appropriate warnings and advice based on the data obtained by the condition monitoring unit and the health management unit; a data recording unit for recording data relating to the driver's situation and health condition for later analysis; a communication unit that communicates with the driver in real time using an AI communication tool; A system characterized by:

2. The situation monitoring unit Using the driver's facial recognition data, the system customizes detection of signs of drowsiness based on individual driver characteristics.

2. The system of claim 1.

3. The situation monitoring unit Analyze the driver's past driving data and predict the risk of drowsiness at the wheel under specific time periods and environmental conditions.

2. The system of claim 1.

4. The situation monitoring unit Detecting signs of stress or fatigue from the driver's facial expressions and assessing the risk of drowsiness 2. The system of claim 1.

5. The situation monitoring unit In addition to monitoring the driver's condition, the system also monitors vehicle behavior to detect abnormal driving patterns.

2. The system of claim 1.

6. The situation monitoring unit The driver's condition monitoring data is integrated with other vehicle data to perform risk assessments.

2. The system of claim 1.

7. The situation monitoring unit Monitor the driver's emotional state in real time and provide warnings according to changes in the driver's emotions.

2. The system of claim 1.

8. The health management department Collecting vital data of the driver over a long period of time and analyzing trends in the driver's health condition 2. The system of claim 1.

Images